ALBERT

Description: We have used surface elevation measurements acquired by the Ice, Cloud,and land Elevation Satellite Geoscience Laser Altimeter System (GLAS) and EnviSAT Radar Altimeter 2 (RA-2) satellite altimeters to assess the elevation change of the 13 700-km 2 Devon Ice Cap (DIC) in Arctic Canada between 2002 and 2008. We present algorithms for the retrieval of elevation change rates over ice caps using data acquired from these satellites. A comparison of GLAS elevation data to those acquired by the RA-2 shows reasonable agreement between the two instruments; the root mean square elevation change difference was 56 cm, and the correlation coefficient between the two data sets was 0.68. Using only RA-2 elevation measurements, which are spatially and temporally more continuous, we determined the elevation change rate of the areas of the DIC where the surface geometry allows the RA-2 retracker to maintain lock. This includes most of the DIC, excluding large parts of the eastern half of the ice cap. The elevation change rate was found to be insignificant given a statistical estimate of the measurement error (-0.09 ± 0.29 m/a). We also present an assessment of the regional variations of the DIC elevation change, including a significant -0.71 ± 0.49 m/a elevation change rate of the 1980-km 2 western arm. Furthermore, we present evidence of a localized 2-m drop in the surface elevation of the South Croker Bay Glacier during summer 2007. This drop is apparent within both satellite data sets, and we interpret this signal to reflect a sudden speedup of the glacier.

Description: Investigations in data quality and uncertainty modeling are becoming key topics in geoinformation science. This paper models a collection of outcomes from a standard segmentation algorithm as a random set. It quantifies extensional uncertainties of extracted objects using statistical characteristics of random sets. The approach is applied to a synthetic data set and vegetation patches in the Poyang Lake area in China. These patches are of interest as they have both sharp and vague boundaries. Results show that random sets provide useful spatial information on uncertainties using their basic parameters like the mean, level sets, and variance. The number of iterations to achieve a stable covering function and the sum of the variances are good indicators of boundary sharpness. The coefficient of variation has a positive relation with the degree of uncertainty. An asymmetry ratio reflects the uneven gradual changes along different directions where broad boundaries exist. This paper shows that several characteristics of extensional uncertainty of segmented objects can be quantified numerically and spatially by random sets.

Description: Improving watershed nutrient budgets, ecosystem models, and our understanding of the impact of land-use management on ecosystem functioning depends on the development of remote sensing methods that can predict aspects of the nitrogen (N) cycle. This is particularly true for temperate managed grasslands, which constitute a large portion of agricultural land and, at times, export a significant amount of N to aquatic systems and the atmosphere. Although foliar N is often remotely sensed, we explore the use of spectroscopy to predict the foliar isotopic ratio of $^{15}hbox{N}$ to $^{14} hbox{N}$ , i.e., $delta{^{15}hbox{N}}$ . Foliar $delta{^{15}hbox{N}}$ has been shown in global surveys and site-specific studies to reflect N availability and the amount of N lost to the atmosphere. We built a data set of the canopy reflectance of plots in managed pastures and hay lands, which we then harvested for laboratory analysis. For the spectra of dried and ground samples, we calculated the normalized band depth (NBD) of three absorption features most likely to correlate with $delta{^{15}hbox{N}}$ . In these data, foliar N and $delta{^{15}hbox{N}}$ were not correlated, and we found weak, but significant, linear models with $delta{^{15} hbox{N}}$ for the NBD of the 2100-nm feature known to relate to foliar N. The canopy spectra, which inherently reflect the vegetation structure, correlated better with $delta{^{15} hbox{N}}$ than the spectra of dried and- - ground samples. These results suggest that near-term advances in estimating $delta{^{15}hbox{N}}$ and aspects of pasture management style are likely to be related to, or to include, the quantification of the vegetation structure.

Description: High-resolution (submeter) orbital imagers have opened up new possibilities for Mars topographic mapping with unprecedented precision. While the typical sensor model for Martian orbiters has been the linear-array charge-coupled device (CCD), the High-Resolution Imaging Science Experiment (HiRISE) instrument is based on a more complicated structure involving a combination of 14 separate linear-array CCDs. To take full advantage of this high-resolution capability without compromising imaging geometry, we have developed a rigorous photogrammetric model for HiRISE stereo image processing in which third-order polynomials are used to model the change in exterior-orientation parameters over time. A coarse-to-fine hierarchical matching approach was developed, and its performance was evaluated based on manually matched image points and manually measured features for a test area at the Mars Exploration Rover Spirit landing site. Using automatically selected tie points, we performed bundle adjustment (BA) to improve the accuracy of image pointing data and remove or reduce inconsistencies between the stereo pair and inconsistencies between overlapping CCDs in the same image mosaic. A method for the incorporation of jitter terms into the BA was developed and proved to be effective. We created a 1-m-resolution digital elevation model and an orthophoto using this methodology and compared them with topographic products from the U.S. Geological Survey.

Description: Within the hyperspectral community, change detection is a continued area of interest. Interesting changes in imagery typically correspond to changes in material reflectance associated with pixels in the scene. Using a physical model describing the sensor-reaching radiance, change detection can be formulated as a statistical hypothesis test. Complicating the problem of change detection is the presence of shadow, illumination, and atmospheric differences, as well as misregistration and parallax error, which often produce the appearance of change. The proposed physical model incorporates terms to account for both direct and diffuse shadow fractions to help mitigate false alarms associated with shadow differences between scenes. The resulting generalized likelihood ratio test (GLRT) provides an indicator of change at each pixel. The maximum likelihood estimates of the physical model parameters used for the GLRT are obtained from the entire joint data set to take advantage of coupled information existing between pixel measurements. Simulation results using synthetic and real imagery demonstrate the efficacy of the proposed approach.

Description: Forest inventories are important tools for the management of forests. In this context, the estimation of the tree stem volume is a key issue. In this paper, we present a system for the estimation of forest stem diameter and volume at individual tree level from multireturn light detection and ranging (LIDAR) data. The proposed system is made up of a preprocessing module, a LIDAR segmentation algorithm (aimed at retrieving tree crowns), a variable extraction and selection procedure, and an estimation module based on support vector regression (SVR) (which is compared with a multiple linear regression technique). The variables derived from LIDAR data are computed from both the intensity and elevation channels of all available returns. Three different methods of variable selection are analyzed, and the sets of variables selected are used in the estimation phase. The stem volume is estimated with two methods: 1) direct estimation from the LIDAR variables and 2) combination of diameters and heights estimated from LIDAR variables with the species information derived from a classification map according to standard height/diameter relationships. Experimental results show that the system proposed is effective and provides high accuracies in both the stem volume and diameter estimations. Moreover, this paper provides useful indications on the effectiveness of SVR with LIDAR in forestry problems.

Description: This paper provides a novel pointwise-adaptive speckle filter based on local homogeneous-region segmentation with pixel-relativity measurement. A ratio distance is proposed to measure the distance between two speckled-image patches. The theoretical proofs indicate that the ratio distance is valid for multiplicative speckle, while the traditional Euclidean distance failed in this case. The probability density function of the ratio distance is deduced to map the distance into a relativity value. This new relativity-measurement method is free of parameter setting and more functional compared with the Gaussian kernel-projection-based ones. The new measurement method is successfully applied to segment a local shape-adaptive homogeneous region for each pixel, and a simplified strategy for the segmentation implementation is given in this paper. After segmentation, the maximum likelihood rule is introduced to estimate the true signal within every homogeneous region. A novel evaluation metric of edge-preservation degree based on ratio of average is also provided for more precise quantitative assessment. The visual and numerical experimental results show that the proposed filter outperforms the existing state-of-the-art despeckling filters.

Description: The advent of routine collection of high-quality digital photography provides for traditional uses, as well as “remote sensing” uses such as the monitoring of environmental indicators. A well-devised monitoring system, based on consistent data and methods, provides the opportunity to track and communicate changes in features of interest in a way that has not previously been possible. Data that are geometrically and radiometrically consistent are fundamental to establishing systems for monitoring. In this paper, we focus on models for the radiometric calibration of mosaics consisting of thousands of images. We apply the models to the data acquired by the Australian Commonwealth Scientific and Industrial Research Organisation and its partners as part of regular systematic acquisitions over the city of Perth for a project known as Urban Monitor. One goal of the project, and hence the model development, is to produce annually updated mosaics calibrated to reflectance at 0.2-m ground sample distance for an area of approximately 9600 $hbox{km}^{2}$ . This equates to terabytes of data and, for frame-based instruments, tens of thousands of images. For the experiments considered in this paper, this requires mosaicking estimates derived from 3000 digital photographic frames, and the methods will shortly be expanded to 30 000 $+$ frames. A key part of the processing is the removal of spectral variation due to the viewing geometry, typically attributed to the bidirectional reflectance distribution function (BRDF) of the land surface. A variety of techniques based on semiempirical BRDF kernels have been proposed in the literature for correcting the BRDF effect in single frames, but mosaics with many frames provide unique challenges. This paper presents and illuminates a complete empirical radiometric calibration method for dig- - ital aerial frame mosaics, based on a combined model that uses kernel-based techniques for BRDF correction and incorporates additive and multiplicative terms for correcting other effects, such as variations due to the sensor and atmosphere. Using ground truth, which consists of laboratory-measured white, gray, and black targets that were placed in the field at the time of acquisition, we calculate the fundamental limitations of each model, leading to an optimal result for each model type. We demonstrate estimates of ground reflectance that are accurate to approximately 10%, 5%, and 3% absolute reflectances for ground targets having reflectances of 90%, 40%, and 4%, respectively.

Description: Spaceborne synthetic aperture radar (SAR) systems operating at lower frequencies, such as P-band, are significantly affected by Faraday rotation (FR) effects. A novel algorithm for calibrating the circular-transmit-and-linear-receive (CTLR) mode spaceborne compact polarimetric SAR using mixed calibrators is proposed, which is able to correct precisely both FR and radar system errors (i.e., channel imbalance and crosstalk). Six sets of mixed calibrators, consisting of both passive calibrators and polarimetric active radar calibrators (PARCs), are investigated. Theoretical analysis and simulations demonstrate that the optimal calibration scheme combines four polarimetric selective mixed calibrators, including two gridded trihedrals and two PARCs, together with total-electron-content measurements by the Global Navigation Satellite System system.

Description: Spectral mixture analysis has been an important research topic in remote sensing applications, particularly for hyperspectral remote sensing data processing. On the basis of linear spectral mixture models, this paper applied directed and weighted graphs to describe the relationship between pixels. In particular, we transformed the endmember extraction problem in the decomposition of mixed pixels into an issue of optimization and built feasible solution space to evaluate the practical significance of the objective function, thereby establishing two ant colony optimization algorithms for endmember extraction. In addition to the detailed process of calculation, we also addressed the effects of different operating parameters on algorithm performance. Finally we designed two sets of simulation data experiments and one set of actual data experiments, and the results of those experiments prove that endmember extraction based on ant colony algorithms can avoid some defects of N-FINDR, VCA and other algorithms, improve the representation of endmembers for all image pixels, decrease the average value of root-mean-square error, and therefore achieve better endmember extraction results than the N-FINDR and VCA algorithms.

Description: This paper analyzes the influence of typical target micromotions on synthetic aperture radar (SAR) images, azimuth resolution limit, SAR/ground moving target indication (GMTI), and MTI. According to the micromotion periods contained in the coherent processing interval, a new range model expansion and a generalized paired echo principle are proposed and applied to underlie the analysis. Several new kinds of image characteristics including gray strips, ghost points, and fences are reported, which are sheerly distinct from those of slow movers. Micromotion will also cause a prominent range cell migration even if its amplitude is far smaller than the range resolution. SAR/GMTI and MTI techniques will, in general, become invalid for micromotion targets. The influence is eventually demonstrated by the simulated data in the airborne single-channel geometry, and it can be used for SAR image interpretation as well as passive jamming.

Description: The application potential of remotely sensed optical imagery is boosted through the increase in spatial resolution, and new analysis, interpretation, classification, and change detection methods are developed. Together with all the advantages, shadows are more present in such images, particularly in urban areas. This may lead to errors during data processing. The task of automatic shadow detection is still a current research topic. Since image acquisition is influenced by many factors such as sensor type, sun elevation and acquisition time, geographical coordinates of the scene, conditions and contents of the atmosphere, etc., the acquired imagery has highly varying intensity and spectral characteristics. The variance of these characteristics often leads to errors, using standard shadow detection methods. Moreover, for some scenes, these methods are inapplicable. In this paper, we present an alternative robust method for shadow detection. The method is based on the physical properties of a blackbody radiator. Instead of static methods, this method adaptively calculates the parameters for a particular scene and allows one to work with many different sensors and images obtained with different illumination conditions. Experimental assessment illustrates significant improvement for shadow detection on typical multispectral sensors in comparison to other shadow detection methods. Examples, as well as quantitative assessment of the results, are presented for Landsat-7 Enhanced Thematic Mapper Plus, IKONOS, WorldView-2, and the German Aerospace Center (DLR) 3K Camera airborne system.

