ALBERT

Description: The capabilities of bistatic radar observations to estimate the wind field over the ocean are investigated in this paper. The work is based on the analysis of simulated data obtained through a well-established electromagnetic model, which accounts for the anisotropy of the ocean’s spectrum and of second-order effects of the scattering phenomenon. Both co-polarized and cross-polarized C-band numerical data, obtained considering monostatic and bistatic configurations, are exploited to investigate on the existence of optimal configurations able to minimize the wind vector error estimation. To this aim, the sensitivities of the bistatic normalized radar cross section with respect to both wind speed and direction are accurately investigated and exploited to evaluate the minimum achievable error standard deviation of the estimation. Small and large baselines are analyzed, giving particular emphasis to bistatic geometries constituted by one or two passive receivers aligned along the track defined by the active system. This investigation, originally performed in the framework of the SAOCOM-CS scientific satellite mission, is conceived to accurately assess the potentiality of bistatic observations of the ocean over variable baselines and to gather valuable information for the design of future bistatic satellite missions.

Description: The normalized differential spectral attenuation (NDSA) approach was proposed years ago as an effective way to estimate the integrated water vapor (IWV) along a tropospheric propagation path between two low Earth orbit satellites. Two applications are possible: the retrieval of vertical profiles of WV if the sense of rotation is opposite and the retrieval of 2-D fields of WV over vertical tropospheric sections if the sense is the same. The method relies on the measurement of the so-called spectral sensitivity $S$ at given frequencies, and on IWV-S relationships that convert $S$ into an estimate of IWV along the radio link where $S$ is measured. In this paper, we recompute the IWV-S relationships using synthetic atmospheres generated by means of European Centre for Medium-Range Weather Forecasts (ECMWF) analysis data instead of radiosonde profiles as done by ourselves in the past. Thanks to the uniform spatial distribution of the ECMWF data on a global Earth scale, we were able to validate the IWV-S relationships in the Ku/K band previously found through synthetic atmospheres generated by means of the aforementioned irregularly spaced radiosonde data, and to define the IWV-S relationships at 179 and 181 GHz that are exploitable in the upper troposphere. Since the ECMWF data also include information about the liquid water (LW) content, we then show that an additional $S$ channel at 32 GHz can be exploited to detect and correct the bias induced by LW on IWV estimates made by applying the NDSA in the Ku/K band.

Description: Provides a listing of current staff, committee members and society officers.

Description: Presents the table of contents for this issue of this publication.

Description: The reconstruction of urban buildings from large-scale airborne laser scanning point clouds is an important research topic in the geoscience field. Large-scale urban scenes usually contain a large number of object categories and many overlapped or closely neighboring objects, which poses great challenges for classifying and modeling buildings from these data sets. In this paper, we propose a deep reinforcement learning framework that integrates a 3-D convolutional neural network, a deep Q-network, and a residual recurrent neural network for the efficient semantic parsing of large-scale 3-D point clouds. The proposed framework provides an end-to-end automatic processing method that maps the raw point cloud to the classification results of the given categories. After obtaining the building classes, we utilize an edge-aware resampling algorithm to consolidate the point set with noise-free normals and clean preservation of sharp features. Finally, 2.5-D dual contouring, which is a data-driven approach, is introduced to generate urban building models from the consolidated point clouds. Our method can generate lightweight building models with arbitrarily shaped roofs while preserving the verticality of connecting walls.

Description: This paper shows initial results from estimating Doppler radial surface velocities (RVLs) over Arctic sea ice using the Sentinel-1A (S1A) satellite. Our study presents the first quantitative comparison between ice drift derived from the Doppler shifts and drift derived using time-series methods over comparable time scales. We compare the Doppler-derived ice velocities with global positioning system tracks from a drifting ice station as well as vector fields derived using traditional cross correlation between a pair of S1A and Radarsat-2 images with a time lag of only 25 min. A strategy is provided for precise calibration of the Doppler values in the context of the S1A level-2 ocean RVL product. When comparing the two methods, root-mean-squared errors (RMSEs) of 7 cm/s were found for the extra wide (EW4) and EW5 swaths, while the highest RMSE of 32 cm/s was obtained for the EW1 swath. Though the agreement is not perfect, our experiment demonstrates that the Doppler technique is capable of measuring a signal from the ice if the ice is fast moving. However, for typical ice speeds, the uncertainties quickly grow beyond the speeds we are trying to measure. Finally, we show how the application of an antenna pattern correction reduces a bias in the estimated Doppler offsets.

Description: The bistatic radar equation currently used for simulating surface-reflected waveforms or delay-Doppler maps (DDMs), produced by signals of opportunity from global navigation satellites system (GNSS) or communication satellites, was previously derived under some limiting assumptions. One of them was the use of the Kirchhoff approximation in a geometric optics limit that assumes strong diffuse (noncoherent) scattering typical for very rough surfaces. This equation would produce an incorrect result for the case of weak diffuse scattering, or in the presence of coherent reflection. In this paper, it is shown that the assumption of strong diffuse scattering is not necessary in deriving such an equation. The derivation of a generalized bistatic radar equation is now based only on the assumption of roughness statistics being spatially homogeneous, and thus this equation is applicable for a much wider range of surface conditions and scattering geometries. This approach allows to correctly describe the transition from partially coherent scattering to completely noncoherent, strong diffuse scattering. It is demonstrated for the case of the GNSS-R DDMs simulated for a wide range of surface winds, and their transitional behavior is discussed.

