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: Several strategies have been proposed for operating doubly-fed induction generator (DFIG)-based wind turbine under unbalanced grid voltage conditions. This study focuses on those strategies in which the rotor-side power converter aims at eliminating the oscillations affecting the electromagnetic torque and the stator reactive power. Given the limited size of the DFIG’s power converters, and hence their tolerable current and voltage boundaries, this study analyzes which DFIG power generation capability is under unbalanced grid voltage, and therefrom derives rotor current and stator power controllable ranges. Besides, the feasibility regions of the DFIG are also deduced for different types of imbalance. Furthermore, and based on the outcome of previous analysis, a modified rotor current limiter, as also its equivalent stator power limiter, is proposed. In contrast to the conventional ones, the limiters proposed here take into account that imbalances may arise in the grid voltage. As a consequence, system’s overall performance is considerably enhanced under unbalanced grid voltage conditions. Finally, simulation results establish the validity of the treated issues.

Description: Wind turbine technology has evolved into a unique technical identity with potential to contribute significantly to the global energy mix powered by renewables. Wind energy, being a fluctuating resource, requires tight control management that addresses stability issues for it to be integrable with the grid system. Difficulty in controller construction for wind energy conversion systems (WECSs) is reinforced by sensitivity to numerical complexity, fast parameter variations due to wind stochasticity, tight performance requirements, as well as the presence of flexible modes that limit the control bandwidth. Proposed herein is a pitch control scheme and a model-based ${cal H}_{infty }$ synthesis controller that yields a multivariable control law governing operation of the power electronic converter for a megawatt-class WECS over the entire nominal operating trajectory. The ${cal H}_{infty }$ control objectives are cast as optimization programs with a unique cost function subject to linear matrix inequality constraints. Simulational analysis confirms the efficacy of the adopted technique: issues regarding uncertainties with respect to system modeling and possible adverse control due to interactions with highly turbulent winds are handled with precision, while significantly improving the quality of voltage and output power.

Description: A general dynamic hysteresis core loss model has been developed. Experimental data for different lamination thicknesses have been measured and analyzed at various frequencies. Based on the analysis, a dynamic hysteresis finite element core loss model is established and validated by experiments. The developed dynamic hysteresis model is simple and efficient, and has been shown to be very accurate compared with the experiments. Moreover, the model can calculate core losses based on the input parameters obtained from experimental measurements at only one single frequency in a thin lamination. The model, to our best knowledge, is the first one that is capable of calculating core losses for different thicknesses of materials and different operating frequencies, without a massive experimental database. In addition, only the eddy current and hysteresis models are necessary for this calculation but with the important addition of the variation of the flux density within the lamination.

Description: Conventional active islanding detection methods (IDMs) are designed for the inverter-based distributed generation systems (DGSs) with current control interface. Such strategies can hardly be extended to the DGS with power control interface because the power control loop can affect the IDMs by enlarging the nondetection zones (NDZs). This paper presents an active IDM based on negative-sequence power injections for the DGS with power control interface. Combining with the IDM, the power control of the DGS is achieved with two control loops. One is the positive-sequence power loop that satisfies the conventional power control requirements. The other is the negative-sequence power/current variation loop for the islanding detection. The positive and negative sequences are separated by a simple strategy based on an all digital phase-locked loop. Due to the differences between the grid impedance and the local load impedance, the percentage of the voltage imbalance (VI) at the point of common coupling is utilized to indicate the islanding operation. For the grid-connected DGS, the VI is dominated by the grid voltage, which is a constant. If the DGS is disconnected from the grid, the VI is determined by the injected negative-sequence power/current. The NDZs of the presented scheme with different system configurations, such as grid impedances, load quality factors, etc., are also analyzed in this paper. By injecting the negative-sequence power/current periodically, the NDZs resulting from the imbalance of the grid voltages or the local loads are further mitigated. For multi-DGSs, the IDM is still effective if combined with the conventional under/overfrequency-protection strategy. The simulation and experimental results verify the effectiveness of the IDM.

Description: This paper presents an analysis of the cost of utilizing battery electric vehicle (BEV) batteries as energy storage in power grids [also known as vehicle-to-grid (V2G)] associated with lessening battery cycle life due to more frequent charging and discharging activities and utilization in elevated ambient temperature. Comparison is made between V2G in the U.K., where annual electricity peak demand is reached in winter, and in China, where peak demand is reached in summer due to the air conditioning load. This paper presents mathematical correlations between charging–discharging, ambient temperature, depth of discharge (DoD), and the degradation of electric vehicle batteries based on manufacturer's data. Simulation studies were carried out for V2G in both the U.K. and China. Numerical results show that ambient temperature and DoD of a BEV battery play a crucial role in the cost of battery wear. Lead-acid and NiMH battery powered BEVs are not cost effective in V2G use due to the present electricity tariff. Under the present electricity tariff structure, no vehicles would be cost effective for the peak power sources in China. However, lithium-ion battery powered BEVs are cost effective in the U.K. due to a much longer cycle life.