Description: In recent years, independent component analysis (ICA) has been applied to unmix the hyperspectral data since it can perform without the prior knowledge of ground objects. The traditional ICA algorithm regards the extracted independent components as unmixing results, which is not reasonable for hyperspectral imagery, because different endmembers are not actually independent from each other. In order to solve this problem, a new approach, named as constrained ICA, is proposed, in which we consider “uncorrelation” instead of “independence.” Two constraints of the hyperspectral data (the abundance nonnegative and abundance sum-to-one constraints) are introduced to the ICA, changing its objective function based on independence assumption. Furthermore, we develop a technique, called as adaptive abundance modeling, to characterize the statistical distribution of the data. The model is automatically constructed according to the given data, which can encourage the algorithm that is applicable to various hyperspectral images with different statistical characteristics. The experimental results on both simulated and real hyperspectral data demonstrate that the proposed approach can obtain more accurate results with respect to existing algorithms. As an algorithm with no need of prior spectral knowledge, our method provides an effective solution for the blind unmixing of the hyperspectral data.

Description: Proposed in recent literature, a novel two-stage stratified hierarchical hybrid remote-sensing image understanding system (RS-IUS) architecture comprises the following: 1) a first-stage pixel-based application-independent top-down (physical-model-driven and prior-knowledge-based) preliminary classifier and 2) a second-stage battery of stratified hierarchical context-sensitive application-dependent modules for class-specific feature extraction and classification. The first-stage preliminary classifier is implemented as an operational automatic near-real-time per-pixel multisource multiresolution application-independent spectral-rule-based decision-tree classifier (SRC). To the best of the author's knowledge, SRC provides the first operational example of an automatic multisensor multiresolution Earth-observation (EO) system of systems envisaged under ongoing international research programs such as the Global Earth Observation System of Systems (GEOSS) and the Global Monitoring for the Environment and Security (GMES). For the sake of simplicity, the original SRC formulation adopts crisp (hard) membership functions unsuitable for dealing with component cover classes of mixed pixels (class mixture). In this paper, the crisp (hierarchical) SRC first stage of a two-stage hybrid RS-IUS is replaced by a fuzzy (horizontal) SRC. In operational terms, a relative comparison of the fuzzy SRC against its crisp counterpart reveals that the former features the following: 1) the same degree of automation which cannot be surpassed, i.e., they are both “fully automatic”; 2) a superior map information/knowledge representation where component cover classes of mixed pixels are modeled; 3) the same robustness to changes in the input multispectral imagery acquired across time, space, and sensors; 4) a superior maintainability/scalability/reusability guaranteed by an internal horizontal (flat) modular structure independent of hierarchy; and 5) a computation time increased - - by 30% in a single-process single-thread implementation. This computation overload would reduce to zero in a single-process multithread implementation. In line with theory, the conclusion of this work is that the operational qualities of the fuzzy and crisp SRCs differ, but both SRCs are suitable for the development of operational automatic near-real-time multisensor satellite-based measurement systems such as those conceived as a visionary goal by the ongoing GEOSS and GMES research initiatives.

Description: Herein we provide a description of the atmospheric infrared sounder (AIRS) version 5 (v5) carbon monoxide (CO) retrieval algorithm and its validation with the DACOM in situ measurements during the INTEX-A and -B campaigns. All standard and support products in the AIRS v5 CO retrieval algorithm are documented. Building on prior publications, we describe the convolution of in situ measurements with the AIRS v5 CO averaging kernel and first-guess CO profile as required for proper validation. Validation is accomplished through comparison of AIRS CO retrievals with convolved in situ CO profiles acquired during the NASA Intercontinental Chemical Transport Experiments (INTEX) in 2004 and 2006. From 143 profiles in the northern mid-latitudes during these two experiments, we find AIRS v5 CO retrievals are biased high by 6%–10% between 900 and 300 hPa with a root-mean-square error of 8%–12%. No significant differences were found between validation using spiral profiles coincident with AIRS overpasses and in-transit profiles under the satellite track but up to 13 h off in time. Similarly, no significant differences in validation results were found for ocean versus land, day versus night, or with respect to retrieved cloud top pressure or cloud fraction.

Description: This paper demonstrates a new method called progressive discrimination (PD) for mapping an individual spectral class within an image. Given training data for a target, PD iteratively samples nontarget image pixels using a collapsing distance threshold within the space of an evolving discriminant function. This has the effect of progressively isolating the target class from similar spectra in the image. PD was compared to Bayesian maximum likelihood classification, mixture-tuned matched filtering, spectral angle mapping, and support vector machine methods for mapping three different invasive species in two types of high-spatial-resolution airborne hyperspectral imagery, AVIRIS and AISA. When tested with 20 different randomly selected groups of training fields, PD classification accuracies for the two spectrally distinct plant species in these images had an average of 98% and a standard deviation of 1%. These randomized trials were capable of providing higher classification accuracies than the best results obtained by two expert analysts using existing methods. For the third species that was less distinct, PD results were comparable to the results obtained by experienced analysts with existing methods. Despite requiring less input from the user than many techniques, PD provided more consistent high mapping accuracy, making it an ideal tool for scientists and land use managers who are not trained in image processing.

Description: The Earth's atmosphere heavily affects the remote sensing images collected by spaceborne passive optical sensors due to radiation–matter interaction phenomena like radiation absorption, scattering, and thermal emission. A complex phenomenon is the adjacency effect, i.e., radiation reflected by the ground that, due to the atmospheric scattering, is being seen in a viewing direction different from that corresponding to the ground location that reflected it. Adjacency gives rise to crosstalk between neighboring picture elements up to a distance that depends on the width of the integral kernel function employed for the mathematical modeling of the problem. As long as the atmosphere is a linear space-invariant system, the adjacency can be modeled as a low-pass filter, with the atmospheric point spread function (APSF) applied to the initial image. In this paper, a direct method of estimating the discrete normalized APSF (NAPSF) using images gathered by high-resolution optical sensors is discussed. We discuss the use of the NAPSF estimate for deducing the Correction Spatial high-pass Filter (CSF)—a correction filter that removes the adjacency effect. The NAPSF estimation procedure has been investigated using statistical simulations, whose outcomes permitted us to identify the conditions under which the NAPSF could be measured with acceptable errors. The NAPSF estimation is examined for various natural images acquired by MOMS-2P, CHRIS, AVIRIS, and MIVIS.

Description: We exploit the amplitude information of a sequence of synthetic aperture radar (SAR) images, acquired at different times, in order to generate displacement time-series in areas characterized by large and/or rapid deformation, the size of which is on the order of the image's pixel dimensions. We follow the same rationale of the Small BAseline Subset (SBAS) differential SAR interferometry (DInSAR) approach, by coupling the available SAR images into pairs characterized by a small separation between the acquisition orbits. We exploit the amplitudes of the selected image pairs in order to calculate the relative across-track (range) and along-track (azimuth) pixel-offsets (PO). Finally, we apply the SBAS inversion strategy to retrieve the range and azimuth displacement time-series. This approach, referred to as pixel-offset (PO-) SBAS technique, has been applied to a set of 25 ENVISAT SAR observations of the Sierra Negra caldera, Galápagos Islands, spanning the 2003–2007 time interval. The retrieved deformation time-series show the capability of the technique to detect and measure the large displacements affecting the inner part of the caldera that, in correspondence to the October 2005 eruption, reached several meters. Moreover, by comparing the PO-SBAS results to continuous GPS measurements, we estimate that the accuracy of the PO-SBAS time-series is on the order of 1/30th of a pixel for both range and azimuth directions.

Description: The advent of polarimetric synthetic aperture radar has spurred a growing interest in statistical models for complex-valued covariance matrices, which is the common representation of multilook polarimetric radar images. In this paper, we respond to an emergent need by proposing statistical tests for the simple and composite goodness-of-fit (GoF) problem for a class of compound matrix distributions. The tests are based on Mellin-kind matrix cumulants. These are derived from a novel characteristic function for positive definite Hermitian random matrices, defined in terms of a matrix-variate Mellin transform instead of the conventional Fouriér transform, and belong to a new framework for statistical analysis of multilook polarimetric radar data recently introduced by the authors. The cumulant-based tests are easy to compute, and the asymptotic sampling distribution of the test statistic is chi-square distributed in the simple hypothesis case. Under the composite hypothesis, the sampling distribution is obtained by Monte Carlo simulations. We evaluate the power of the proposed GoF tests with simulated data. We also use them to assess the fit of several matrix distributions to real data acquired by Radarsat-2 in fine-quad polarization mode.

Description: We report on the preflight spectral calibration of the first Orbiting Carbon Observatory (OCO) instrument. In particular, the instrument line shape (ILS) function as well as spectral position was determined experimentally for all OCO channels. Initial determination of these characteristics was conducted through laser-based spectroscopic measurements. The resulting spectral calibration was validated by comparing solar spectra recorded simultaneously by the OCO flight instrument and a collocated high-resolution Fourier transform spectrometer (FTS). The spectral calibration was refined by optimizing parameters of the ILS as well as the dispersion relationship, which determines spectral position, to yield the best agreement between these two measurements. The resulting ILS profiles showed agreement between the spectra recorded by the spectrometers and FTS to approximately 0.2% rms, satisfying the preflight spectral calibration accuracy requirement of better than 0.25% rms.

Description: Since the adoption of the rational polynomial coefficient (RPC) adjustment model as a preferred sensor orientation model for high-resolution optical satellite imagery, it has been demonstrated to be effective and robust. However, no publication discusses the application of the RPC adjustment model to the 3-D intersection from SAR stereoscopic pairs. This paper aims to validate the RPC adjustment model for spaceborne SAR stereoscopic orientation. Initially, a brief summary of the mathematical background of the RPC model is presented. Then, the SAR orientation errors are analyzed, namely, the orientation parameters, having the same net effect on the object-image relationship, and combined into a single adjustment parameter. The required adjustment is then discussed, and the formulation of the adjustment model is outlined. Finally, a number of designed adjustment experiments controlled via well-surveyed corner reflectors and an existing digital elevation model plus a digital orthophotograph map at the scale of 1:10 000 are performed. Multisensor images of TerraSAR-X, COnstellation of small Satellites for the Mediterranean basin Observation (COSMO-SkyMed), and Satellite Pour l'Observation de la Terre-5 (SPOT-5) over the Guangzhou area are used as test data. The results demonstrate that the proposed method can be generally applied to different imaging systems or the stereoscopic fusion of combined data and can achieve high orientation accuracy.

Description: The Dual-frequency Precipitation Radar (DPR) on the core satellite of the Global Precipitation Measurement mission will measure the radar reflectivity factor in the Ku- and Ka-bands. A rain-rate retrieval algorithm that does not require a surface reference was developed (called the MA04 method). However, MA04 cannot give the true solution in some cases of heavy rainfall. MA04 is a simplified version of the iterative backward retrieval method (IBRM), and the IBRM is equivalent to the forward retrieval method with a constraint. The purpose of this study is to clarify the essential conditions under which the IBRM and MA04 can give the true solution (the conditions are referred to as “the applicability” as in the title). For the purpose, DPR measurements are simulated under simplified assumptions. The applicability of the IBRM and MA04 is closely related to the magnitude of internal attenuation. The upper limit of rain rate for which the IBRM can obtain a true solution is 10 to 20 mm · h -1 if the internal attenuation occurs between the top and middle of the target range bin and the vertical resolution is 0.25 km. The upper limit of rain rate for which MA04 can obtain a true solution is dependent on the number of range bins, and it is 24 to 36 mm · h -1 in the case of n = 12. MA04 can apply a wider range of rainfall than the IBRM because MA04 tends to select the solution with the smallest attenuation among possible solutions.