Description: Differential interferometric synthetic aperture radar (InSAR) time-series processing relies on identifying coherent pixels in SAR image stacks that show the persistent scatterer (PS) or distributed scatterer (DS) behavior. Accuracy of InSAR time-series estimates is dependent on the quality of selected PS/DS pixels. Current pixel selection techniques perform well when identifying highly coherent pixels but produce many false alarms in low coherence regions due to the inherent bias in residual phase estimation. Therefore, pixels with low coherence may have the appearance of noise and be rejected if the coherence threshold is too high. In contrast, lowering the threshold increases the number of false alarms introduced in processing giving noisier time-series as a result of incorrect phase unwrapping. The multidimensional SAR data acquisition can be described as a zero mean Gaussian process fully described by the covariance matrix. In this paper, we investigate the covariance matrix using a random matrix theory approach to find the statistical properties of the eigenvalues for simulated and real SAR data. The probability distribution of all the eigenvalues in this case is limited by the Marcenko–Pastur distribution. The histogram of the highest eigenvalue follows a Tracy–Widom distribution. Thus, by adopting a pixel selection strategy based on a threshold on the highest eigenvalue of the coherence matrix, we can differentiate between low coherence and noise pixels. In addition, our technique provides a methodology to detect the number of targets present in multiscatterer layover pixels and extract time-series information from double bounce response of bridges. Applying the technique for TerraSAR-X data over Berlin shows the effectiveness of the algorithm.

Description: Logging-while-drilling (LWD) or measuring-while-drilling tools are routinely used to guide well placement during exploration of hydrocarbons reservoirs. These tools have become fundamental for directional and horizontal drilling operations. In this paper, we present a perturbation method to model electromagnetic fields produced by time-harmonic sources in radially stratified and axially toroidal structures describing LWD tools inside curved boreholes (i.e., boreholes with axial bending). The proposed formulation is validated against brute-force finite-difference results in various representative scenarios. Numerical results indicate that the proposed perturbative approach can provide a reduction in the computational effort required to analyze this class of problems of several orders of magnitude versus brute-force approaches.

Description: Due to the 3-D nature of hyperspectral images, as well as the spatial properties (such as regularity and continuity) of land covers, many 3-D feature extraction operators have been designed to fully exploit the joint spatial–spectral information. However, the large amount of obtained features can suffer from the “curse of dimensionality” problem, especially for the small training sample set. Moreover, various spatial–spectral features can represent the characteristics of the hyperspectral image from different aspects. In this paper, a multiple 3-D feature fusion framework (M3DF 3 ) has been proposed for hyperspectral image classification. First, we extend the 2-D Gabor surface feature into 3-D (3DSF) domains to comply with the spatial–spectral structure of the hyperspectral image, which is directly applied on the original hyperspectral image instead of the Gabor features. Second, three 3-D feature extraction methods, including the 3-D morphological profile, the 3-D local binary pattern, and the proposed 3DSF, that, respectively, characterize the hyperspectral image from three different angles, i.e., morphology, local dependence, and shape smoothness, are fused under a multitask sparse representation framework to take full advantage of the multiple 3-D features together. The proposed M3DF 3 approach was fully tested on three real-world hyperspectral image data, i.e., the widely used Indian Pines, Pavia University, and Houston University. The results show that our method can achieve as high as 68.22%, 79.44%, and 72.84% accuracies, respectively, even when only few samples, i.e., three samples per class, are used for training.

Description: Presents the front cover for this issue of the publication.

Description: The conventional methods for target detection and discrimination in high-resolution synthetic aperture radar (SAR) images usually have low accuracy and slow speed, especially for large complex scenes. To overcome these drawbacks, in this paper, we propose a target detection and discrimination method based on visual attention model. In the detection stage, to pop out the targets and suppress the background clutter in the saliency map, we select the task-dependent scales from the Gaussian pyramid of the original SAR image. Moreover, we adopt the clustering algorithm to remerge several isolated focus of attention areas, which are obtained from the saliency map, into a complete target region. The candidate target SAR image chips are extracted with relative high accuracy and low time cost in this stage. Since there may be single target, multiple targets, or partial targets with complex clutter in each SAR image chip, it is hard to acquire accurate target-shaped blob via segmentation. Some classical discrimination features which are extracted based on target segmentation may lose effectiveness. In the discrimination stage of our method, to solve the above problem, based on the saliency and gist (SG) features for optical satellite images, we propose the modified SG (MSG) features for SAR target discrimination. The MSG features are complementary to each other and can provide a more complete description of the extracted SAR image chips without segmentation, which also reduces the computation burden. The experimental results on the synthetic images and miniSAR real SAR image data set demonstrate that the proposed target detection and discrimination method can detect and discriminate the targets from the complex background clutter with high accuracy and fast speed in high-resolution SAR images.

Description: Hyperspectral image (HSI) noise reduction is an active research topic in HSI processing due to its significance in improving the performance for object detection and classification. In this paper, we propose a joint spectral and spatial low-rank (LR) regularized method for HSI denoising, based on the assumption that the free-noise component in an observed signal can exist in latent low-dimensional structure while the noise component does not have this property. The proposed HSI denoising method not only considers the traditional LR property across the spectral domain but also leverages nonlocal LR property over the spatial domain. The main contribution of this paper is the incorporation of the low-rankness-based nonlocal similarity into sparse representation to characterize the spatial structure. Specially, the similar patches in each cluster usually contain similar sharp structure such as edges and textures; LR performed on cluster entitles to achieve a lower rank than that on the global spectral correlation. To make the proposed method more tractable and robust, we develop a variable splitting-based technique to solve the optimization problem. Experiment results on both simulated and real hyperspectral data sets demonstrate that the proposed method outperforms state-of-the-art methods with significant improvements both visually and quantitatively.