Description: In this paper, an effective new approach for estimating the operating temperature of a photovoltaic (PV) module by using the simple diode model is presented. The developed method is simple and does not need any additional hardware. The proposed approach uses an analytical formula to derive the temperature from the maximum power point voltage and current. Since the temperature estimation (TE) is based on the model of the PV module, at first the model is explained. In the model, effects of the temperature coefficients are considered. Moreover, a new approach to find all parameters of a PV module model is described. Unlike other methods, the parameters’ extraction method that is proposed here only needs manufacturer data from the datasheet and does not need any additional information. Effectiveness of the new TE procedure is investigated through some conducted simulations in MATLAB/Simulink environment and its validity is verified by experiment on a REC-AE220 solar module.

Description: The most important reference quantities for monitoring and controlling transient stability in real time are the rotor angle and speed of the synchronous generators. If these quantities can be estimated with sufficient accuracy, they can be used in global and local control methods. In the classic state estimation methods, such as the extended Kalman filter (EKF) technique, the linear approximations of the system at a given moment in time may introduce errors in the states. In order to overcome the drawbacks of the EKF, the authors of this paper have applied the unscented Kalman filter (UKF) to estimating and predicting the states of a synchronous machine, including rotor angle and rotor speed, using phasor measurement unit (PMU) quantities. The UKF algorithm propagates the pdf of a random variable in a simple and effective way and is accurate up to the second order in estimating the mean and covariance. The overall impression is that the performance of the UKF is better than the EKF in terms of robustness, speed of convergence, and also different levels of noise. Simulation results including saturation effects and grid faults show the accuracy and efficiency of the UKF method in state estimation of the system, especially at higher noise ratios.

Description: Over the last decade, tremendous gains, leading to near-capacity achieving performance, have been shown for a variety of communication systems through the application of the turbo principle, i.e., the exchange of extrinsic information between constituent algorithms for tasks such as channel decoding, equalization, and multiple-input-multiple-output (MIMO) detection. In this paper, we study the practical application of such an iterative detection and decoding (IDD) framework to underwater acoustic communications. We explore complexity and performance tradeoffs of a variety of turbo equalization (TEQ)-based receiver architectures. First, we elaborate on two popular but suboptimal turbo equalization techniques: a channel-estimate-based minimum mean-square error TEQ (CE-based MMSE-TEQ) and a direct-adaptive TEQ (DA-TEQ). We study the behavior of both TEQ approaches in the presence of channel estimation errors and adaptive filter adjustment errors. We confirm that after a sufficient number of iterations, the performance gap between these two TEQ algorithms becomes small. Next, we demonstrate that an underwater receiver architecture built upon the least mean squares (LMS) DA-TEQ technique can leverage and dramatically improve the performance of the conventional implementation based on the decision-feedback equalizer at a feasible complexity. To maintain performance gains over time-varying channels, the slow convergence speed of the LMS algorithm has been improved via two methods: 1) repeating the weight update for the same set of data with decreasing step size and 2) reducing the dimensionality of the equalizer by capturing sparse channel structure. This receiver architecture was used to process collected data from the SPACE 08 experiment (Martha's Vineyard, MA). Receiver performance for different modulation orders, channel codes, and hydrophone configurations is examined at a variety of distance, up to 1 km from the transmitters. Experimental results show great promise - - for this approach, as data rates in excess of 15 kb/s could readily be achieved without error.

Description: In this paper, we introduce the addition of an iterative, successive interference cancellation (SIC) process to improve on a multiuser, single-input-multiple-output (SIMO) communications receiver using passive time reversal as a space-time preprocessor. Time reversal has been shown to apply the spatial degrees of freedom to enhance the signal-to-noise ratio (SNR) and suppress interference for a target user. With the introduction of SIC, the receiver can remove the residual interference experienced by each user while preserving the SNR gain achieved by time-reversal preprocessing. The SIC process is a decision-directed approach for removing multiuser interference at the receiver and is similar to the decision-feedback equalizer (DFE) for intersymbol interference (ISI) channels. The interference experienced by each user is estimated at the receiver using previously decoded symbols from interfering users. This estimate is scaled and synchronized before subtraction from the target user's signal after time-reversal combining. Since SIC is applied before symbol decoding, symbol estimates are improved as the process is allowed to iterate until a stationary point is reached. Following time-reversal combining and SIC, a DFE can mitigate any remaining ISI before symbol decisions are made. Data collected from two Focused Acoustic Fields experiments (FAF-05 and FAF-06) are used to demonstrate the performance of the proposed interference cancellation scheme. During the FAF-05 experiment, three users transmitted 16-quadrature amplitude modulation (QAM) symbols simultaneously over the 3-4-kHz frequency band to a 20-element receiving array deployed in 120-m-deep water at a range of 20 km. The FAF-06 experiment included the simultaneous transmissions of 8-QAM symbols from two users over the 9-21-kHz band to a 16-element receiving array in 92-m-deep water at a range of 2.2 km. For both of the examples, SIC is shown to improve the output SNR in the presence of strong interference ove- - r time-reversal processing alone. This translates to a significant bit error rate (BER) reduction from 1.53 × 10 -2 to 8.80 × 10 -4 for the FAF-05 data and from 1.77 × 10 -3 to error-free decoding for the FAF-06 data.