Description: The Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) mission responds to the need to improve the understanding of the interactions between cloud, aerosol, and radiation processes. The fundamental mission objective is to constrain retrievals of cloud and aerosol properties such that their impact on top-of-atmosphere (TOA) radiative fluxes can be determined with an accuracy of 10 W · m -2 . However, TOA fluxes cannot be measured instantaneously from a satellite. For the EarthCARE mission, fluxes will be estimated from the observed solar and thermal radiances measured by the Broadband Radiometer (BBR). This paper describes an approach to obtain shortwave (SW) fluxes from BBR radiance measurements. The retrieval algorithms are developed relying on the angular distribution models (ADMs) employed by Clouds and the Earth's Radiant Energy System (CERES) instrument. The solar radiance-to-flux conversion for the BBR is performed by simulating the Terra CERES ADMs us ing a backpropagation artificial neural network (ANN) technique. The ANN performance is optimized by testing different architectures, namely, feedforward, cascade forward, and a customized forward network. A large data set of CERES measurements used to resemble the forthcoming BBR acquisitions has been collected. The CERES BBR-like database is sorted by their surface type, sky conditions, and scene type and then stratified by four input variables (solar zenith angle and BBR SW radiances) to construct three different training data sets. Then, the neural networks are analyzed, and the adequate ADM classification scheme is selected. The results of the BBR ANN-based ADMs show SW flux retrievals compliant with the CERES flux estimates.

Description: Although originally designed solely for wind retrieval, the QuikSCAT scatterometer has proved to be a useful tool for rain estimation as well. Resolution enhancement algorithms designed for QuikSCAT allow for ultra-high-resolution (UHR) (2.5 km) simultaneous wind and rain (SWR) retrieval. The principle advantage of UHR SWR estimation is that compared to conventional resolution, the higher resolution allows for identification of much smaller rain events and their effects on the wind field. To enable SWR retrieval, we adjust the geophysical model function to account for rain effects such as attenuation and increased backscatter due to increased surface roughness. Two possible rain models are proposed, a phenomenological rain model and an effective rain model. Both models are compared by evaluating data fit and rain estimation performance. Comparisons of a co-located data set show that QuikSCAT UHR SWR integrated rain rates are comparable to those from tropical rain measuring mission precipitation radar (TRMM PR) but have higher variance. Buoy comparisons reveal improved wind estimates in the presence of rain. The theoretic estimator bounds are compared to both the simulated estimator variance and the actual estimator variance. The estimator bounds indicate that despite high-noise levels, wind and rain information is still retrievable at UHR, although certain directions have degraded estimator bounds. Both rain models are compared to truth data and are shown to have comparable performance for most rain rates. Comparison with buoy measurements shows that in the presence of rain, QuikSCAT UHR SWR wind estimates have less bias and variability than wind-only estimates. Although QuikSCAT UHR SWR rain estimates are noisier than TRMM PR rain rates, they provide a useful rain flag for QuikSCAT winds.

Description: The authors propose a new sea ice detection method for a rotating Ku-band scatterometer with dual-polarization capability, such as SeaWinds on the Quick Scatterometer (QuikSCAT), based on probabilistic distances to ocean wind and sea ice geophysical model functions (GMFs) and evaluate its performance against other active and passive microwave algorithms. All the methods yield similar results during the sea ice growth season but show substantial differences during the spring and summer months. A detailed comparison based on high-resolution synthetic aperture radar and optical imagery shows that major discrepancies relate to newly formed, low-concentration, and water-saturated sea ice species. The new GMF-based algorithm for sea ice detection with QuikSCAT improves on the misclassification scores that affect other algorithms and provides daily sea ice masks at a 25-km resolution for use in ground processors that require the effective removal of sea ice contaminated pixels all year round.

Description: Synchronous and colocated optical and microwave signals from waves in the surf zone are presented and analyzed. The field data were collected using a high-resolution video system and a calibrated horizontally polarized marine radar during the decaying phase of a storm. The resulting changes in the received signals from varying environmental conditions were analyzed. The analysis of the optical signal histograms showed functional shapes that were in accordance with the expected imaging mechanisms from the breaking and nonbreaking waves. For the microwave returns, the histogram shape showed a little dependence on the environmental parameters and exhibited an inflexion point at high returned power that is attributed to a change in the scattering mechanism. The high intensity signals were clearly associated with active wave breaking. However, with either sensor, it can be difficult to effectively isolate the wave breaking signature from other sources, such as a remnant foam or the highly steepened nonbreaking waves. A combined method was developed using the joint histograms from both sensors, and it is shown to effectively discriminate between active breaking, remnant foam, and steepened waves. The new separation method allows a further analysis of the microwave scattering from the breaking waves and a better quantification of the length scales of the breaking wave roller and the spatial/temporal distribution of wave breaking and wave dissipation in the surf zone.

Description: This paper approaches wind field estimation from scatterometer measurements as the inversion of a noisy nonlinear sampling operation. The forward sampling model is presented and made discrete for practical purposes. Generally, the wind estimation problem is ill-posed at high resolution, which means that there are more parameters to estimate than measurements. A Bayesian approach based on maximum a posteriori (MAP) estimation is proposed to regularize the problem. This allows the simultaneous estimation of the regular samples of the high-resolution wind vector field directly from the noisy aperture-filtered backscatter σ° measurements. The MAP reconstruction approach is applied to the SeaWinds scatterometer, the examples are presented, and the results are compared to standard products. The MAP reconstruction method produces results that are consistent with standard products while preserving the higher spatial resolution information. The MAP estimates result in a similar resolution to the standard ultrahigh-resolution processing method but with a lower bias and a lower variability in the estimates.

Description: We have explored the possibility of obtaining first-order permeability estimates for saturated alluvial sediments based on the poro-elastic interpretation of the P-wave velocity dispersion inferred from sonic logs. Modern sonic logging tools designed for environmental and engineering applications allow one for P-wave velocity measurements at multiple emitter frequencies over a bandwidth covering 5 to 10 octaves. Methodological considerations indicate that, for saturated unconsolidated sediments in the silt to sand range and typical emitter frequencies ranging from approximately 1 to 30 kHz, the observable velocity dispersion should be sufficiently pronounced to allow one for reliable first-order estimations of the permeability structure. The corresponding predictions have been tested on and verified for a borehole penetrating a typical surficial alluvial aquifer. In addition to multifrequency sonic logs, a comprehensive suite of nuclear and electrical logs, an S-wave log, a litholog, and a limited number laboratory measurements of the permeability from retrieved core material were also available. This complementary information was found to be essential for parameterizing the poro-elastic inversion procedure and for assessing the uncertainty and internal consistency of corresponding permeability estimates. Our results indicate that the thus obtained permeability estimates are largely consistent with those expected based on the corresponding granulometric characteristics, as well as with the available evidence form laboratory measurements. These findings are also consistent with evidence from ocean acoustics, which indicate that, over a frequency range of several orders-of-magnitude, the classical theory of poro-elasticity is generally capable of explaining the observed P-wave velocity dispersion in medium- to fine-grained seabed sediments.

Description: We propose a new technique, named SISTEM, based on the elastic theory, to efficiently estimate 3-D displacements for producing deformation maps by integrating sparse Global Positioning System (GPS) measurements of deformations and differential interferometric synthetic aperture radar (DInSAR) maps of movements of the Earth's surface. Previous approaches in the literature to combine GPS and DInSAR data require two steps: a first step in which sparse GPS measurements are interpolated in order to fill in GPS displacements in the DInSAR grid and a second step to estimate the 3-D surface displacement maps by using a suitable optimization technique. One of the advantages of the proposed approach, compared to previous ones, is that it does not require the preliminary interpolation of the observed deformation pattern. Indeed, we propose a linear matrix equation which accounts for both the GPS and DInSAR data whose solution simultaneously provides the strain tensor, the displacement field, and the rigid body rotation tensor. The mentioned linear matrix equation is solved by using the weighted least square (WLS), thus assuring both numerical robustness and high computation efficiency. The methodology was tested on both synthetic and experimental data, these last from GPS and DInSAR measurements carried out on Mount Etna during the 2003-2004 period. In order to appreciate the accuracy of the results, the estimated standard errors computed by the WLS are provided. These tests also allow optimizing the choice of specific parameters of this algorithm. This method can be further exploited to account for other available data sets, such as additional interferograms or other geodetic data (e.g., leveling, tilt, etc.), in order to achieve higher accuracy.

Description: The panchromatic (PAN) sharpening of multispectral (MS) bands can be performed by fusing the PAN and MS images. Measuring similarity criterion computed among input images is one way to synthesize MS images in higher resolution based on either spectral or spatial domains. However, a few methods consider both spectral and spatial similarities. In this paper, the fusion between PAN and MS images is performed by engaging both similarities. We use the spectral histogram, recently introduced to characterize the spectral information of an image in different frequency ranges, as the spectral similarity criterion. This similarity suggests considering a statistical similarity measure between two spectral histograms of two images. Furthermore, we use the fourth-order correlation coefficient as a spatial similarity criterion instead of correlation coefficient. Meanwhile, in the decision level of fusion process, a proper threshold should be selected to determine whether the details should be injected or not. There is no reference to choose it in general cases, and this threshold is calculated for each set of input images separately and is based on intersecting two similarity curves. We do this by first calculating the spatial and spectral similarity criteria for some specific threshold values and then fit two similarity curves on these sample points by the spline interpolation method. Then, after decomposing input images using the nonsubsampled contourlet transform, we inject the PAN details into the MS details considering the selected threshold. The experimental results obtained by applying the proposed image fusion method indicate some improvements in the fusion performance.

Description: Satellite images are being used in many fields of research. One of the major issues of these types of images is their resolution. In this paper, we propose a new satellite image resolution enhancement technique based on the interpolation of the high-frequency subbands obtained by discrete wavelet transform (DWT) and the input image. The proposed resolution enhancement technique uses DWT to decompose the input image into different subbands. Then, the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new resolution-enhanced image by using inverse DWT. In order to achieve a sharper image, an intermediate stage for estimating the high-frequency subbands has been proposed. The proposed technique has been tested on satellite benchmark images. The quantitative (peak signal-to-noise ratio and root mean square error) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.

Description: The dates of crop developmental stages are important variables for many applications including assessment of the impact of abnormal weather on crop yield. Time-series 250-m vegetation-index (VI) data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) provide valuable information for monitoring the spatiotemporal changes of corn growth across large geographic areas. The goal of this study is to evaluate the performance of a new crop phenology detection method, namely, two-step filtering (TSF), for revealing the spatiotemporal pattern of specific corn developmental stages (early vegetative: V2.5; silking: R1; dent: R5; mature: R6) over an eight-year period (2001-2008) across Iowa, Illinois, and Indiana using MODIS derived Wide Dynamic Range VI data. Weekly crop progress reports produced by the U.S. Department of Agriculture National Agricultural Statistics Service (NASS) were used to assess the accuracy of TSF-based estimates of corn developmental stages. The results showed that the corn developmental stages could be estimated with high accuracy (the root mean squared error ranged from 4.1 to 5.5 days, the determination coefficient ranged from 0.66 to 0.84, and the coefficient of variation ranged from 2.1% to 3.7%) based on NASS-derived statistics on an agricultural statistics district level. In particular, the annual changes in the spatiotemporal patterns of the estimated silking stage had a high level of agreement with those of the NASS-derived statistics. These results suggested that the TSF method could provide local-scale information of corn phenological stages, which had an advantage over the NASS-derived statistics particularly in terms of the spatial resolution.

Description: The appearance of objects and surfaces in synthetic aperture radar (SAR) images significantly differs from the human perception of the environment. In addition, the quality of SAR data is degraded by speckle noise, superposing the true radiometric and textural information of the radar image. Hence, the interpretation of SAR images is considered to be more challenging compared to the analysis of optical data. However, in this paper, we demonstrate how information on the local development of speckle can be used for the differentiation of basic land cover (LC) types in a single-polarized SAR image. For that purpose, we specify the speckle characteristics of the following LC types: 1) water; 2) open land (farmland, grassland, bare soil); 3) woodland; and 4) urban area by means of an unsupervised analysis of scatter plots and standardized histograms of the local coefficient of variation. Next, we use this information for the implementation of a straightforward preclassification of single-polarized TerraSAR-X stripmap images by combining information on the local speckle behavior and local backscatter intensity. The output is either provided as a discrete classification or as a color composite image whose bands can be interpreted in terms of a fuzzy classification. The results of this paper show that unsupervised speckle analysis in high-resolution SAR images supplies valuable information for a differentiation of the water, open land, woodland, and urban area LC types. While the color composite image supports the visual interpretation of SAR data, the outcome of the fully automated discrete LC classification procedure represents a valuable preclassification image, showing overall accuracies of 77%-86%.