Description: Downward-looking linear array 3-D synthetic aperture radar (SAR) has attracted increasing attention in the field of radar imaging. As widely reported, the volume of data can be significantly reduced by a random sparse linear array. However, the 2-D under-sampled azimuth-cross-track data brought by the sparse linear array will produce high-level side-lobes, as well as the aliasing and the false-alarm targets. To deal with those problems, this paper introduces a recently developed theory, matrix completion (MC). The new theory could recover a matrix with a small subset of known elements of the matrix. It is founded on the assumption that the matrix is essentially low rank. For downward-looking 3-D SAR with a random sparse linear array, the received 3-D data can be treated as a series of uncorrelated 2-D matrices by the separated channel process. First, range compression can be realized by means of pulse compression. Then, the sets of the 2-D under-sampled azimuth-cross-track matrix can be completed into a full-sampled one via MC trick. The resulting 3-D images can be focused by synthetic aperture technique along the azimuth direction and beamforming operation along the cross-track direction, with the recovered full-sampled matrix. The proposed algorithm achieves high resolution and low-level side-lobes with the acceptable computational cost and memory consumption. It is verified by several numerical simulations and multiple comparative studies on real data. The experimental results clearly demonstrate the imaging performance across different under-sampling rates and signal-noise rates.

Description: Hydraulic fracturing is a technique to fracture rocks by pumping high-pressure fluid into a segment of a well. The created fractures help to release a hydrocarbon resource such as oil or natural gas from the rock. A group of small-scaled fracturing field tests are performed by the Advanced Energy Consortium to investigate the feasibility of using the galvanic electromagnetic (EM) method to map fractures. The injected proppants are designed with high EM contrasts (e.g., conductivity and permittivity) to generate detectable signals at electrode-type sensors. To map the created fractures, an efficient 3-D EM inversion method is introduced to simultaneously reconstruct conductivity and permittivity profiles in fractures. First, to test the capability of the inversion solver and the designed experimental setting for successful fracture mapping, the noise-polluted synthetic data are used to reconstruct the fracture on a theoretical model. It shows that the designed experimental setting can be used to map the fracture and the inversion solver is able to reconstruct the fracture in both conductivity and permittivity. The inversion method is then applied to two hydraulic fracturing field tests with injected high-contrast proppants, Loresco coke breeze and steel shot. The fracture conductivity and permittivity are reconstructed based on the voltage signals difference between the postfracturing and prefracturing data. The reconstructed fracture profiles are compared with the coring samplings to show the reliability of the inversion results. Their good agreement demonstrates that the experimental setting and the galvanic inverse solver are able to estimate the fracture size and location reliably.

Description: One option for the improvement of weather radar technology is the use of dual-polarized phased-array radar for weather observations. Several risk factors on this path have been identified and one of the most important ones is the existence of significant cross-polar patterns inherent to the phased-array antenna. These antenna patterns induce cross-coupling between returns from the two orthogonal radiation planes, which results in the biases of polarimetric variable estimates. Furthermore, the inductive and capacitive coupling in hardware behind the antenna may exacerbate the cross-coupling effects. This presents a formidable challenge because sufficient cross-polar isolation is difficult to achieve by the antenna hardware alone. Hence, additional approaches are required to reduce the biases due to cross-coupling. One proposed technique is a 180° pulse-to-pulse phase change of signals injected in either the horizontal or vertical ports of the transmission elements. This approach was analyzed for signals processed in the time domain but its effects in the frequency domain have not been investigated. Herein, these effects are considered in the presence of nondepolarizing scatterers.

Description: Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation have been successfully generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data since early 2000. As the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard, the Suomi National Polar-orbiting Partnership (SNPP) has inherited the scientific role of MODIS, and the development of a continuous, consistent, and well-characterized VIIRS LAI/FPAR data set is critical to continue the MODIS time series. In this paper, we build the radiative transfer-based VIIRS-specific lookup tables by achieving minimal difference with the MODIS data set and maximal spatial coverage of retrievals from the main algorithm. The theory of spectral invariants provides the configurable physical parameters, i.e., single scattering albedos (SSAs) that are optimized for VIIRS-specific characteristics. The effort finds a set of smaller red-band SSA and larger near-infrared-band SSA for VIIRS compared with the MODIS heritage. The VIIRS LAI/FPAR is evaluated through comparisons with one year of MODIS product in terms of both spatial and temporal patterns. Further validation efforts are still necessary to ensure the product quality. Current results, however, imbue confidence in the VIIRS data set and suggest that the efforts described here meet the goal of achieving the operationally consistent multisensor LAI/FPAR data sets. Moreover, the strategies of parametric adjustment and LAI/FPAR evaluation applied to SNPP-VIIRS can also be employed to the subsequent Joint Polar Satellite System VIIRS or other instruments.

Description: Ultrawideband radar with high-range resolution is a promising technology for use in short-range 3-D imaging applications, in which optical cameras are not applicable. One of the most efficient 3-D imaging methods is the range-point migration (RPM) method, which has a definite advantage for the synthetic aperture radar approach in terms of computational burden, high accuracy, and high spatial resolution. However, if an insufficient aperture size or angle is provided, these kinds of methods cannot reconstruct the whole target structure due to the absence of reflection signals from large part of target surface. To expand the 3-D image obtained by RPM, this paper proposes an image expansion method by incorporating the RPM feature and fully polarimetric data-based machine learning approach. Following ellipsoid-based scattering analysis and learning with a neural network, this method expresses the target image as an aggregation of parts of ellipsoids, which significantly expands the original image by the RPM method without sacrificing the reconstruction accuracy. The results of numerical simulation based on 3-D finite-difference time-domain analysis verify the effectiveness of our proposed method, in terms of image-expansion criteria.