Description: Underwater acoustic (UWA) channels exhibit time-varying fading statistics, thus a coded modulation scheme optimally designed for a specific model (e.g., Rayleigh fading) will perform poorly when the channel statistics change. Exploiting diversity via coded modulation is a robust approach to improve the reliability of the acoustic link in a variety of channel conditions. Two coded modulation schemes drawn from the terrestrial radio literature are compared in terms of their bit error rate (BER). The first scheme combines trellis coded modulation (TCM) based on an 8-phase-shift keying (8-PSK) signal set and symbol interleaving. The second scheme is based on bit-interleaved coded modulation (BICM), which includes a convolutional encoder, a bit interleaver, and a 16-quadrature-amplitude-modulation (16-QAM) signal set. These schemes, which are designed to have the same bit rate and decoding complexity, are tested under two scenarios. In the first scenario, a single-input-multiple-output (SIMO) system is implemented by means of orthogonal frequency-division multiplexing (OFDM) modulation. In the second scenario, a multiple-input-multiple-output (MIMO) system is implemented and each of the coded modulation scheme is coupled with a 3/4-rate space-time block code (STBC) before applying OFDM. Analyzing both simulated and experimental data, the following results, which also hold for terrestrial radio, are confirmed: coded modulation schemes emphasizing higher Hamming distance (such as BICM) yield a lower error rate when spatial diversity is very limited (first scenario). On the other hand, coded modulation schemes emphasizing higher free Euclidean distance (such as TCM) demonstrate a lower error rate when spatial diversity is sufficiently high (second scenario).

Description: A low-complexity receiver is proposed for high-frequency underwater acoustic multiple-input-multiple-output (MIMO) channels. The receiver uses time reversal combining followed by a single-channel decision feedback equalizer (DFE) to deal with the intersymbol interference. Periodical MIMO channel estimation is employed to track fast channel fluctuations. Both serial and parallel interference cancellation techniques are integrated with time reversal DFE to address the cochannel interference (CoI) in underwater MIMO systems. Two channel estimation algorithms are also implemented. It was demonstrated through the experiment conducted at Kauai, HI in 2005 that the proposed receiver can deal with the fast-fluctuating, dispersive MIMO channel at the carrier frequency of 37.5 kHz. Parallel interference cancellation combined with matching pursuit channel estimation was shown to provide significant performance improvements, indicating the receiver algorithm can effectively suppress the CoI. Four streams of binary phase-shift keying (BPSK) sequences at an aggregate rate of 16 kb/s and quadrature phase-shift keying (QPSK) sequences at a rate of 32 kb/s were demodulated at low bit error rates. These data rates corresponded to bandwidth efficiencies of 2.29 b/s/Hz or 4.57 b/s/Hz in a dynamic underwater environment, where the source and the receiver were drifting at a 2-km range.

Description: In this paper, we derive bounds to the channel capacity of orthogonal frequency division multiplexing (OFDM) systems over the underwater (UW) acoustic fading channel as a function of the distance between the transmitter and the receiver. The upper bound is obtained under perfect channel state information (CSI) at the receiver. The lower bound is obtained assuming the input is drawn from phase-shift keying (PSK) constellation which results in non-Gaussian distribution of the output signal and no CSI. The reduction from the upper bound is due to limited mutual information that can be conveyed by PSK constellation and the linear minimum mean square prediction error. Our UW channel deviates from the wide sense stationary and uncorrelated scattering (WSSUS) model commonly used for small bandwidths. We incorporate frequency-dependent path loss due to the acoustic propagation into each arrival path between the transmitter and the receiver. This leads the UW channel to be modeled as a frequency-dependent doubly spread fading channel characterized by the wide sense stationary and correlated scattering (WSS-non-US) fading assumption. Both Rayleigh and Ricean fading assumptions are investigated in our model. Results from the model show a gap between the upper and lower bounds which depends not only on the ranges and shape of the scattering function of the UW channel but also on the distance between the transmitter and the receiver. Our model for the scattering function was suggested by Rescheduled Acoustic Communications Experiment (RACE08) experimental data, leading to a multilag autoregressive (AR- q ) model for the fading.