Description: The semi-analytic mode matching (SAMM) algorithm is a quick and efficient computational method that models wave scattering from objects in half spaces. This algorithm relies on appropriately choosing coordinate scattering centers (CSCs) for modal expansions, and successful automation of the CSC selection process is the goal of this paper. CSCs are found for several complex shaped scattering test objects by considering the radius of curvature (ROC) function for each object. The CSCs are found to be largely independent of frequency and located at cusps in the ROC function for scattering objects of modest aspect ratios. Additional CSCs may be required in numbers that are directly proportional to the aspect ratios of more complicated objects, but again, the extra CSC locations are largely independent of frequency. Excellent results are found comparing SAMM and finite-difference frequency domain for 2-D scattering objects that are 0.1-15 wavelengths in size.

Description: Endmember extraction is usually based on the solution of a system of linear equations that allows the identification of some basic spectra in terms of which the observed mixed spectra may be expressed as linear combinations. In this paper, we propose to close the loop of such an approach by identifying only the basic spectra that reproduce the dominant cover classes of a region as endmembers, and distinguishing them from outlier spectra present in the scene. The latter are often confused by other methods as endmember spectra, whereas in many practical applications, they are treated as anomalies or targets in the scene. Thus, the proposed method identifies endmembers in a robust way, separating them from outliers.

Description: Edge detection is a crucial approach for the location and acreage calculation of oil slick when oil spills on the sea. In this paper, in view of intensity inhomogeneity, high noise, and blurring of oil slick infrared (IR) aerial images, a novel algorithm is proposed to detect the edges of oil slick IR aerial images. In the proposed algorithm, we define an energy function model combining a region-scalable-fitting concept and a global minimization active contour (GMAC) model. The proposed novel algorithm avoids the existence of local minima and meanwhile deals with the intensity inhomogeneity, noise, and weak edge boundaries exiting in oil spill IR images. In the process of the active contour evolving toward object boundaries and numerical minimization, a dual formulation is used for overcoming drawbacks of the usual level set and gradient descent method so that the process of minimization can be much easier and our algorithm is independent of the initial position of the contour. Using the proposed algorithm, we can gain continuous and closed edges of oil slick IR aerial images. The experiment results have shown that the proposed algorithm outperforms conventional edge detection methods and other algorithms in terms of the efficiency and accuracy. In addition, the proposed algorithm is extended to synthetic-aperture-radar oil slick images, and satisfactory results of edge extraction can be obtained as well.

Description: This paper is aimed at developing an applicable and feasible facet model, which formulation should be tractable and time saving for personal computers to take charge of the efficient evaluation on the complex reflective function of large-scope 2-D oceans, either in the monostatic or bistatic case. The sea surface is envisaged as a two-scale profile on which the long waves are locally approximated by planar facets. The microscopic profile within a facet is assumed to be represented by a set of sinusoidal ripple patches. The complex reflective function of each modified facet is evaluated by a modified formula of the original Bass and Fuks' two-scale model, in which the phase factor of each facet is with the capillary wave modification. Several examples with application to the frozen or time-evolving case are given to prove the implementation.

Description: In this paper, we propose a method for automatic enhancement and identification of partial curvilinear structures. The accurate identification of line structures in geophysical images plays an important role in geophysical interpretation and the detection of subsurface structures. The method was applied on geophysical images in an effort to recognize the linear patterns of subsurface architectural structures that exist in archaeological sites. To our knowledge, the problem of identification of curvilinear structures in geophysical images for archaeological sites is faced for the first time. The method efficiently combines a rotation- and scale-invariant filter and a pixel-labeling method, providing a robust enhancement and detection of mostly line structures in 2-D grayscale images, respectively. Experimental results on real and synthetic images and comparison with existing methods in the literature indicated the reliable performance of the proposed scheme.

Description: In a combined experimental and model study, we investigated the thermal L-band signatures of a sandy soil with periodic topography (furrows) with dimensions close to the observation wavelength of 21 cm. Measurements were carried out with a radiometer mounted on a tower and aimed at a soil box with an artificially prepared furrowed soil surface. Corresponding reflectivities were derived from brightness temperature measurements performed under dry and moist conditions, with the furrow direction either along or perpendicular to the plane of incidence. Results showed that the furrows had a pronounced effect on the reflectivity, depending on the polarization of the observed radiance, the direction of the furrows, and the soil moisture. A physical reflectivity model for dielectric periodic surfaces was used to explain the soil reflectivities measured for the different furrow directions and soil-water contents. Using this model improved the agreement between the measured and modeled reflectivities considerably compared to the Fresnel reflectivities. The observed dependence of soil reflectivity on furrow orientation and soil moisture could be reproduced by the reflectivity model. The quantitative agreement with the observed reflectivities was further improved by using a simple empirical approach to consider the small-scale heterogeneity of the top soil layer.

Description: Classification of nonlinearly separable data by nonlinear support vector machines (SVMs) is often a difficult task, particularly due to the necessity of choosing a convenient kernel type. Moreover, in order to get the optimum classification performance with the nonlinear SVM, a kernel and its parameters should be determined in advance. In this paper, we propose a new classification method called support vector selection and adaptation (SVSA) which is applicable to both linearly and nonlinearly separable data without choosing any kernel type. The method consists of two steps: selection and adaptation. In the selection step, first, the support vectors are obtained by a linear SVM. Then, these support vectors are classified by using the K-nearest neighbor method, and some of them are rejected if they are misclassified. In the adaptation step, the remaining support vectors are iteratively adapted with respect to the training data to generate the reference vectors. Afterward, classification of the test data is carried out by 1-nearest neighbor with the reference vectors. The SVSA method was applied to some synthetic data, multisource Colorado data, post-earthquake remote sensing data, and hyperspectral data. The experimental results showed that the SVSA is competitive with the traditional SVM with both linearly and nonlinearly separable data.

Description: The curse of dimensionality is the main reason for the computational complexity and the Hughes phenomenon in supervised hyperspectral classification. Previous studies seldom consider in a simultaneous fashion the real situation of insufficiency of available training samples, particularly for small land covers that often contain the key information of the scene, and the problem of complexity. In this paper, the capabilities of a feature reduction technique used for discrimination are combined with the advantages of a Bayesian learning-based probabilistic sparse kernel model, the relevance vector machine (RVM), to develop a new supervised classification method. In the proposed method, the hyperdimensional data are first transformed to a lower dimensionality feature space using the feature reduction technique to maximize separability between classes. The transformed data are then processed by a multiclass RVM classifier based on the parallel architecture and one-against-one strategy. To verify the effectiveness of the method, experiments were carried out on real hyperspectral data. The results are compared with the most efficient supervised classification techniques such as the support vector machine using appropriate performance indicators. The results show that the proposed method performs better than the other approaches particularly for small and scattered landcover classes which are harder to be precisely classified. In addition, this method has the advantages of low computational complexity and robustness to the Hughes phenomenon.

Description: The systematic evaluation of synthetic aperture radar (SAR) data analysis tools, such as segmentation and classification algorithms for geographic information systems, is difficult given the unavailability of ground-truth data in most cases. Therefore, testing is typically limited to small sets of pseudoground-truth data collected manually by trained experts, or primitive synthetic sets composed of simple geometries. To address this issue, we investigate the potential of employing an alternative approach, which involves the synthesis of SAR data and corresponding label fields from real SAR data for use as a reliable evaluation testbed. Given the scale-dependent nonstationary nature of SAR data, a new modeling approach that combines a resolution-oriented hierarchical method with a region-oriented binary tree structure is introduced to synthesize such complex data in a realistic manner. Experimental results using operational RADARSAT SAR sea-ice data and SIR-C/X-SAR land-mass data show that the proposed hierarchical approach can better model complex nonstationary scale structures than local MRF approaches and existing nonparametric methods, thus making it well suited for synthesizing SAR data and the corresponding label fields for potential use in the systematic evaluation of SAR data analysis tools.

Description: Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called as endmembers) in each mixed pixel collected by an imaging spectrometer. In many situations, the identification of the endmember signatures in the original data set may be challenging due to insufficient spatial resolution, mixtures happening at different scales, and unavailability of completely pure spectral signatures in the scene. However, the unmixing problem can also be approached in semisupervised fashion, i.e., by assuming that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In practice, this is a combinatorial problem which calls for efficient linear sparse regression (SR) techniques based on sparsity-inducing regularizers, since the number of endmembers participating in a mixed pixel is usually very small compared with the (ever-growing) dimensionality (and availability) of spectral libraries. Linear SR is an area of very active research, with strong links to compressed sensing, basis pursuit (BP), BP denoising, and matching pursuit. In this paper, we study the linear spectral unmixing problem under the light of recent theoretical results published in those referred to areas. Furthermore, we provide a comparison of several available and new linear SR algorithms, with the ultimate goal of analyzing their potential in solving the spectral unmixing problem by resorting to available spectral libraries. Our experimental results, conducted using both simulated and real hyperspectral data sets collected by the NASA Jet Propulsion Laboratory's Airborne Visible In- - frared Imaging Spectrometer and spectral libraries publicly available from the U.S. Geological Survey, indicate the potential of SR techniques in the task of accurately characterizing the mixed pixels using the library spectra. This opens new perspectives for spectral unmixing, since the abundance estimation process no longer depends on the availability of pure spectral signatures in the input data nor on the capacity of a certain endmember extraction algorithm to identify such pure signatures.

Description: This paper introduces a new supervised technique to segment hyperspectral images: the Bayesian segmentation based on discriminative classification and on multilevel logistic (MLL) spatial prior. The approach is Bayesian and exploits both spectral and spatial information. Given a spectral vector, the posterior class probability distribution is modeled using multinomial logistic regression (MLR) which, being a discriminative model, allows to learn directly the boundaries between the decision regions and, thus, to successfully deal with high-dimensionality data. To control the machine complexity and, thus, its generalization capacity, the prior on the multinomial logistic vector is assumed to follow a componentwise independent Laplacian density. The vector of weights is computed via the fast sparse multinomial logistic regression (FSMLR), a variation of the sparse multinomial logistic regression (SMLR), conceived to deal with large data sets beyond the reach of the SMLR. To avoid the high computational complexity involved in estimating the Laplacian regularization parameter, we have also considered the Jeffreys prior, as it does not depend on any hyperparameter. The prior probability distribution on the class-label image is an MLL Markov-Gibbs distribution, which promotes segmentation results with equal neighboring class labels. The -expansion optimization algorithm, a powerful graph-cut-based integer optimization tool, is used to compute the maximum a posteriori segmentation. The effectiveness of the proposed methodology is illustrated by comparing its performance with the state-of-the-art methods on synthetic and real hyperspectral image data sets. The reported results give clear evidence of the relevance of using both spatial and spectral information in hyperspectral image segmentation.

Description: From its launch in 2006, the phased array L-band synthetic aperture radar (PALSAR) onboard the advanced land observing satellite (ALOS) has acquired many dual-polarized (FBD) images with a 70-km swath width, aiming to produce spatially consistent coverage over tropical rainforest. This paper investigates the relevancy of PALSAR orthorectified FBD product at 50-m resolution for regional land cover classification by the support vector machines (SVM). Our test site is the Riau province, Sumatra island, Indonesia, known to hold vast area of natural peatland forest with an extreme biodiversity threatened by industrial deforestation. Since it is demonstrated the radiometric information (HH and HV channels) cannot be solely used to achieve a good classification, the spatial information in these orthorectified data is investigated. A new tool using the recursive feature elimination SVM-based process and the textural Haralick's parameters is introduced. The real contribution of textures within the land cover classification can be understood. A small set of textural parameters is determined at local scale while being optimal for the land cover discrimination. The SVM-based classifier is carried out across the whole Riau province and its results are compared with a Landsat-based estimation. The agreement is over 70% with six classes and 86% for the natural forest map. These results are remarkable since only one PALSAR FBD product is used and this assessment is performed on more than 40 million pixels. The results confirm the high potential of the PALSAR sensor for forest monitoring at regional, if not global scale.

Description: We propose a multistream discrete hidden Markov model (DHMM) framework and apply it to the problem of land-mine detection using ground-penetrating radar (GPR). We hypothesize that each signature (mine or nonmine) can be characterized better by multiple synchronous sequences representing features that capture different environments and different radar characteristics. This paper is motivated by the fact that mines and clutter objects can have different characteristics depending on the mine type, soil and weather conditions, and burial depth. Thus, ideally different sets of specialized feature extraction mechanisms may be needed to achieve high detection and low false alarm rates. In order to fuse the different modalities, a multistream DHMM that includes a stream relevance weighting component is developed. The relevance weight of each stream depends on the symbols and the states. We reformulate the Baum-Welch and the minimum classification error/gradient probabilistic descent learning algorithms to include stream relevance weights and partial state probabilities. We generalize their objective functions and derive the necessary conditions to update all model parameters simultaneously. The results on a synthetic data set and a collection of GPR signatures show that the proposed multistream DHMM framework outperforms the basic single-stream DHMM where all the streams are treated equally important.