Description: This paper addresses the performance in the retrieval of 3-D mean deformation maps by exploiting simultaneous or quasi-simultaneous squinted synthetic aperture radar (SAR) interferometric acquisitions in a repeat-pass scenario. In multisatellite or multibeam low earth observation missions, the availability of two (or more) lines of sight (LOSs) allows the simultaneous acquisition of SAR images with different squint angles, hence improving the sensitivity to the north–south component of the deformation. Due to the simultaneity of the acquisitions, the troposphere will be highly correlated and, therefore, will tend to cancel out when performing the differential measurement between the interferograms obtained with the different LOSs, hence resulting in a practically troposphere-free estimation of the along-track deformation measurement. In practice, however, the atmospheric noise in the differential measurement will increase for increasing angular separations. This paper expounds the mathematical framework to derive the performance by properly considering the correlation of the atmospheric delays between the simultaneous acquisitions. To that aim, the hybrid Cramér–Rao bound is exploited making use of the autocorrelation function of the troposphere. Some performance examples are presented in the frame of future spaceborne SAR missions at C and L band.

Description: The detection of meteorological targets using ground-based weather radars usually suffers from ground clutter and beam blockage. These nonmeteorological or weakened signals should be identified so quality control should be implemented before weather radar data can be used. Conventional quality control methods aim at differentiating between echo structures of ground clutter and meteorological targets, and use terrain information to calculate beam blockage regions based upon standard atmospheric refraction. However, it is difficult to achieve the goal for long-term large data sets by conventional methods due to the complexity and diversity of weather radar echoes. In this paper, regions of ground clutter and beam blockage are first identified through the statistics on spatial distribution of reflectivity and fuzzy logic classification, and then they are used as masks to remove data from the scan. The new method is applied to data of the Nanjing weather radar in China. By the aid of a proposed evaluation scheme and the visual recognition, quality control results of the new method are compared with those of the conventional methods. It is found that the new method can provide better identification of ground clutter or beam blockage and thus better quality control results. The new scheme has a good prospect in operational service for its principle advantages, easy applicable conditions, and better performance compared with conventional methods.

Description: This paper describes the adaptation of a Bayesian sea ice detection algorithm for the scatterometer on-board the European Remote Sensing (ERS) satellites (ERS-1 and ERS-2). The algorithm is based on statistics of distances to ocean wind and sea ice geophysical model functions (GMFs) and its performance is validated against coincident active and passive microwave data. We furthermore propose a new model for sea ice backscatter at the C-band in vertical polarization based on the sea ice GMFs derived from ERS and advanced scatterometer data. The model characterizes the dependence of sea ice backscatter on the incidence angle and the sea ice type, allowing a more precise incidence angle correction than afforded by the usual linear transformation. The resulting agreement between the ERS, QuikSCAT, and special sensor microwave imager sea ice extents during the year 2000 is high during the fall and winter seasons, with an estimated ice edge accuracy of about 20 km, but shows persistent biases between scatterometer and radiometer extents during the melting period, with scatterometers being more sensitive to summer (lower concentration and rotten) sea ice types.

Description: This paper evaluates the calibration quality during the blackbody (BB) warm-up cool-down cycle for thermal emissive bands onboard Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS). This evaluation utilizes data from Aqua MODIS Collection 6 Level-1B products and VIIRS Sensor Data Records in 6-min granule format provided by the NASA Land Science Investigator-led Processing System. Nearly simultaneous hyperspectral measurements from the Aqua Atmospheric Infrared Sounder (AIRS) and the S-NPP Cross-track Infrared Sounder (CrIS) are used as references for MODIS and VIIRS, respectively. Each AIRS footprint of 13.5 km is co-located with multiple MODIS pixels while each CrIS field of view of 14 km is co-located with multiple VIIRS pixels. The corresponding AIRS-simulated MODIS and CrIS-simulated VIIRS radiances are derived by convolutions based on sensor-dependent relative spectral response functions. In this paper, the analysis mainly focuses on the bands that are used in sea surface temperature products. The results show that there is virtually no impact for MODIS bands 22 and 23 and bands 31 and 32 for a BB temperature below 290 K; however, when the BB temperature increases above 290 K, the impact is up to 0.3 K for bands 22 and 23 and 0.05 K for bands 31 and 32, respectively. For VIIRS, BB temperature-dependent drifts are observed in M15 and M16, which can reach 0.15 and 0.1 K, respectively, over the operational BB temperature range and the VIIRS brightness temperature range.

Description: Most of the existing deep-learning-based methods are difficult to effectively deal with the challenges faced for geospatial object detection such as rotation variations and appearance ambiguity. To address these problems, this paper proposes a novel deep-learning-based object detection framework including region proposal network (RPN) and local-contextual feature fusion network designed for remote sensing images. Specifically, the RPN includes additional multiangle anchors besides the conventional multiscale and multiaspect-ratio ones, and thus can deal with the multiangle and multiscale characteristics of geospatial objects. To address the appearance ambiguity problem, we propose a double-channel feature fusion network that can learn local and contextual properties along two independent pathways. The two kinds of features are later combined in the final layers of processing in order to form a powerful joint representation. Comprehensive evaluations on a publicly available ten-class object detection data set demonstrate the effectiveness of the proposed method.

Description: Recently, multifeature learning in collaborative representation classification (CRC) for hyperspectral images has generated promising performance. In this paper, two novel multifeature learning algorithms that update dictionary directly and indirectly are proposed. In order to offer the complementarity of multifeature, four different types of features—global feature (i.e., Gabor feature), local feature (i.e., local binary pattern), shape feature (i.e., extended multiattribute profiles), and spectral feature—are adopted in this paper. Under the hypothesis that most of the features should share the same coding pattern in CRC, this paper proposes to learn proper dictionaries for each feature until obtaining stable codes in a linear classifier. Furthermore, to avoid the explicit mapping of infinite-dimensional dictionaries in a nonlinear kernelized classifier, an indirect approach to construct the transformation matrix from original dictionaries to learn new dictionaries is developed. Three real hyperspectral images acquired from different sensors are adopted for performance evaluation. The experimental results demonstrate that the proposed methods can provide superior performance compared with those of the state-of-the-art classifiers.