Description: At very high speeds, underwater bodies develop cavitation at the trailing edges of sharp corners or from contours where pressures are sufficiently low to allow the formation of cavities containing water vapor. At sufficiently high speeds, a properly designed cavitator at the nose of an underwater vehicle can induce the formation of a vaporous cavity that entirely envelops the vehicle. The injection of gas behind the cavitator can result in the creation of cavities of comparable size at lower vehicle velocity and drag than would be required for vapor cavities. The formation of the cavity results in a significant reduction in drag on the vehicle and so-called high-speed supercavitating vehicles (HSSVs) have been reported to operate at speeds in excess of 100 m/s. The first part of this paper presents a derivation of a model for the longitudinal or pitch-plane dynamics of an HSSV. The vehicle is characterized by its mass and moment of inertia relative to a reference frame fixed to the body. The cavitator is assumed to be a disk, with a scale parameter that can be adjusted to represent an acute cone having an opposite sign for its lift curve slope. The control surface lift curve is specified relative to the cavitator lift effectiveness. A force model for a planing afterbody is also presented. The planing force model is found to be a significant source of damping and depending on a number of vehicle characteristics, the longitudinal dynamics may be stable. This result is significantly different than the conclusions of a number of previously published works. The final section of the paper examines the longitudinal stability at equilibrium of a 170-mm diameter HSSV. Results of parametric studies show the variation of pole locations associated with the transfer function relating cavitator angle to body pitch rate. The varied parameters are length, speed, fin effectiveness, body density, and the load carried on the aft planing surface.

Description: The advantages of combining offshore wind and wave energy into a single farm include reduced hours of zero power output and reduced interhour variability. Both advantages facilitate grid integration of variable renewables. The power output profile of a combined farm with wind and wave is substantially different from a 100% offshore wind energy farm or a 100% wave energy farm. The different power output profile of combined farms with a higher frequency of hourly power output near the annual capacity factor potentially allows for a reduction in the required capacity of the offshore transmission system. The transmission capacity reduction is balanced by the curtailment of energy during the few hours a year that a combined farm generates at full power. An optimization of the transmission capacity for various generation mixes of wind and wave was investigated, and results show that the optimal transmission capacity for a 1000-MW combined farm is approximately 80 MW, or 8%, less than either a 100% wind or 100% wave energy farm.

Description: In this paper, the context of several self-propelled, short-length cables, embedded with passive sensors for environmental diagnostics and swimming efficiently in formation over long duration and in shallow water, is considered. The basic problem of this volumetric diagnostic-namely, the low-speed motion control of a short-length, neutrally buoyant cable-is examined. More specifically, the constant-rate, circular turning of a 7-m-long cable held taut in a shallow-water basin using a biorobotic propulsor that has multiple flapping fins at one end, the other end being tied to a mooring post, is examined via modeling and laboratory and basin experiments. A drag analysis is used to estimate the fastest steady turning rate achievable while holding the cable taut. An axial tension and position controller, as well as a depth controller, is developed and evaluated in a quiescent laboratory tank accounting for the cycle-averaged hydrodynamic characteristics of a rigid cylinder to which six flapping fins are attached, three at each end. A small test range of 100-m scale, containing seven floor-mounted hydrophones in a hexagonal layout, is built in a stillwater basin to track the motion of the propulsor, to which a pinger is attached. The estimated overall resolution of the acoustic tracking system is 5 cm; it is possible to detect the imprint of the environmental unsteadiness on the cable and propulsor assembly. In the basin experiment, a mean radius of turning of 8.91 m can be achieved within a standard of deviation of 0.27 m, and a uniform turn rate of 22 min for one full revolution can also be maintained, when the applied turning force is 10% of the cable tension. The basin experiment has verified the drag analysis. This paper explores the value of a flapping fin propulsor (which is inspired by large swimming animals) as an alternative to conventional rotational propulsors for the low-speed maneuvering of a short cable.

Description: Underwater forward-looking imaging sonar (FLS) is widely used on stationary and moving platforms to overcome underwater visibility problems. The SONAR images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. Speckle reduction filters are necessary to optimize the images' exploitation procedures. The results of speckle filters may vary from one sensor and one wavelength to another; therefore, no generic de-speckling algorithm exists. Several studies have been carried out on speckle noise suppression on sidescan sonar, but the problem of speckle noise suppression for FLS has not yet been covered. A comparison of the most used classical speckle suppression filters as well as advanced wavelet-based ones was carried out. The Frost filter was found to be the most adequate for FLS data, but also the most computationally complex and not suitable for real-time processing. Two novel architectures for real-time and low-power field-programmable gate array (FPGA) implementation of the Frost speckle filter for underwater imaging sonar are presented. The proposed architectures have superior performance and power efficiency compared to standard software implementation.