Description: The simultaneous detection and identification of multiple targets using electromagnetic induction (EMI) time-domain sensors remains a challenge due to the fast decay of the magnetic field with sensor-target distance. For example, the signal from a weak yet shallow target or clutter item can overshadow that from a much larger yet deeper unexploded ordnance (UXO), potentially resulting in erroneous localization and/or identification. We propose, in this paper, a method based on the Gauss–Newton algorithm for the inversion of multiple targets within the field of view of sensors operating at EMI frequencies (tens of hertz to a few hundred kilohertz). In order to minimize the number of unknowns to invert for, the polarizability tensor is written as a time-independent orientation matrix multiplied by a time-dependent diagonal intrinsic polarizability tensor. Similarly, position is supposed to be time independent so that both position and orientation angles are inverted only once using all time channels collected by the instrument. Moreover, using the dipole approximation, we are able to compute the Jacobian in closed form for instruments with either square or circular primary field coils, thus contributing to the speed of the algorithm. Validating results are shown based on the measurement data collected with two EMI sensors on various types of UXO.

Description: Retrieval of leaf biochemical parameters from reflectance measurements using model inversion generally faces “ill-posed” problems, which dramatically decreases the estimation accuracy of an inverse model. While the standard approach for model inversion retrieves various parameters simultaneously, usually only based on one merit function, the new approach proposed in this paper assigns a specific merit function for each retrieved parameter. Each merit function is specified in terms of the wavelength domains that the given parameter was found to be specifically sensitive to in an earlier sensitivity analysis. The approach has been validated with both in situ measured data sets and an artificial data set of 10 000 spectra simulated by the PROSPECT model. Results indicate that the new approach greatly improves the performance of inversion models, with root-mean-square error (rmse) values for chlorophyll content (Chl), equivalent water thickness (EWT), and leaf mass per area (LMA), based on the simulated data, of 7.12 $muhbox{g/cm}^{2}$ , 0.0012 $hbox{g/cm}^{2}$ , and 0.0019 $hbox{g/cm}^{2}$ , respectively, compared with 11.36 $muhbox{g/cm}^{2}$ , 0.0032 $hbox{g/cm}^{2}$ , and 0.0040 $hbox{g/cm}^{2}$ when using the standard approach. As for field-measured data sets, the proposed approach also greatly outperformed the standard approach, with respective rmse values of 8.11 $muhbox{g/cm}^{2}$ , 0.0012 $ hbox{g/cm}^{2}$ , and - - 0.0008 $hbox{g/cm}^{2}$ for Chl, EWT, and LMA when all data are pooled, compared with 11.84 $mu hbox{g/cm}^{2}$ , 0.0020 $hbox{g/cm}^{2}$ , and 0.0027 $hbox{g/cm}^{2}$ when using the standard approach. Hence, the proposed approach for model inversion can largely alleviate the “ill-posed” problem, and it could be widely applied for retrieving leaf biochemical parameters.

Description: Frequency- and temperature-dependent complex permittivity or conductivity of a silty clay loam were examined in a broad saturation and porosity range with network analyzer technique (1 MHz–10 GHz, 5 $^{circ}hbox{C}$ –40 $^{circ}hbox{C}$ , coaxial transmission line and open ended coaxial cells). An advanced mixture model based on the well-known Lichtenecker–Rother model (ALRM) was developed and used to parameterize complex permittivity or conductivity at a measurement frequency of 1 GHz under consideration of a dependence of the so-called structure parameter as well as the apparent pore water conductivity on saturation and porosity. The ALRM is compared with frequently applied mixture models: complex refractive index model, Looyenga–Landau–Lifschitz model, Bruggeman–Hanai–Sen model, and Maxwell–Garnet model as well as empirical calibration functions. Comparison of ALRM applied to the investigated frequency and temperature range with sophisticated broadband relaxation models indicates the potential and the limitation to predict the high-frequency electromagnetic material properties.

Description: A stochastic approach is developed to resolve the scale variability between point and aerospace measurements in ocean color match-up sites. The model used the differences between in situ and aerospace-observed spectra and ocean color model inversion to estimate the subscale variability of apparent and inherent optical properties (IOPs). The model was tested and validated against three sets of ocean color data: simulated, in situ measured, and satellite data sets. The results showed that the variability of chlorophyll-a absorption was derived with high accuracy. Errors in derived subscale variability of detritus–gelbstoff absorption and particle scattering were larger than those of chlorophyll-a. The subscale radiometric variability was found to be proportional to that of IOPs and decreased with increasing water turbidity. The subpixel variability of reduced resolution ocean color image was derived with less than 12% of relative errors in clear and moderate turbid waters. Larger errors were obtained in estuarine turbid waters. Better accuracy was obtained for match-up sites with high internal contrast, i.e., spatial variability.

Description: Automatic image registration (AIR) is still a present challenge for the remote sensing community. Although a wide variety of AIR methods have been proposed in the last few years, there are several drawbacks which avoid their common use in practice. The recently proposed scale invariant feature transform (SIFT) approach has already revealed to be a powerful tool for the obtention of tie points in general image processing tasks, but it has a limited performance when directly applied to remote sensing images. In this paper, a new AIR method is proposed, based on the combination of image segmentation and SIFT, complemented by a robust procedure of outlier removal. This combination allows for an accurate obtention of tie points for a pair of remote sensing images, being a powerful scheme for AIR. Both synthetic and real data have been considered in this work for the evaluation of the proposed methodology, comprising medium and high spatial resolution images, and single-band, multispectral, and hyperspectral images. A set of measures which allow for an objective evaluation of the geometric correction process quality has been used. The proposed methodology allows for a fully automatic registration of pairs of remote sensing images, leading to a subpixel accuracy for the whole considered data set. Furthermore, it is able to account for differences in spectral content, rotation, scale, translation, different viewpoint, and change in illumination.

Description: We propose a polarimetric two-scale surface scattering model employed to retrieve the surface parameters of bare soils from polarimetric synthetic aperture radar or scatterometer data. The scattering surface considered here is composed of slightly rough randomly tilted facets, for which the small perturbation scattering method holds. The facet random tilt causes both a random variation of the local incidence angle and a random rotation of the local incidence plane around the line of sight. Unlike other similar already existing approaches, our method considers both these stochastic effects in the analytical evaluation of the facet scattering matrix, and their statistical modeling is derived from a proper statistical description of the scattering surface. In particular, we assume that the facet slope (i.e., the slope of the large-scale surface roughness) is a Gaussian random variable, in agreement with both classical and fractal surface models. The proposed scattering model is then used to retrieve bare soil moisture and (large-scale) roughness from the co- and cross-polarized ratios. The performance of the resulting retrieval algorithm and its validity limits are finally assessed by comparing obtained results to “ in situ” measurements. To this aim, data from different measurement campaigns available in literature are employed.

Description: Earth observation satellites provide a valuable source of data which when conveniently processed can be used to better understand the Earth system dynamics. In this regard, one of the prerequisites for the analysis of satellite image time series is that the images are spatially coregistered so that the resulting multitemporal pixel entities offer a true temporal view of the area under study. This implies that all the observations must be mapped to a common system of grid cells. This process is known as gridding and, in practice, two common grids can be used as a reference: 1) a grid defined by some kind of external data set (e.g., an existing land-cover map) or 2) a grid defined by one of the images of the time series. The aim of this paper is to study the impact that gridding has on the quality of satellite time series. More precisely, the impact of the so-called gridding artifacts is quantified using a time series of 12 images acquired over The Netherlands by the Medium Resolution Imaging Spectrometer (MERIS). First, the impact of selecting a reference grid is evaluated in terms of geolocation errors and pixel overlap. Then, the effect of observation geometry is studied as nongeostationary satellites, like MERIS, can acquire images from the same area from a number of orbits. Finally, a high-resolution land-cover data set is used to account for temporal information consistency (pixel homogeneity in terms of land-cover composition). Results have shown an average pixel overlap with the nearest pixel between 20% and 41% depending on the selected reference grid and on the differences in observation geometry. These results indicate that inappropriate gridding might result in collocated time series that are not adequate for temporal studies at pixel level (particularly over nonhomogeneous areas) and that, in any case, it is interesting to identify areas with low pixel overlap in order to further analyze the reliability of the products derived over these areas.

Description: Microwave radiometry soil moisture retrieval methods suffer from uncertainties about the representation of several effects, including dielectric mixing, surface roughness, and vegetation opacity. These uncertainties lead to two major types of error: systematic bias and random errors. The effect of the uncertainties is studied using the Soil Moisture Active Passive Algorithm Testbed, a simulation environment for evaluating error propagation in retrieval algorithms, and two different common retrieval algorithms (single and dual polarizations). The two types of errors are simulated by using different representations for each factor in the forward and retrieval parts. For both algorithms, this approach introduces a spatially variable bias, which is particularly large when using a single-polarization retrieval algorithm. This paper illustrates the emergence of both this bias and the random error due to uncertainty in the representation of vegetation and soil texture effects in retrieval algorithms. The dependence of these two types of error on vegetation and soil texture properties is shown through mapping them over the simulation region. The relative contribution of these errors to the total error is strongly dependent on the simulation conditions and is not necessarily indicative of what may be experienced during actual observations. Uncertainty due to roughness representation causes a lower error than uncertainty in vegetation opacity and dielectric mixing parameterizations in the simulated soil moisture retrieval. Summation and compensation of multiple errors can cause the estimate error to increase with improved radiative transfer knowledge, even after bias removal. The retrieval of soil moisture from microwave measurements depends on several other parameterizations that are also uncertain. This paper is limited to only three parameterizations that are considered to be among the larger contributors to bias.

Description: In this paper, a time-frequency analysis (TFA) is proposed to derive the backscattering properties of each pixel in single-polarization synthetic aperture radar (SAR) images. At high resolution (HR), some backscattering variations which are linked to the scene geometry and the surface property occur during the radar acquisition. TFA permits to retrieve these variations from the synthesized images. The proposed TFA algorithm is based on a sliding bandpass filtering in the Fourier domain, from which a spectrogram featuring the range and azimuth backscattering variations is derived. The spectrograms summarize the physical properties of each pixel. From the spectrogram analysis, four target classes representing the four main kinds of backscattering behaviors observed in SAR images are defined: frequency invariant, range variant, azimuth variant, and 2-D variant. These classes can further be linked to the physical properties of the objects. An original and simple set of five features estimated from spectrograms is proposed to classify point targets into these four classes. A performance assessment of this classification is carried out, using ONERA/RAMSES X-band airborne images acquired over the city of Toulouse, France. A robustness analysis is also conducted, in order to assess the impact of incidence angle and resolution on the classification performance. Finally, results are also given for spaceborne images (TerraSAR-X spotlight images). The physical interpretation developed in airborne case appears to be also valid for metric spaceborne data. After studying the TFA on HR spaceborne images, the tradeoff between HR coupled with TFA and medium resolution coupled with polarimetric analysis is investigated. Actually, TFA represents another way of characterizing the physical mechanisms involved in image formation.

Description: This paper proposes a novel framework called Gaussian process maximum likelihood for spatially adaptive classification of hyperspectral data. In hyperspectral images, spectral responses of land covers vary over space, and conventional classification algorithms that result in spatially invariant solutions are fundamentally limited. In the proposed framework, each band of a given class is modeled by a Gaussian random process indexed by spatial coordinates. These models are then used to characterize each land cover class at a given location by a multivariate Gaussian distribution with parameters adapted for that location. Experimental results show that the proposed method effectively captures the spatial variations of hyperspectral data, significantly outperforming a variety of other classification algorithms on three different hyperspectral data sets.