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Description: Ground penetrating radar imaging from the data acquired with arbitrarily oriented dipole-like antennas is considered. To take into account variations of antenna orientations resulting in spatial rotation of antenna radiation patterns and polarizations of transmitted fields, the full-wave method that accounts for the near-, intermediate-, and far-field contributions to the radiation patterns is applied for image reconstruction, which is formulated as a linear inversion problem. Two approaches, namely, an interpolation-based method and a nonuniform fast Fourier transform-based method, are suggested to efficiently implement the full-wave method by computing exact Green’s functions. The effectiveness and accuracy of the method proposed have been verified via both numerical simulations and experimental measurements, and significant improvement of the reconstructed image quality compared with the traditional scalar-wave-based migration algorithms is demonstrated. The results can be directly utilized by forward-looking microwave imaging sensors such as installed at tunnel boring machine or can be used for the observation matrix computation in regularization-based inversion algorithms.

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Description: This paper presents an extension of H/A/Alpha polarimetric SAR (PolSAR) decomposition for polarimetric interferometric SAR (PolInSAR) images, and introduces new parameters using both polarimetric and interferometric data. These parameters provide more information than extracted parameters from H/A/Alpha PolSAR decomposition. The new parameters significantly increase the potential of PolSAR data for, for example, forest and oriented building discrimination. With the availability of interferometric information in addition to PolSAR information, there is the possibility to reduce the entropy of PolSAR data. The relationship between the entropy of PolInSAR decomposition and coherence between images has also been shown. E-SAR PolInSAR L-band data of Oberpfaffenhofen, Germany, Northern Sweden, and Goermin, Germany are used to validate the H/A/Alpha PolInSAR decomposition. The experimental results on the first data set, including agricultural, forest, and urban areas, show that this decomposition has a better performance than the standard H/A/Alpha decomposition method in oriented urban areas with large orientation angle, for example. Also, the results on the second data set of a forest area show that the PolInSAR decomposition has more reasonable and superior performance over the PolSAR decomposition. The forest and nonforest regions can be correctly discriminated by using the presented PolInSAR parameters. Finally, in the last data set, the better detection of the agricultural crops and fields boundaries is provided by the proposed method.

Description: Atmospheric compensation (AC) is a fundamental and critical step for quantitative exploitation of hyperspectral data. It is the means by which the reflectance of an object/material is estimated from the measured at-sensor radiance. Such reflectance is the inherent signature that is used to identify various materials in a monitored scene. AC is quite complex and is hampered by the large amount of uncontrollable variables that play a role: just think about the spatial variability of some atmospheric constituents such as water vapor and aerosols, or to the rapidly spatially varying effects of the radiation coming from adjacent areas. Though, in principle, some atmospheric parameters and radiometric quantities such as solar irradiance and sky irradiance can be measured during the flight, in practice such measures are rarely available in an operational framework or are taken at a single point of the surface ignoring their spatial variation. Thus, a prompt quantitative exploitation of hyperspectral data for operational purposes, such as material identification and object detection, requires unsupervised and accurate AC procedures that can learn from the image itself the parameters of the inversion model and follow their variability within the scene. In this framework, we present a new unsupervised methodology for AC of airborne hyperspectral images in the visible and near-infrared spectral range. The proposed methodology relies on a radiative transfer model accounting for the adjacency effect and allows the estimation of relevant atmospheric parameters. Specifically, it embeds two new algorithms for the estimation of: 1) aerosol and atmospheric visibility and 2) the water vapor content of the atmosphere accounting for the spatial variability of such a parameter. The two algorithms significantly differ from those adopted by existing state-of-the-art approaches or in commercial packages such as fast line-of-sight atmospheric analysis of spectral hypercubes and airborne atm- spheric and topographic correction algorithm. In this paper, we present the detailed description of the new AC methodology, and we analyze the results provided by the algorithm over real data.

Description: Sparsity-regularized synthetic aperture radar (SAR) imaging framework has shown its remarkable performance to generate a feature-enhanced high-resolution image, in which a sparsity-inducing regularizer is involved by exploiting the sparsity priors of some visual features in the underlying image. However, since the simple prior of low-level features is insufficient to describe different semantic contents in the image, this type of regularizer will be incapable of distinguishing between the target of interest and unconcerned background clutters. As a consequence, the features belonging to the target and clutters are simultaneously affected in the generated image without concerning their underlying semantic labels. To address this problem, we propose a novel semantic information guided generative framework for target-oriented SAR image formation, which aims at enhancing the interested target scatters while suppressing the background clutters. First, we develop a new semantics-specific regularizer for image formation by exploiting the statistical properties of different semantic categories in a target scene SAR image. In order to infer the semantic label for each pixel in an unsupervised way, we moreover induce a novel high-level prior-driven regularizer and some semantic causal rules from the prior knowledge. Finally, our regularized framework for image formation is further derived as a simple iteratively reweighted $ell _{1}$ minimization problem that can be conveniently solved by many off-the-shelf solvers. Experimental results demonstrate the effectiveness and superiority of our framework for SAR image formation in terms of target enhancement and clutters suppression, compared with the state of the arts. Additionally, the proposed framework opens a new direction of devoting some machine learning strategies to image formation, which can benefit the subsequent decision-making tasks.