Description: Registering sonar images to correctly describe seafloors and explain wide geological or biological phenomena is often achieved manually requiring significant human resources. This paper proposes an automatic intensity-based registration algorithm that relies on the optimization of a new similarity measure (SM), within a multiresolution block matching framework. Indeed, several SMs have been evaluated and ranked on real sidescan sonar data to determine the most relevant intensity dependencies between images for matching purposes. Correlation ratio (CR) and mutual information (MI) are then selected and because of their complementary behaviors, merged in a new SM (MI&CR), which performs better than CR or MI alone, to determine robust matching blocks between images. Thus, the proposed two-step registration algorithm uses MI&CR to match two sonar images: a single rigid translation globally matches the images, then a field of locally applied translations is computed for adjusting the final registration to remaining local distortions. Actual processing time can then be tuned according to the required registration accuracy. Due to a survey standard operating mode, only same-survey overlapping images are considered as candidates for matching. Moreover, building mosaics from registered images assumes a flat sea bottom as no global elevation information is provided by sidescan sonar images.

Description: There is significant interest in harvesting ocean energy for powering the autonomous vehicles that can conduct surveillance for long durations. In this paper, we analyze the applicability of solar cells as a power source for medusa-inspired biomimetic vehicles. Since these vehicles will be operating under ocean waters and may need to dive at various depths, a systematic investigation was conducted to determine the variation of output power as a function of depth and salinity levels. We modeled solar energy harvested by flexible amorphous solar cell coated jellyfish vehicles by considering the variables bell diameter, turbidity, depth, and fineness ratio. Low fineness ratio shapes were found to be better for solar energy powered vehicles. Study of three representative species, Aurelia aurita (AA), Mastigias sp., and Cyanea capillata indicates that harvested power was proportional to bell diameter. Optimum power can be harvested by tilting the vehicle axis to face refracted sunrays. Depending on a swimming pattern, power harvested in charging mode and in propulsion mode could vary significantly. The model indicates that, under some circumstances, amorphous silicon solar cell may be a cost-effective way to power autonomous underwater vehicles (AUVs) operating in shallow-water conditions with large lateral travel distances.

Description: In this paper, an image processing system for estimating 3-D particle distributions from stereo light scatter images is described. The system incorporates measured, three-component velocity data to mitigate particle blur associated with instrument motion. An iterative background estimation algorithm yields a local threshold operator that dramatically reduces bias in particle counts over the full image field. Algorithms are tested on simulated particle distributions and data from an open-ocean profile collected near the Santa Barbara Channel Islands, CA. They yield over a 50% reduction in root-mean-squared error in particle size estimates, and a 30% reduction in the magnitude of the motion blur point spread function. In situ particle distributions are estimated and compared to several models. It is demonstrated that quantitative, 3-D particle distributions can be accurately estimated from these data for particles with diameter larger than 4 pixels (0.8 mm).

Description: In this paper, an optimized simulated annealing algorithm is proposed for thinning and weighting large planar arrays in both far-field and near-field of underwater 3-D sonar imaging systems. This optimized algorithm is designed for the large planar array with a fixed sidelobe peak and a fixed current taper ratio under a narrowband excitation. It applies the approximation for time delay in the near-field beam pattern, and extends the simulated annealing algorithm to both far-field and near-field by defining a new “energy” function. One example of large planar array was used to evaluate the accuracy and efficiency of the optimized method.

Description: The first- and second-order monostatic cross sections of the ocean surface in the context of high-frequency ground wave radar operation are derived for a dipole source with a linear frequency-modulated continuous waveform. The Fourier coefficients of the rough ocean surface are described as zero-mean Gaussian random variables. The electric field equations for the reception of vertically polarized radiation scattered from the ocean surface are obtained. The range spectra are calculated by Fourier transforming the received signal within the sweep interval. Fourier transformation of the autocorrelations of the electric fields received from the time-varying surface gives the Doppler spectra (power spectral densities). The cross sections are obtained by direct comparison of the spectral results with the monostatic radar range equation.

Description: Underwater explosions with energy yields equivalent to less than 40 kg of TNT are detected at hydrophones in the deep-sound channel at distances up to 16 000 km. The arrival times and azimuths of the signals are compared with values based on known explosion locations and times. Arrival-time mismatches are of the order of 10 s. Azimuth mismatches are less than half a degree. Signal arrival times and azimuths are input to a seismological global association algorithm and the resulting estimates of explosion times and locations are compared with actual values. Estimated locations are shown to lie within tens of kilometers of the actual explosions and estimated explosion times to be correct to within tens of seconds. All location estimates have spatial error ellipses that contain the location of the relevant explosion.