Description: Ground-based microwave radiometer profilers in the 20–60-GHz range operate continuously at numerous sites in different climate regions. Recent work suggests that a 1-D variational (1-DVAR) technique, coupling radiometric observations with outputs from a numerical weather prediction model, may outperform traditional retrieval methods for temperature and humidity profiling. The 1-DVAR technique is applied here to observations from a commercially available microwave radiometer deployed at Whistler, British Columbia, which was operated by Environment Canada to support nowcasting and short-term weather forecasting during the Vancouver 2010 Winter Olympic and Paralympic Winter Games. The analysis period included rain, sleet, and snow events ( $sim$ 235-mm total accumulation and rates up to 18 mm/h). The 1-DVAR method is applied “quasi-operationally,” i.e., as it could have been applied in real time, as no data were culled. The 1-DVAR-achieved accuracy has been evaluated by using simultaneous radiosonde and ceilometer observations as reference. For atmospheric profiling from the surface to 10 km, we obtain retrieval errors within 1.5 K for temperature and 0.5 $hbox{g/m}^{3}$ for water vapor density. The retrieval accuracy for column-integrated water vapor is 0.8 $ hbox{kg/m}^{2}$ , with small bias $(-0.1 hbox{kg/m}^{2})$ and excellent correlation (0.96). The retrieval of cloud properties shows a high probability of detection of cloud/no cloud (0.8/0.9, respectively), low false-alarm ratio (0.1), and cloud-base height estimate error within $sim$ 0.60 km.

Description: In several application domains (e.g., crop conversion subsidies, forestry, natural hazard mapping, and spatial planning), the ultimate operational objective of change-detection analysis is actually limited to the identification of only one (or few) specific land-cover transition(s) of interest (i.e., a “targeted” change-detection problem), disregarding all the other potential changes occurring in the area under analysis at the same time. Supervised change-detection techniques generally represent the most accurate methodological solution for mapping land-cover changes while identifying the associated land-cover transitions between two different dates. However, the application of these techniques depends on the availability of exhaustive ground-truth information for all the land-cover classes present in the area of interest at the times under investigation. Such a requirement is seldom satisfied since gathering a reliable ground truth for all the classes characterizing the considered scenes at the two dates under analysis presents several practical drawbacks and limitations (both in terms of time and economic cost) that may render this task almost impossible in most real-life cases. Nevertheless, to solve these specific types of problems, it would be highly beneficial for an operator to rely on a robust automatic technique that may allow an effective detection of the “targeted” land-cover transitions by taking into account only ground-truth information for the few classes of interest at each date (thus, avoiding the burden and cost associated to the collection of a full and exhaustive ground-truth data set at both times). In this paper, we address this challenging issue and propose a novel technique (formulated in terms of a compound decision problem) capable of identifying specific “targeted” land-cover transitions by exploiting the ground truth available only for the classes of interest at the two dates, while providin- - g accuracies comparable to those of traditional fully supervised change-detection methods. The proposed technique relies on a partially supervised approach that jointly exploits the expectation-maximization algorithm and an iterative labeling strategy based on Markov random fields accounting for spatial and temporal correlation between the two images. Moreover, the proposed method is applicable to images acquired by different sensors (or to different sets of features) at the two investigated times. Experimental results on different multitemporal and multisensor data sets confirmed the effectiveness and the reliability of the proposed technique.

Description: We present an algorithm aimed at correcting satellite orbit information for the generation of differential SAR interferometry (DInSAR) deformation time-series. Our approach exploits small baseline differential interferograms, to preserve their spatial coherence, and is directly compatible with the Small BAseline Subset (SBAS) DInSAR technique. In particular, the algorithm investigates the differential phase gradient directly computed from the wrapped interferograms, and is focused on the estimation of the perpendicular baseline and of the parallel baseline azimuth rate components, separately performed along the range and azimuth directions, respectively. Starting from the estimations carried out on the interferograms, we then retrieve the orbit correction associated with each SAR acquisition of our time-series by solving a system of linear equations via the SVD method, extending the SBAS inversion concept also to the orbit estimation problem. Key application of this technique is the generation of deformation time-series from interferometric sequences of RADARSAT-1 SAR acquisitions which are available for several areas in the world, but are characterized by significantly low accuracy of the orbit information. The presented results, obtained by processing a data set consisting of 33 RADARSAT-1 images of Big Island at Hawaii, show that we may retrieve DInSAR time-series with sub-centimeter accuracy, thus confirming the effectiveness of the proposed technique.

Description: Extracting well-distributed, reliable, and precisely aligned point pairs for accurate image registration is a difficult task, particularly for multisource remote sensing images that have significant illumination, rotation, and scene differences. The scale-invariant feature transform (SIFT) approach, as a well-known feature-based image matching algorithm, has been successfully applied in a number of automatic registration of remote sensing images. Regardless of its distinctiveness and robustness, the SIFT algorithm suffers from some problems in the quality, quantity, and distribution of extracted features particularly in multisource remote sensing imageries. In this paper, an improved SIFT algorithm is introduced that is fully automated and applicable to various kinds of optical remote sensing images, even with those that are five times the difference in scale. The main key of the proposed approach is a selection strategy of SIFT features in the full distribution of location and scale where the feature qualities are quarantined based on the stability and distinctiveness constraints. Then, the extracted features are introduced to an initial cross-matching process followed by a consistency check in the projective transformation model. Comprehensive evaluation of efficiency, distribution quality, and positional accuracy of the extracted point pairs proves the capabilities of the proposed matching algorithm on a variety of optical remote sensing images.

Description: An automatic method for road extraction from satellite imagery is presented. The core of the proposed method is locally excitatory globally inhibitory oscillator networks (LEGION). The road extraction task is decomposed into three stages. The first stage is image segmentation by LEGION. In the second stage, the medial axis of each segment is computed, and the medial axis points corresponding to narrow regions are selected. The third is the road grouping stage. Alignment-dependent connections between selected points are established, and LEGION is utilized to group well-aligned points, which represent the extracted roads. Due to the selective gating mechanism of LEGION, different roads in an image are grouped separately. Road extraction results on synthetic and real images are presented. A comparison with other methods shows that the proposed method produces very competitive extraction results.

Description: This paper focuses on assessing the effectiveness of applying orientation angle calibration to polarimetric synthetic aperture radar (PolSAR) data for soil moisture estimation. We employ Cloude-decomposition-based method to estimate the orientation angle because it can relate a scatter-distributed pixel to its major component of an equivalent “pure target,” use the Jet Propulsion Laboratory/Airborne Synthetic Aperture Radar L-band fully polarimetric data to validate the proposed method, and observe results in good agreement after orientation angle compensation is employed. Specifically, root mean square errors of measured radar backscattering coefficients $sigma_{hh}^{0}$ and $ sigma_{vv}^{0}$ and copolarization ratio versus advanced integral equation model predictions are reduced significantly from 1.95, 1.33, and 2.03 dB to 1.30, 1.15, and 1.43 dB, respectively. The compensated copolarized backscattering coefficients are also used as inputs to a novel inversion model to estimate the dielectric factor $R_{hh}$ and volumetric soil moisture $m_{v}$ . The results show that the estimation errors are reduced significantly from 0.075 to 0.054 and 0.056 to 0.041 for $R_{hh}$ and $m_{v}$ , respectively. This paper demonstrates the advantage of orientation angle calibration as a preprocessing for estimating bare soil moisture, particularly in agricultural areas, and the preponderance of fully PolSAR data on soil moisture estimation over dual and single polarizations.

Description: An approach for continuous daytime cloud classification system through satellite images is presented. The system is based on spectral ratio values as input features and a modified version of probabilistic neural network (PNN), named Quick PNN (QPNN), as a classifier. The use of spectral ratio values makes the system more efficient in detecting the minor changes in cloud spectral properties, leading to better classification capability. The modification to PNN consists of shrinking the hidden layer which is accomplished by performing K-means clustering on the training data of each class separately. Thus, for each class, instead of presenting all the training data samples in the hidden layer nodes, only the means of the resultant clusters are presented. The training data and the class labels are derived through the generation and interpretation of ratio images. The application of the approach to Meteosat-8 images has shown the separation of eight classes, including low clouds, middle clouds, high clouds, areas of high water vapor, sea surface, and land. The average accuracy of the system is 87.15% with a range of 84%–91% for the cloud and area of high water vapor classes, 93% for sea surface class, and 85% for land surface class. The computation time of the classification mode, including image ratioing and QPNN operations, is less than 1 min, which is good for continuous cloud classification and monitoring. The approach can be adapted to any multichannel satellite sensor only by using proper combination of ratio images.

Description: The rich information available in hyperspectral imagery not only poses significant opportunities but also makes big challenges for material classification. Discriminative features seem to be crucial for the system to achieve accurate and robust performance. In this paper, we propose a 3-D Gabor-wavelet-based approach for pixel-based hyperspectral imagery classification. A set of complex Gabor wavelets with different frequencies and orientations is first designed to extract signal variances in space, spectrum, and joint spatial/spectral domains. The magnitude of the response at each sampled location $(x, y)$ for spectral band $b$ contains rich information about the signal variances in the local region. Each pixel can be well represented by the rich information extracted by Gabor wavelets. A feature selection and fusion process has also been developed to reduce the redundancy among Gabor features and make the fused feature more discriminative. The proposed approach was fully tested on two real-world hyperspectral data sets, i.e., the widely used Indian Pine site and Kennedy Space Center. The results show that our method achieves as high as 96.04% and 95.36% accuracies, respectively, even when only few samples, i.e., 5% of the total samples per class, are labeled.

Description: In this paper, we investigate an issue related to the use of synthetic aperture radar imagery to detect ocean and lake waterlines. In a previous publication, the waterlines indicated by L- and P-band AIRSAR imagery of a tidal flat in South Korea were compared and found to be offset from one another by approximately 80 to 170 m. The authors postulated that the difference was due to depth-dependent dissipation or dispersion of surface waves and the subsequent modulation of the radar backscatter as described by the Bragg model. In this paper, we present an alternative explanation based on the growth of wind waves as a function of distance (i.e., fetch). This new explanation is more consistent with the environmental conditions, radar look geometry, and surface wave theory, while also explaining several finer-scale features observed in the imagery that are not addressed in the original publication. Our results indicate that the detected waterline position should be a sensitive function of radar frequency only under a restricted set of conditions, namely, when the body of water in question is cutoff from incoming swell and the only surface waves present are locally generated by a land breeze. While such conditions may occur relatively infrequently in the coastal ocean, they appear to be common when imaging the windward shores of inland lakes, as illustrated by additional AIRSAR imagery. The analysis also serves as a remote-sensing-based validation of the existing theory for wind-wave growth in a wavenumber regime not previously studied in the field.

Description: A main target of seismic data processing is to remove the surface waves and improve the quality of seismic records. Here, we propose a Co-Core Trace $(CCT)$ transform filtering based on the Trace transform from image processing and apply it to seismic surface wave attenuation. The $CCT$ transform is designed according to the distribution and propagation of the surface waves. In the $CCT$ transform domain, the energies of surface waves are significantly enhanced and could be filtered out with a relative threshold, while the reflection events are saved due to the conspicuous disparity. Experiments on both synthetic model and field data demonstrate that the proposed algorithm performs well both in surface wave attenuation and reflected signal preservation, besides presenting advantages over some conventional methods.

Description: A data fusion method for land cover (LC) classification is proposed that combines remote sensing data at a fine and a coarse spatial resolution. It is a two-step approach, based on the assumption that some of the LC classes can be merged into a more generalized LC class. Step one creates a generalized LC map, using only the information available at the fine spatial resolution. In the second step, a new classifier refines the generalized LC classes to create distinct subclasses of its parent class, using the generalized LC map as a mask. This classifier uses all image information (bands) available at both fine and coarse spatial resolutions. We followed a simple data fusion technique by stacking the individual image bands into a multidimensional vector. The advantage of the proposed approach is that the spatial detail of the generalized LC classes is retained in the final LC map. The method has been designed for operational LC mapping over large areas. Within this paper, it is shown that the proposed data fusion approach increased the robustness of forest-type mapping within Europe. Robustness is particularly important when creating continental LC maps at fine spatial resolution. These maps become more popular now that remote sensing data at fine resolution are easier to access.

Description: The purpose of this paper is twofold: 1) to develop a high-resolution sea ice motion tracking system at the geospatial mesoscale (1–100 $hbox{km}^{2}$ ) and 2) to propose an algorithm that measures motion at close proximity to discontinuous regions. Here, we present a motion tracking system that computes differential motion at 400 m resolution and validate the accuracy/precision of this system via four studies. The first study measures the accuracy against displacements measured from in situ Global Positioning System (GPS) buoys deployed at the Sea-ice Experiment: Dynamic Nature of the Arctic (SEDNA) and the Surface HEat Budget of the Arctic Ocean (SHEBA) experiments. The estimates are found to be statistically comparable with GPS, with an average error of 361.9 and 600.6 m for the experiments, respectively. The second study compares the estimated displacements to those measured by the RADARSAT Geophysical Processing System. A precision error of 75.7 m is found between the two motion tracking systems. The third study uses intensity warping of randomly sampled measurements to evaluate discontinuous motion tracking. A one-tailed Wilcoxon signed rank test is used to validate these measurements at $alpha = 0.01$ . Results from this paper prove that anisotropic smoothing produces significantly smaller errors at discontinuous locations ( $W = 4240$ and $p 〈 0.001$ ) over conventional isotropic smoothing. The fourth study compares displacements measured by anisotropic smoothing against manual measurements. This paper demonstrates an average reduction of the estimation error by 50 m with the use of anisotropic smoothing over the conventional isotropic smoothing.