Description: Small-scale glaciological processes can drive large-scale ice sheet behavior but remain underreported due to a paucity of surface elevation measurements in remote polar regions. Satellite images provide a relatively long record of spatially dense surface observations, which allow us to investigate changes in ice sheet topography on the spatial scales of 1–10 km. Inferring surface topography from satellite images is an established technique, but in previous efforts, strict requirements for illumination conditions and image quality have led to a great quantity of discarded data. Relaxing quality requirements and fitting linear trends to the time series of image-derived surface topography allow inclusion of more total signal and enable blending data from multiple platforms. As a proof of concept, we combine 121 MODerate-resolution Imaging Spectroradiometer images to develop a 250-m resolution map of surface elevation change at Totten Glacier, Antarctica achieving a 1- $sigma $ uncertainty of 0.22 $textrm {m},textrm {a}^{-1}$ for a 15-year period. Our method of repeat photoclinometry agrees with repeat laser altimetry while revealing clear patterns of surface elevation change associated with ice advection, channelized ice shelf basal melt, subglacial lake activity, and possible grounding line migration.

Description: Amplitude-versus-offset (AVO) inversion always plays an important role in reservoir fluid identification, which allows the estimation of various rock and fluid properties from prestack seismic data. In this paper, we propose a new method for discrimination of hydrocarbon accumulation that combines frequency-dependent AVO inversion scheme and variational mode decomposition (VMD). VMD is a recently developed algorithm for adaptive signal decomposition that is able to nonrecursively decompose a multicomponent signal into a number of quasi-orthogonal intrinsic mode functions and avoid mode mixing effectively. VMD is superior to other state-of-the-art approaches in obtaining high-resolution and high-fidelity local time–frequency depiction performance. Two synthetic signals are employed to illustrate that VMD achieves higher temporal and frequency resolution than the conventional continuous wavelet transform (CWT) decomposition. Other synthetic examples, elastic and dispersive, are utilized to demonstrate that the proposed method is more reliable for the detection of hydrocarbon saturation and a comparison is made with the CWT-based inverted results. Application on field data has further shown that the proposed approach has the potential in identifying the reservoir related to hydrocarbon.

Description: The low-level temperature inversions have significant impacts on Arctic climate change feedbacks. The Atmospheric Infrared Sounder (AIRS) can extract the inversions over both land and ocean, and it is, however, sensitive to the presence of clouds. In this paper, we evaluate the effect of cloud fraction (CF) on AIRS inversions over both land and ocean. First, the AIRS inversions under clear-sky conditions are compared with the results from the microwave-based Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) observations in 2007–2013. Results show that despite the COSMIC and AIRS inversions appearing to be deeper and stronger in winter than in autumn, spring, and summer, the former is generally shallower and stronger than the latter in all seasons over both land and ocean. Time-series analysis of the mean monthly inversions from COSMIC and AIRS observations in 2007–2013 under both clear-sky and cloudy conditions further indicates that their differences are systematic and can be effectively mitigated after calibration under all sky conditions. Taking the calibrated COSMIC inversions as references, the AIRS inversion depths can be estimated with a root mean square (rms) of less than about 86 and 135 m, and the AIRS inversion strength can be obtained with an rms of better than about 1.7 °C and 1.3 °C under cloudy conditions over land and ocean, respectively. Moreover, while the AIRS inversion depths are insensitive to CF variations over both land and ocean, the inversion strengths are more sensitive to the CF variations over land than ocean.

Description: Hyperspectral image classification has become a research focus in recent literature. However, well-designed features are still open issues that impact on the performance of classifiers. In this paper, a novel supervised deep feature extraction method based on siamese convolutional neural network (S-CNN) is proposed to improve the performance of hyperspectral image classification. First, a CNN with five layers is designed to directly extract deep features from hyperspectral cube, where the CNN can be intended as a nonlinear transformation function. Then, the siamese network composed by two CNNs is trained to learn features that show a low intraclass and high interclass variability. The important characteristic of the presented approach is that the S-CNN is supervised with a margin ranking loss function, which can extract more discriminative features for classification tasks. To demonstrate the effectiveness of the proposed feature extraction method, the features extracted from three widely used hyperspectral data sets are fed into a linear support vector machine (SVM) classifier. The experimental results demonstrate that the proposed feature extraction method in conjunction with a linear SVM classifier can obtain better classification performance than that of the conventional methods.

Description: The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input in many climate and ecological models. The accuracy of satellite FAPAR products directly influences estimates of ecosystem productivity and carbon stocks. The targeted accuracy of FAPAR products is 10% or 0.05 for many applications. However, most current FAPAR products do not meet such requirements, and further improvements are still needed. In this paper, a data fusion scheme based on the multiple resolution tree (MRT) approach is developed to integrate multiple satellite FAPAR estimates at site and regional scales. MRT was chosen because of the superior computational efficiency compared with other fusion methods. The fusion scheme removed the bias in FAPAR estimates and resulted in a 15% increase in the $R^{2}$ and 3% reduction in the root-mean-square error compared with the average of individual FAPAR estimates. The regional-scale fusion filled in the missing values, and provided spatially consistent FAPAR distributions at different resolutions. Overall, MRT can be used to efficiently and accurately generate spatially and temporally continuous FAPAR data across both site and regional scales.