Description: In this paper, the application of track-before-detect (TkBD) processing against airborne radar surveillance data is examined. The sensitivity of TkBD processing to the choice of clutter model was examined through processing of real and simulated data containing noncoherent radar echo amplitude returns of a small maritime target in sea clutter. The utilization of K - and KA -distributed clutter models in place of the simpler Rayleigh distribution was examined through analysis of simulated and real data. Significant shortcomings were identified. An alternate empirical approach, clutter event based characterization, is proposed for characterizing and simulating sea clutter based on the identification and grouping of so-called “clutter events”.

Description: Adaptive beamforming is often used in passive sonar, e.g., to improve the detectability of weak sources. The recently proposed robust Capon beamformer (RCB) exploits array steering vector uncertainty sets, eliminating the need for the ad hoc parameter choices often required when implementing robust adaptive beamforming algorithms. Here, we evaluate the performance of the RCB using experimental and simulated underwater acoustics data.

Description: This paper discusses a simple problem in detection theory using a model with a simple discretized space. This allows the probability problem to be analyzed algebraically to demonstrate that separate targets, independent before entering the detection space, do not have probabilistically independent detection probability distributions. Several variants of the basic case are discussed.

Description: This paper presents a novel approach to synthesize realistic environment for ocean-acoustic parametric studies. In its current form, this methodology applies to internal waves and tides. Empirical orthogonal function (EOF) decomposition is applied to a temporal series of temperature profiles. It can be observed that the first two time-dependent expansion coefficients are dynamically linked. When they are plotted one versus another in a scatter diagram, the cloud of points consists of a crescent shape that can easily be represented by a polynomial fit. If the first two expansion coefficients capture enough variability in the temperature profiles, the EOF modes plus the polynomial can be used to reconstruct temperature profiles independently from the set of data. This realistic synthesized environment can then be input to acoustic propagation models. This approach is applied to the case of the Messina Strait in which internal waves are known to be intensive. From a short-term series of temperature profiles collected on a thermistor string, range-dependent profiles along and across the strait are reconstructed. The acoustical impact study is conducted with the range-dependent acoustic model (RAM) parabolic equation (PE) model. The methodology presented in this paper is simple to run and requires a very affordable set of data. It could be used as an efficient alternative to ocean and acoustic model coupling for process studies or for regional studies especially in poorly known areas or highly variable areas, where it is difficult to obtain good sound-speed profile prediction from ocean models.

Description: Due to a production error, the lower left-hand side graph in Fig. 7 and the right-hand side graph in Fig. 8 in the above named paper were incorrect. The correct figures are presented here.

Description: In this paper, we examine how existing rules for bandpass sampling rates can be applied to quadrature bandpass sampling. We find that there are significantly more allowable sampling rates and that the minimum rate can be reduced.

Description: How the relative position between two spacecraft can be estimated utilizing signals emitted from X-ray pulsars is explained. The mathematical models of X-ray pulsar signals are developed, and the pulse delay estimation problem is formulated. The Cramér-Rao lower bound (CRLB) for any unbiased estimator of the pulse delay is presented. To retrieve the pulsar photon intensity function, the epoch folding procedure is characterized. Based on epoch folding, two different pulse delay estimators are introduced, and their performance against the CRLB is studied. One is obtained by solving a least squares problem, and the other uses the cross correlation function between the empirical rate function and the true one. The effect of absolute velocity errors on position estimation is also studied. Numerical simulations are performed to verify the theoretical results.

Description: This paper proposes a novel multisite radar system (MSRS) with multiple-input and multiple-output (MIMO) radars, i.e., MIMO-MSRS system, to improve the detection performance of fluctuating targets. The proposed MIMO-MSRS system increases the local signal-to-noise ratio (SNR) by using digital beamforming (DBF) among all transmitting and receiving channels in a single site. Then it smoothes the target's fluctuation via spatial diversity among the DBF outputs of different sites. For the MIMO-MSRS system, we derive the likelihood ratio test (LRT) detector at first based on the proposed signal model and spatial diversity conditions. Furthermore, with the derived statistics of the LRT detector in the fixed noise background, three optimization problems are discussed on the MIMO-MSRS system configurations, i.e., the numbers of sites and collocated channels in different sites. The first problem is to detect the lowest SNR target with a given probability of false alarm ( PF ), probability of detection ( PD ) and total system degrees of freedom (DOF). The second is to detect a target with the highest PD for a given PF , target SNR, and system DOF. The third is on the minimal system DOF to detect a target with a given PF , PD , and target SNR. For the uniform MIMO-MSRS system, both the standard optimal site number, i.e., the diversity DOF, and its closed-form approximation of the above three problems are obtained. Finally, some numerical results are also provided to demonstrate the effectiveness of the proposed MIMO-MSRS systems.