Description: A method that is used to generate synthetic interferograms of the atmospheric phase delay temporal changes is presented. The Weather Research and Forecasting Model is used to forecast the spatial distribution of the main atmospheric parameters at the acquisition times of synthetic aperture radar (SAR) images. The method is applied to mitigate atmospheric artifacts in SAR interferograms. The Lisbon Region and the Pico and Faial Islands in the Azores archipelago are chosen as case studies. They are characterized by a different temporal behavior of atmospheric phase delay properties. Results are assessed by means of a statistical analysis.

Description: A system for automatic extraction of various feature layers and recognition of the text content of scanned topographic maps is presented here. Linear features which are often intersecting with the text are first extracted using a novel line representation method and a set of directional morphological operations. Other graphical objects are then removed in several stages to obtain a text-only image. A custom defect model is subsequently used to create an artificial training set for a Hidden Markov Model-based character recognition engine. Finally, the recovered text is recognized using this multifont segmentation-free optical character recognition (OCR). Extensive testing is conducted to assess the performance of different stages of the proposed system. Furthermore, our custom OCR is shown to achieve a 94% recognition rate for the extracted text, thereby outperforming a commercial OCR used as a benchmark.

Description: Shadows in remotely sensed images create difficulties in many applications; thus, they should be effectively detected prior to further processing. This paper presents a novel semiautomatic shadow detection method that meets the requirements of both high accuracy and wide practicability in remote sensing applications. The proposed method uses only the properties derived from the shadow samples to dynamically generate a feature space and calculate decision parameters; then, it employs a series of transformations to separate shadow and nonshadow regions. The proposed method can detect shadows from both color and gray images. If the chromatic properties of color images do not agree with the defined rules through the shadow samples, then the shadow detection process will automatically reduce to the process for gray images. As the shadow samples are manually selected from the input image by the user, the derived parameters conform well to the characteristics of the input image. Experiments and comparisons indicate that the proposed self-adaptive feature selection algorithm is accurate, effective, and widely applicable to shadow detection in practical applications.

Description: Markov random field (MRF) models are powerful tools to model image characteristics accurately and have been successfully applied to a large number of image processing applications. This paper investigates the problem of fusion of remote sensing images, e.g., multispectral image fusion, based on MRF models and incorporates the contextual constraints via MRF models into the fusion model. Fusion algorithms under the maximum a posteriori criterion are developed to search for solutions. Our algorithm is applicable to both multiscale decomposition (MD)-based image fusion and non-MD-based image fusion. Experimental results are provided to demonstrate the improvement of fusion performance by our algorithms.

Description: Ultrawideband (UWB) pulse radar with high range resolution is suitable for near-field sensing. Applications of UWB pulse radar include human body identification in blurry vision for security or rescue purposes and accurate spatial measurements for industrial products such as a reflector antenna. The synthetic aperture radar is still promising for these applications because it creates an accurate image even for near-field targets in free space. However, for complex-shaped or multiple objects, this algorithm suffers from increased shadow region because it employs only a single-scattered signal for imaging. To resolve this difficulty, this paper proposes a novel imaging algorithm based on aperture synthesis for double-scattered signals. In general, double-scattered waves include independent information on target points, which are not obtained by a single-scattered wave. Based on this principle, the proposed method effectively synthesizes the double-scattered signals and enhances the reconstructible range of a target shape, part of which becomes a shadow in the former approach. In order to enhance accuracy, a false image suppression approach based on the Fresnel zone theory is also incorporated in the proposed method. The results from numerical simulations and an experiment verify that our method significantly enhances the visible range of target surfaces without either a priori knowledge of target shapes or preliminary observation of their surroundings.

Description: We present a new method for the visualization of spectral images, based on a selection of three relevant spectral channels to build a red–green–blue composite. Band selection is achieved by means of information measures at the first, second, and third orders. Irrelevant channels are preliminarily removed by means of a center-surround entropy comparison. A visualization-oriented spectrum segmentation based on the use of color matching functions allows for computational ease and adjustment of the natural rendering. Results from the proposed method are presented and objectively compared to four other dimensionality reduction techniques in terms of naturalness and informative content.

Description: Image matching and water-body-detection methodologies are essential parts of generating good-quality digital elevation model (DEM) data. It is one of the very important results for image matching where 1-D searching in the along-track direction is sufficient to find the maximum correlation point if reconstructed unprocessed Advanced Spaceborne Thermal Emission and Reflection data (Level-1A data) are used as the source data for DEM products. This important situation is obtained from the general formulation of how to make 1-D searching possible. The image matching quality is evaluated for this 1-D searching method. An image correlation kernel size of 5 by 5 is recommended as the most suitable selection for better horizontal resolution with a slight sacrifice of the image matching error. The satellite pointing fluctuation effect on image matching is also evaluated, leading to the conclusion that it does not seriously affect DEM quality. The water-body-detection technique is another core of DEM generation. The low image correlation coefficient, the low reflectance of water in the near-infrared band 3N, and other spectral characteristics of water were used to identify surface water bodies. In addition, water-body size and the standard deviation of the water-body perimeter elevation are limited for consistent detection without misidentification. As a result, the minimum size of a detectable water body is $0.2 hbox{km}^{2}$ .

Description: Both a geometrical correction and a residual error correction schemes are proposed to improve the positioning accuracy of a three-frequency differential global positioning system (DGPS) on the order of centimeters, using 1 s of received data, and the baseline can be up to 120 km. An ad hoc network of floating dropsondes bearing DGPS receivers is proposed to monitor the progress of a typhoon in real time. An empirical typhoon model is adopted to simulate the deployment of such a network in typhoon Morakot and hurricane Katrina to verify its feasibility.

Description: While originally designed only for wind measurement, the QuikSCAT scatterometer is capable of making wind and rain estimates over the ocean. Three separate estimators are used, a wind-only estimator, a rain-only estimator, and a simultaneous wind–rain estimator. No one of the estimators is suitable under all wind and rain conditions. We therefore propose a Bayesian estimator selection technique whereby the appropriate estimator can be selected from the estimates themselves. This paper introduces the Bayes estimator selection technique and discusses its application to QuikSCAT wind and rain estimation for conventional (25-km) resolution products. Results indicate that using Bayes estimator selection can improve both the bias and mean-squared error of wind estimates in both raining and nonraining conditions, as well as provide an improved rain flag.

Description: We develop a novel remote sensing technique for the observation of waves on the ocean surface. Our method infers the 3-D waveform and radiance of oceanic sea states via a variational stereo imagery formulation. In this setting, the shape and radiance of the wave surface are given by minimizers of a composite energy functional that combines a photometric matching term along with regularization terms involving the smoothness of the unknowns. The desired ocean surface shape and radiance are the solution of a system of coupled partial differential equations derived from the optimality conditions of the energy functional. The proposed method is naturally extended to study the spatiotemporal dynamics of ocean waves and applied to three sets of stereo video data. Statistical and spectral analysis are carried out. Our results provide evidence that the observed omnidirectional wavenumber spectrum $S(k)$ decays as $k^{-2.5}$ is in agreement with Zakharov's theory (1999). Furthermore, the 3-D spectrum of the reconstructed wave surface is exploited to estimate wave dispersion and currents.

Description: Structured canopies can show pronounced directional effects which influence land surface temperature (LST) estimates from thermal infrared satellite data. The effects depend on illumination and viewing geometries, because changes in these two geometries effectively cause the sensor to “see” different fractions of the canopy and the “background” surface (bare soil or low vegetation). Furthermore, parts of these two components will be in shadow, depending on the specific geometry of the canopy and its structure. This paper investigates these directional effects for a specific savanna site in West Africa and extends the findings to areas with denser tree crown cover. This is achieved by modeling the combined effects of the structured surface with a geometric optics model. The model assumes that the surface consists of four components: shaded and sunlit tree canopies and shaded and sunlit backgrounds. The brightness temperatures of these four surface components are provided by in situ measurements at the validation site, and emissivities are taken from the Land Surface Analysis Satellite Applications Facility (LSA-SAF) project. The LST modeling is performed for the geometry of the geostationary Meteosat Second Generation and for nadir geometry. Analyses of the temperature differences between the LST estimates for the two geometries show that, in many cases, the directional effects exceed 1 $^{circ}hbox{C}$ within a day and that the timing and the sign of the effects change with season. Directional errors due to structured canopies are currently not considered in error estimates of operationally available LST products, e.g., the LSA-SAF LST product or the Moderate Resolution Imaging Spectroradiometer (MODIS) LST/emissivity products.

Description: Biomass burning releases a significant amount of trace gases and aerosols into the atmosphere and affects climate change, carbon cycle, and air quality. Accurate estimates of emissions depend strongly on the calculations of burned areas. Here, we present an algorithm that is used to derive burned areas by blending active fire observations from multiple satellites which are provided in the Hazard Mapping System (HMS). The HMS consolidates automated fire detections from Geostationary Operational Environmental Satellite (GOES) Imager, Advanced Very High Resolution Radiometer (AVHRR), and MODerate resolution Imaging Spectroradiometer (MODIS). Our goals are to derive burned areas in each GOES fire pixel across contiguous United States (CONUS) from 2004 to 2007 and to validate the estimates using Landsat Thematic Mapper/Enhanced Thematic Mapper plus (TM/ETM+) burn scars and National Fire Inventory data. The results show that annual fire events burn 0.4% $(3.4times break 10^{4} hbox{km}^{2})$ of total land across CONUS, which consists of 0.49% of total forests, 0.64% of savannas, 0.68% of shrublands, 0.40% of grasslands, and 0.30% of croplands. The large burned areas are dominantly distributed in the western CONUS, followed by the states in the southeast region and along the Mississippi Valley. Extensive validation shows that MODIS+AVHRR+GOES instruments greatly improve the determination of fire duration and fire detection rate compared to single instrument detections. The detection rate of small fire events $(〈 10 hbox{km}^{2})$ from multiple instruments is 24% and 36% higher than that from MODIS and GOES, respectively. The error in the burned-area estimate is less than 30% in individual ecosystems, and it decreases exponentially with the increase of burn scar size. Overall, the accuracy of total burned area across CONUS is 98.9% w- - hen compared to TM/ETM+-based burn scars and 83% when compared to national inventory data.

Description: An adaptive regularization iterative inversion of array multicomponent induction well logging datum is established to simultaneously reconstruct the horizontal and vertical conductivities of both invasion zone and origin formation, invasion radius, and the interface depth of each bed in the horizontally stratified inhomogeneous transversally isotropic (TI) formation. Applying numerical mode matching method, we can obtain a much compact semianalytic expression of the electromagnetic tensor Green's functions by magnetic current source in the inhomogeneous TI formation. Then, using the perturbation principles, an efficient computation of Fréchet derivatives of the multicomponent induction logging response is set up with respect to all the model parameters. After that, the combination of Morozev's discrepancy principle with Cholesky's decomposition is applied to adaptively select regularization factor during inversion so that stabilization of inversion solution is assured as well as realization of best fit of the input data with the modeling logs. Finally, the numerical tests validate the algorithm.

Description: Indoor target detection and imaging technologies hold great interest for security surveillance systems. The ultrawideband (UWB) radar is promising because it can complement conventional camera-based systems. However, conventional UWB radar imaging systems are costly and impractical because they require large antenna arrays for acceptable resolution. This paper proposes a low-cost UWB radar imaging method using the motion of a target. The method employs five antennas for estimating the motion of a target, including its rotation, to obtain an image. Previous work deals only with a target in translation without rotation, which makes the method difficult to apply in practice. The proposed method, an extension of such previous methods, obtains an accurate image for an elliptical or distorted nonelliptical target with arbitrary translation and rotation. Numerical simulation and experimental results show that the proposed method is capable of accurately estimating motions and shapes.