Description: Synthetic aperture radar (SAR) image classification is a fundamental process for SAR image understanding and interpretation. With the advancement of imaging techniques, it permits to produce higher resolution SAR data and extend data amount. Therefore, intelligent algorithms for high-resolution SAR image classification are demanded. Inspired by deep learning technology, an end-to-end classification model from the original SAR image to final classification map is developed to automatically extract features and conduct classification, which is named deep recurrent encoding neural networks (DRENNs). In our proposed framework, a spatial feature learning network based on long–short-term memory (LSTM) is developed to extract contextual dependencies of SAR images, where 2-D image patches are transformed into 1-D sequences and imported into LSTM to learn the latent spatial correlations. After LSTM, nonnegative and Fisher constrained autoencoders (NFCAEs) are proposed to improve the discrimination of features and conduct final classification, where nonnegative constraint and Fisher constraint are developed in each autoencoder to restrict the training of the network. The whole DRENN not only combines the spatial feature learning power of LSTM but also utilizes the discriminative representation ability of our NFCAE to improve the classification performance. The experimental results tested on three SAR images demonstrate that the proposed DRENN is able to learn effective feature representations from SAR images and produce competitive classification accuracies to other related approaches.

Description: We present a new synthetic aperture radar (SAR) raw signal simulator, which is able to simultaneously generate raw signals of different polarimetric channels of a polarimetric SAR system in such a way that a correct covariance matrix is obtained for the final images. Extended natural scenes, dominated by surface scattering, are considered. A fast Fourier-domain approach is used for the generation of raw signals. Presentation of theory is supplemented by meaningful experimental results, including a comparison of simulations with real polarimetric scattering data.

Description: Sparse representation has been widely used in the field of remote sensing image super-resolution (SR) to restore a high-quality image from a low-resolution (LR) image, e.g., from the blurred and downsampled version of an LR image’s high-resolution (HR) counterpart. It is well known that each image patch can be represented by a linear combination of the atoms of an overcomplete dictionary, and we can obtain an expression of sparse coefficients by $l_{1}$ norm regularization. Owing to the lack of an inner relationship between image patches and an image’s global information, the traditional methods of jointly training two overcomplete dictionaries cannot obtain good SR results. Therefore, we propose an effective approach for remote sensing image SR based on sparse representation. More specifically, a novel global joint dictionary model (GJDM) is used to explore the prior knowledge of images, including local and global characteristics. First, we train two dictionaries for detail image patches and HR patches. Second, in order to enhance the inner relationship between image patches, we introduce a global self-compatibility model for global regularization. Finally, the sparse representation and the local and nonlocal constraints are integrated to improve the performance of the model, and the fast adaptive shrinkage-thresholding algorithm is employed to solve the convex optimization problem in the GJDM. Compared with other methods, the results of the proposed method show good SR performance in preserving details and texture information and significant improvement in a peak signal-to-noise ratio.

Description: This paper presents the estimation of the ground elevation and canopy top elevation from the data collected by an airborne frequency-modulated continuous waveform profiling radar, Tomoradar. The estimated ground and canopy top elevations are critical for the derivation of reference information for the satellite-borne microwave radar data and the modeling of interaction between microwave radar signal and foliage. The methods of estimating the ground elevation and canopy top elevation from profiling radar are introduced, and the accuracy was evaluated via digital terrain model and Velodyne VLP-16 LiDAR integrated with the Tomoradar. To our knowledge, the ranging radar and the LiDAR data were simultaneously collected for the first time. The evaluation proved that the root-mean-square error (RMSE) of ground level estimation of the developed profiling radar can reach up to 0.33 m. When comparing the estimated canopy top peak elevation between the profile radar data and the LiDAR data, it was found that the side lobes of Tomoradar antenna system may produce undesired canopy backscatters when the size of the canopy gap is comparable to the footprint size of the main lobe, resulting in a higher canopy top elevation measurement from Tomoradar than that from LiDAR. The RMSE of the estimated canopy top peak elevation between two data sets was 0.32 m in the best case and 0.852 m on average. Moreover, the RMSE of point-to-point comparing the entire canopy tops elevation estimated from the data of two active remote sensing systems is 0.799 m after excluding the outliers.

Description: For airborne repeat-pass synthetic aperture radar interferometry (InSAR), precise trajectory information is needed to compensate for deviations of the platform movement from a linear track. Using the trajectory information, motion compensation (MoCo) can be implemented within SAR data focusing. Due to the inaccuracy of current navigation systems, residual motion errors (RMEs) exist between the real and measured trajectory, causing phase undulations in the final interferograms. Up to now, MoCo and RME estimation have usually been combined in airborne InSAR to estimate ground deformation. Conventional MoCo methods generally involve azimuthal and range resampling and phase correction. Then frequency-domain focusing techniques can be used to generate the SAR images. After focusing SAR images with MoCo, both multisquint and autofocus approaches can be used to estimate RME. In addition to the MoCo-based frequency-domain focusing, the time-domain backprojection (BP) technique can also focus the SAR data obtained from highly nonlinear platform trajectories. In this paper, we present, for the first time, the combination of BP and multisquint techniques for RME estimation. A detailed derivation of the implementation of the multisquint approach using the BP-focusing images is presented. Repeat-pass data from the SlimSAR system over Slumgullion landslide are used to demonstrate the feasibility of RME estimation for both stationary and nonstationary scenes. We conclude that the proposed method can effectively remove the RME.

Description: Transform sensing is proposed to sense the space through certain beam patterns designed for a transformation basis. The received signals will be the result of transformation instead of the original raw data. In doing so, the process of generating an arbitrary beam pattern on the phased array and multiple-input multiple-output is described. We further propose a transform-sensing mechanism using wavelets, and compare it with traditional sensing methods. Simulations and experiments demonstrate how to generate the transmission patterns and sense through the transform-sensing mechanism. The results show that transform sensing obtains high-resolution data on the target area, while spending less time on nontarget ones that need low resolutions. In this way, the new sensing mechanism generates a multiresolution result, which balances resolution and sensing efficiency.