Description: In general, for multitarget problems where the number of targets and their states are time varying, the optimal Bayesian multitarget tracking is computationally demanding. The Probability Hypothesis Density (PHD) filter, which is the first-order moment approximation of the optimal one, is a computationally tractable alternative. By evaluating the PHD, the number of targets as well as their individual states can be extracted. Recent sequential Monte Carlo (SMC) implementations of the PHD filter have paved the way to its application to realistic nonlinear non-Gaussian problems. It is observed that the particle implementation of the PHD filter is dependent on current measurements, especially in the case of low observable target problems (i.e., estimates are sensitive to missed detections and false alarms). In this paper a PHD smoothing algorithm is proposed to improve the capability of PHD-based tracking system. It involves forward multitarget filtering using the standard PHD filter recursion followed by backward smoothing recursion using a novel recursive formula. Smoothing, which produces delayed estimates, results in better estimates for target states and a better estimate for the number of targets. Multiple model PHD (MMPHD) smoothing, which is an extension of the proposed technique to maneuvering targets, is also provided. Simulations are performed with the proposed method on a multitarget scenario. Simulation results confirm improved performance of the proposed algorithm.

Description: Multitarget tracking methods in a sensor network often assume the knowledge of the locations of the sensor nodes. However, in reality sensor nodes are randomly deployed with no prior knowledge about their positions. We propose a method to track an unknown and variable number of targets in the presence of false detections with the positions of sensor nodes estimated jointly to avoid the need of extra localization hardware. Moreover, as low-power consumption is a requirement in sensor networks, a collaborative estimation scheme is presented. For each target in the field under observation there is only a small set of sensor nodes that are active while the others remain in an idle state. The proposed technique is based on a Rao-Blackwellized sequential Monte Carlo (SMC) method that takes advantage of the fact that the state space of the unknown variables is separable. Therefore the problem is divided in two parts. The first one generates samples to estimate the number of targets and solves the association uncertainty between measurements and targets; while the second one is a multiple target tracking problem that can be solved with a modified unscented Kalman filter (MUKF) for each sample. It is shown through simulations that it is possible to track the multiple targets and also get accurate estimates of the unknown locations of the sensor nodes.

Description: In the general framework of radar detection, estimation of the Gaussian or non-Gaussian clutter covariance matrix is an important point. This matrix commonly exhibits a particular structure: for instance, this is the case for active systems using a symmetrically spaced linear array with constant pulse repetition interval. We propose using the particular persymmetric structure of the covariance matrix to improve the detection performance. In this context, this work provides two new adaptive detectors for Gaussian additive noise and non-Gaussian additive noise which is modeled by the spherically invariant random vector (SIRV). Their statistical properties are then derived and compared with simulations. The vast improvement in their detection performance is demonstrated by way of simulations or experimental ground clutter data. This allows for the analysis of the proposed detectors on both real Gaussian and non-Gaussian data.

Description: In this paper, a methodology is developed to evaluate differential carrier phase navigation architectures subject to reference receiver faults. Carrier phase measurements can be used to provide high accuracy estimates of a user's position. But in applications that involve safety-of-life, such as in precision approach for autonomous shipboard landing, integrity also plays a critical role. One source of integrity risk is the potential for GPS reference receiver failure. Integrity risk in these situations is typically mitigated by equipping the reference station with redundant receivers. However, various approaches to utilize redundant carrier phase measurements from multiple reference receivers are possible. In this paper, we describe two new methods: an averaging approach where different position solutions are averaged in the position domain, and a coupled estimation approach where the measurements from all reference receivers are coupled in the range domain and used to estimate a unified position solution. Furthermore, we investigate the impact of using these methods on accuracy and integrity from several perspectives, including availability performance, cycle resolution capabilities, implementation complexity, and computational efficiency.

Description: Multiscan data association can significantly enhance tracking performance in critical radar surveillance scenarios involving multiple targets, low detection probability, high false alarm probability, evasive target maneuvers, and finite radar resolution. Unfortunately, however, this approach is affected by the curse of dimensionality which hinders its real-time application for tracking problems with short scan periods and/or a high number of scans of the association logics and/or many measurements per scan. It is shown here how the formulation of the multiscan association as a multi-commodity or single-commodity flow optimization problem allows a relaxation of the association problem which, on one hand, provides close-to-optimal association performance and, on the other hand, implies a significant reduction of the computational load.