Description: Cloud properties were retrieved by applying the Clouds and Earth's Radiant Energy System (CERES) project Edition-2 algorithms to 3.5 years of Tropical Rainfall Measuring Mission Visible and Infrared Scanner data and 5.5 and 8 years of MODerate Resolution Imaging Spectroradiometer (MODIS) data from Aqua and Terra, respectively. The cloud products are consistent quantitatively from all three imagers; the greatest discrepancies occur over ice-covered surfaces. The retrieved cloud cover ( $sim$ 59%) is divided equally between liquid and ice clouds. Global mean cloud effective heights, optical depth, effective particle sizes, and water paths are 2.5 km, 9.9, 12.9 $muhbox{m}$ , and 80 $hbox{g}cdot hbox{m}^{-2}$ , respectively, for liquid clouds and 8.3 km, 12.7, 52.2 $muhbox{m}$ , and 230 $hbox{g}cdot hbox{m}^{-2}$ for ice clouds. Cloud droplet effective radius is greater over ocean than land and has a pronounced seasonal cycle over southern oceans. Comparisons with independent measurements from surface sites, the Ice Cloud and Land Elevation Satellite, and the Aqua Advanced Microwave Scanning Radiometer–Earth Observing System are used to evaluate the results. The mean CERES and MODIS Atmosphere Science Team cloud properties have many similarities but exhibit large discrepancies in certain parameters due to differences in the algorithms and the number of unretrieved cloud pixels. Problem areas in the CERES algorithms are identified and discussed.

Description: The analysis of radar time series with persistent scatterer techniques usually relies on temporal unwrapping, because phase behavior can be often described by simple models. However, one of the major limitations of temporal algorithms is that they do not take advantage of spatially correlated information. Here, we focus on two types of information that can be spatially estimated, namely, observation precision and the probability density function of the model parameters. We introduce them in phase unwrapping using Bayesian theory. We test the proposed method using simulated data. We also apply them to a small area in the southern Netherlands and compare with conventional temporal unwrapping methods.

Description: The Spectral Profiler (SP) is a visible–near infrared spectrometer on board the Japanese Selenological and Engineering Explorer, which was launched in 2007 and observed the Moon until June 2009. The SP consists of two gratings and three linear-array detectors: VIS (0.5–1.0 $muhbox{m}$ ), NIR 1 (0.9–1.7 $muhbox{m}$ ), and NIR 2 (1.7–2.6 $muhbox{m}$ ). In this paper, we characterize the radiometric and spectral properties of VIS and NIR 1 using in-flight observational data as well as preflight data derived in laboratory experiments using a calibrated integrating sphere. We also proposed new methods for radiometric calibration, specifically methods for nonlinearity correction, wavelength correction, and the correction of the radiometric calibration coefficients affected by the water vapor. After all the corrections, including the photometric correction, we obtained the reflectance spectra for the lunar surface. Finally, we examined the stability of the SP using the SP data near the Apollo 16 landing site observed at four different times. The difference in reflectance among these four observations was less than $simpm$ 1% for most of the bands, suggesting that the degradation of the SP is not significant over the mission period.

Description: This paper introduces new polarimetric algorithms for generating 3-D images and estimating scattering mechanisms from polarimetric multibaseline (MB) interferometric synthetic aperture radar (SAR) measurements. First, an MB interferometric SAR signal model is generalized to the fully polarimetric configuration, establishing the notion of polarimetric reflectivity. Subsequently, polarimetric beamforming, Capon, and MUSIC methods that determine optimal polarization combinations for height estimation are developed. These new techniques allow for extracting the height of reflectors, the associated scattering mechanisms, and the polarimetric (pseudo)reflectivities. By means of polarimetric dual-baseline interferometric SAR observations of an urban environment, the performance of the conceived algorithms is examined in detail. Producing 3-D images of a building layover, the quality of the approaches is compared in terms of refined resolution and lowered side lobes. Furthermore, the scattering processes occurring in urban scenes are investigated thoroughly by analyzing the optimal reflection types. The algorithms are validated using dual-baseline polarimetric SAR interferometric data at L-band acquired by German Aerospace Center's experimental SAR system over Dresden city.

Description: Backscatter from an aggregate of inhomogeneities combine to form an apparent surface reflection particularly in relation to interferometric synthetic aperture radar. The depth $z_{phi}$ of this reflection can reside well below the true surface when the transmissivity at the interface between air and the aggregate is high. Snow and ice provide good examples, for which we calculate $z_{phi}$ with different accumulation history and physical properties using a 0.5–3.0-GHz ground-penetrating radar. We acquired our data along transects in Antarctica and Svalbard. We find values of $z_{phi} > 40 hbox{m}$ in low-absorbing Antarctic firn and $approx$ 10 meters in glaciers and ice shelves where melt-freeze cycles and lateral mass movement lead to an electrically more heterogeneous snow and ice column. The heterogeneity reduces dielectric contrast more rapidly with depth. Thus, $z_{phi}$ is found at shallower depth, but still resides several meters beneath the snow surface.

Description: We develop an inversion technique to process overlapping data that arise from closely spaced targets. In contrast to a usual single-object inversion model, a multiobject problem is more challenging because of the increased number of parameters to be found and because of the additional nonlinearity and nonuniqueness. Our solution strategy is to break down the full problem into a sequence of smaller problems so that optimization is conducted in a lower dimensional model space. In the numerical implementation, a set of nonlinear model parameters, e.g., the locations of the underlying sources, is sought while the set of linear model parameters, i.e., their polarization tensors, are updated accordingly in a nested manner. This is an explicit separable nonlinear optimization technique that we cast. We employ a joint diagonalization to find an average principal direction among multiple magnetic polarizability tensors. Since the principal directions are more sensitive to the inaccuracies in the estimated polarization tensor, we suggest a subsequent procedure to optimize the two sets of parameters: orientation and principal polarizations of objects. For initialization, we propose a selected multistart nonlinear algorithm for source localizations that paves an efficient way to find a good initial guess of model parameters and makes the nonlinear inversion effectively automated. We report the new applications of the technique to the test-stand and field data acquired with next-generation sensor systems of the TEMTADS and MetalMapper and study the issue of the spatial resolution of overlapping anomalies through inversions and using the metric defined as the total uncertainty of the polarizabilities.

Description: The SMOS mission is a European Space Agency project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. This paper is concerned with the contamination of the data collected by SMOS by radio-frequency interferences (RFIs) which degrade the performance of the mission. RFI events are evidenced on both reference radiometer measurements and interferometric ones. It is explained why well-known standard RFI detection methods cannot be used. A specific method for the SMOS mission is presented and illustrated with data acquired during the commissioning phase.

Description: Microcystis aentginosa (MA), which is one kind of cyanobacteria, is the primary algal species in Taihu Lake. The MA bloom has a significantly negative effect on the human health and water environment ecosystem. The monitoring and prediction of MA bloom become more and more critical for the security of drinking water source and environment in the Taihu Lake area. In this paper, the percentage of MA was estimated from remote-sensing reflectance via a novel spectral shape genetic optimization algorithm. This algorithm focuses on the shape of remote-sensing reflectance, and it can remove the influence of the amplitude of remote-sensing reflectance from the retrieval result. The accuracy of this optimization algorithm is acceptable according to both simulated and in situ data. The percentage of mean square root (RMSP) of the percentage of the phytoplankton absorption coefficient to the total absorption coefficient at 440 nm [ $a_{rm r}$ (440 nm)] between the retrieved and the simulated is 18.39%. The RMSP of the total absorption coefficient at 440 nm [ $a$ (440 nm)] between the retrieved and the simulated is 3.65%. The RMSP of the percentage of MA $[S_{rm f}]$ between the retrieved and the simulated is 13.60%. The RMSP of the colored dissolved organic matter (CDOM) absorption coefficient slope $[S]$ between the retrieved and the simulated is 5.89%. The RMSP of the particle backscatter coefficient slope $[Y]$ between the retrieved and the simulated is 30.46%. In Taihu Lake, the RMSPs of $a_{rm r}$ (440 nm), $a$ (440- - nm), $S_{rm f}$ , and $S$ between the retrieved and the measured are 36.59%, 35.70%, 19.25%, and 16.80%, respectively. The retrieved percentage of MA $(S_{rm f})$ and Scenedesmus obliquus  $(1 - S_{rm f})$ by this model from August 2006, October 2006, to November 2008 indicates the variation trend of algal species in different seasons. This trend accords with the results from previous studies and observations. This paper extends and advances the previous retrieval methods and confirms that the genetic optimization algorithm can be used to retrieve the information of water constituents in the high turbid and eutrophic inland water mass.

Description: The Microwave Radiation Imager (MWRI) on board the FengYun-3A/B satellites observes the Earth atmosphere at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with each having dual polarization. Its calibration system is uniquely designed with a main reflector viewing both cold and hot calibration targets. Two quasi-optical reflectors are used to reflect the radiation from the hot load and cold space to the main reflector. In the MWRI calibration process, a radiation loss in the beam transmission path must be taken into account. The loss factor in the hot load transmission path is derived using the antenna pattern data measured on ground and satellite data observing over the Amazon forest where the scene temperature is steady and close to the hot load. The instrument nonlinearity factors at different channels are also evaluated over a wide range of brightness temperatures and compared with the results from the ground vacuum test. After a cross-calibration with Windsat data, atmospheric products are derived from MWRI brightness temperatures with the accuracy similar to those from the legacy sensors (e.g., the Special Sensor Microwave/Imager).

Description: This paper focuses on the use of multispectral measurements to classify remotely sensed radiance and reflectance information into three tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst.), and birch (Betula pubescens Ehrh., Betula pendula Roth), using a Support Vector Machine (SVM) algorithm. The features used for the classifier are radiometric involving different viewing angles, but without textural information. At-sensor radiance (ASR) signals used here were obtained using a four-band Leica ADS40-SH52 airborne sensor. The experiments were carried out in a forest area at Hyytiälä, in southern Finland (61 $^{circ}$ 50 $^{prime} hbox{N}$ , 24 $^{circ}$ 20 $^{prime} hbox{E}$ ), which has been widely used for similar purposes, so that detailed tree-level information has been reported previously. The flight was carried out on August 23, 2008. ADS40 ASR measurements can be converted to ground reflectance signatures in two viewing directions using atmosphere and BRDF modeling implemented in Leica XPro 4.2 software. Taking into account the assumptions entailed in the radiometric model, the classification performance of the ground reflectance is evaluated only for the pixel values under sunlit conditions and is compared with the performance of the ASR data. The sunlit and shaded parts of the tree crown were extracted based on the use of LiDAR data for crown shape modeling. The classification results for the real multispectral measurements are compared with the earlier results obtained with simulated Leica ADS40 at-sensor radiance response values which were based on the ground-level high-resolution ground reflectance factor measurements using a single viewing - - direction. The simulated classification accuracy was 75–79% with the original four bands, while it was up to 85–88%, using the simulated fifth channel. It was found here that the classification accuracy using comparable real ADS40-SH52 four-band data and one viewing angle was 75–79% and increased to 78–82% with two viewing angles. The results show that the best-case classification accuracy with real data can reach 88% if trees are modeled as objects with sunlit and shaded areas, and multiple measurements are available for every tree. The results suggest that ground reflectance estimation with normalization of anisotropic reflectance behavior leads to similar classification performance to ASR data, but can in some cases improve the generalization properties of training data.

Description: From the theory of compressive sensing (CS), we know that the exact recovery of an unknown sparse signal can be achieved from limited measurements by solving a sparsity-constrained optimization problem. For inverse synthetic aperture radar (ISAR) imaging, the backscattering field of a target is usually composed of contributions by a very limited amount of strong scattering centers, the number of which is much smaller than that of pixels in the image plane. In this paper, a novel framework for ISAR imaging is proposed through sparse stepped-frequency waveforms (SSFWs). By using the framework, the measurements, only at some portions of frequency subbands, are used to reconstruct full-resolution images by exploiting sparsity. This waveform strategy greatly reduces the amount of data and acquisition time and improves the antijamming capability. A new algorithm, named the sparsity-driven High-Resolution Range Profile (HRRP) synthesizer, is presented in this paper to overcome the error phase due to motion usually degrading the HHRP synthesis. The sparsity-driven HRRP synthesizer is robust to noise. The main novelty of the proposed ISAR imaging framework is twofold: 1) dividing the motion compensation into three steps and therefore allowing for very accurate estimation and 2) both sparsity and signal-to-noise ratio are enhanced dramatically by coherent integrant in cross-range before performing HRRP synthesis. Both simulated and real measured data are used to test the robustness of the ISAR imaging framework with SSFWs. Experimental results show that the framework is capable of precise reconstruction of ISAR images and effective suppression of both phase error and noise.