Description: In this paper, the problem of attitude control of a 3D nonlinear flexible spacecraft is investigated. Two nonlinear controllers are presented. The first controller is based on dynamic inversion, while the second approach is composed of dynamic inversion and γ-synthesis schemes. The extension of dynamic inversion approach to flexible spacecraft is impeded by the nonminimum phase characteristics when the panel tip position is taken as the output of the system. To overcome this problem, the controllers are designed by utilizing the modified output redefinition approach. It is assumed that only three torques in three directions on the hub are used. Actuator saturation is also considered in the design of controllers. To evaluate the performance of the proposed controllers, an extensive number of simulations on a nonlinear model of the spacecraft are performed. The performances of the proposed controllers are compared in terms of nominal performance, robustness to uncertainties, vibration suppression of panel, sensitivity to measurement noise, environment disturbance, and nonlinearity in large maneuvers. Simulation results confirm the ability of the proposed controller in tracking the attitude trajectory while damping the panel vibration. It is also verified that the perturbations, environment disturbances, and measurement errors have only slight effects on the tracking and damping performances.

Description: Formation flying is an emerging area in the Earth and space science and technology domains that utilize multiple inexpensive spacecraft by distributing the functionalities of a single platform spacecraft among miniature inexpensive platforms. Traditional spacecraft fault diagnosis and health monitoring practices involve around-the-clock monitoring, threshold checking, and trend analysis of a large amount of telemetry data by human experts that do not scale well for multiple space platforms. A novel hierarchical fault diagnosis framework and methodology is presented here that enables a systematic utilization of fuzzy rule-based reasoning to enhance the level of autonomy achievable in fault diagnosis at ground stations. Fuzzy rule-based fault diagnosis schemes for satellite formation flight are developed and investigated at different levels in the hierarchy for a leader-follower architecture. Our formation level fault diagnosis is found to be useful as a supervisory diagnosis scheme that can prompt the operators to have a closer look at the potential faulty components to determine the sources of a fault. Effectiveness of our proposed fault diagnosis methodology is demonstrated by utilizing synthetic formation flying data of five satellites that are configured in the leader-follower architecture, and are subjected to nonabrupt intermittent faults in the attitude control subsystem (ACS) and the electrical power subsystem (EPS) of the follower satellites.

Description: The problem of designing optimal conflict-free maneuvers for planar multiple aircraft encounters is studied. The maneuvers propose suitable heading changes for aircraft in a cooperative manner. The new mathematical approach provides optimal aircraft trajectories to resolve a wide variety of conflicts, especially in the presence of high-altitude clear air turbulence (CAT), normally encountered during en-route flights. The proposed approach effectively uses Genetic Algorithms (GA), together with modified webs, to quickly find conflict-resolving maneuvers. Different case studies show the method is fast enough to be used for real-time applications when resolving conflicts involving two aircraft. It is also efficient enough to resolve conflicts involving as many as 26 aircraft, which would be helpful in designing acrobatic maneuvers or in congested airspaces where emergency plans are needed.

Description: This paper gives a detailed performance analysis for the novel radar long-time coherent integration method, i.e., Radon-Fourier transforms (RFT). Some important properties of RFT, e.g., two-dimensional (2D) impulse response, 2D translational invariance, multitarget linear additivity, linear signal-to-noise ratio gain in additive white Gaussian noise (AWGN), as well as the 2D correlation function of transformed AWGN, are derived for continuous and discrete RFT, respectively. However, because of discrete pulse sampling, finite range resolution, and limited integration time, the "blind-speed sidelobes (BSSL)" of discrete RFT may inevitably appear in real applications. Although the BSSL are reduced with the increase of the blind-speed integer, they may still lead to false alarms or loss detections in a real multitarget scenario. Based on the analytic expression derived for BSSL, the causes of BSSL are analyzed and the effective BSSL suppression methods are proposed. Finally, numerical experiments are also provided to demonstrate the effectiveness of the proposed methods.

Description: Radar offers unique advantages over other sensors for the detection of humans, such as remote operation during virtually all weather and lighting conditions, increased range, and better coverage. Many current radar-based human detection systems employ some type of Fourier analysis, such as Doppler processing. However, in many environments, the signal-to-noise ratio (SNR) of human returns is quite low. Furthermore, Fourier-based techniques assume a linear variation in target phase over the aperture, whereas human targets have a highly nonlinear phase history. The resulting phase mismatch causes significant SNR loss in the detector itself. In this paper, human target modeling is used to derive a more accurate nonlinear approximation to the true target phase history. The likelihood ratio is optimized over unknown model parameters to enhance detection performance. Cramer-Rao bounds on parameter estimates and receiver operating characteristic curves are used to validate analytically the performance of the proposed method and to evaluate simulation results.