Description: Focusing on open forests and woodlands within the Injune Landscape Collaborative Project research area in central southeast Queensland, Australia, and using dual-pol (HH and HV) ALOS PALSAR repeat-pass InSAR data (temporal baseline of 92 days), this paper explores the detection of forest disturbance from the spaceborne repeat-pass InSAR correlation magnitude by developing a simple and efficient forest disturbance detection approach. In particular, a generic physical InSAR scattering model is derived by accounting for the forest disturbance information as well as the normal temporal decorrelation effects that are later compensated for using the modified Random Volume over Ground model. Based on the generic model, a quantitative indicator of forest disturbance is retrieved, namely, disturbance index that varies from 0 (no disturbance) to 1 (complete deforestation). This index is compared with that identified using a time series of Landsat sensor data over a selective logging area and has a relative root mean square error of 13% at a spatial resolution of 0.8 ha. This paper highlights the use of the co-pol InSAR correlation magnitude for forest disturbance detection, which serves as a complimentary application to using the cross-pol counterpart for forest height inversion in a companion work. Given the global availability of this type of data (e.g., Japanese Aerospace Exploration Agency’s ALOS-1/2 and NASA-ISRO’s NISAR), the method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring.

Description: Robust and effective detection of small target and false alarm (FA) suppression are the key techniques in infrared search and track systems. In this paper, the derivative entropy-based contrast measure (DECM) is proposed for small-target detection under various complex background clutters. Initially, different directional derivatives of an infrared image are calculated based on the facet model. Then, by analyzing the derivative properties of small target, the primitive entropy formula is improved by incorporating derivative information. With the improved entropy, the contrast measure is constructed to enhance small target and suppress background clutters in each derivative subband. Finally, the contrast measure maps derived from derivative subbands are fused together. The small target could be segmented easily from the fusion result. Experimental results demonstrate that DECM could effectively enhance dim small targets and suppress complex background clutters. Besides, DECM is also robust to infrared small-target images with noises of different levels. The detection results achieve higher detection ratio and lower FA compared with those of other methods under various infrared scenes.

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Description: Wind profilers (WPs) are coherent pulsed Doppler radars operating in the ultrahigh frequency and very high frequency bands. These atmospheric radars receive backscattered signals from the atmospheric and nonatmospheric targets. Atmospheric targets include clear air turbulence, precipitation, mesospheric turbulence, ionospheric D, E, F regions, and meteors, whereas nonatmospheric targets are objects like airplanes, birds, insects, hills, and so on. Modern WPs operate for long hours and often change radar operating parameters. Echoes from one beam direction are observed for 8 to 32 s and a set of Doppler power spectra is generated. WPs generate more than a hundred sets of Doppler power spectra per hour. These Doppler spectra often contain echoes from more than one target. The studies for atmospheric modeling and prediction require analysis of the backscattered signal. In order to facilitate systematic study, the data need to be classified and archived according to the atmospheric target type. Considering the large data volume consisting of echoes from different atmospheric targets, there is a need of an automated classification technique that segregates the Doppler power spectral data according to the types of atmospheric targets. The technique is expected to operate in real time with the data generation. This paper presents a spectral feature-based classification (SFBC) method for the classification of Doppler power spectra. This method associates three to four descriptor spectral features with each type of atmospheric target. The SFBC method classifies the data into a particular type of the atmospheric target if concurrent occurrence of descriptor features corresponding to that target type is observed. This paper presents the results indicating that the SFBC classifies the data from different WP radars with good accuracy. It also shows that the SFBC method is computationally simpler compared with other established classification methods.

Description: For the first time, the performance of the range imaging technique of the very high frequency band middle and upper atmosphere (MU) radar (Shigaraki observatory, Japan), when using frequency diversity, is assessed. This is done by the detection of unmanned aerial vehicles (UAVs) operated near the radar during the Shigaraki UAV radar experiment campaign, carried out from June 1, 2015 to June 14, 2015. During this campaign devoted to the measurements of fine-scale turbulence and stability in the lower troposphere using the turbulence sensors on the DataHawk UAV and the MU radar, the detection of the small UAV by the MU radar provided an excellent opportunity for taking stock of the range imaging technique in the presence of a single hard target. It was found that range imaging reproduces a faithful image of the aircraft position and its displacements with an excellent accuracy (of the order of ~10 m), giving extra credence to the thin echo layers and their vertical displacements generally observed from the range imaging technique in stably stratified conditions.

Description: It is a feasible and promising way to utilize deep neural networks to learn and extract valuable features from synthetic aperture radar (SAR) images for SAR automatic target recognition (ATR). However, it is too difficult to effectively train the deep neural networks with limited raw SAR images. In this paper, we propose a new approach to do SAR ATR, in which a multiview deep learning framework was employed. Based on the multiview SAR ATR pattern, we first present a flexible mean to generate adequate multiview SAR data, which can guarantee a large amount of inputs for network training without needing many raw SAR images. Then, a unique deep convolutional neural network containing a parallel network topology with multiple inputs is adopted. The features of input SAR images from different views will be learned by the proposed network layer by layer; meanwhile, the learned features from the distinct views are fused in different layers progressively. Therefore, the proposed framework is able to achieve a superior recognition performance, and requires only a small number of raw SAR images for network training samples generation. Experimental results have shown the superiority of the proposed framework based on the Moving and Stationary Target Acquisition and Recognition data set.

Description: Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, notable progress has been made in scene classification and target detection. However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal .

Description: A two-stage sparse structure representation algorithm which can preserve the manifold structure of the data is proposed for synthetic aperture radar target configuration recognition in this paper. Manifold structure of the data is preserved by two stages. In the training stage, taking advantage of both the sparse representation (SR) and manifold learning, local structure of the data is preserved in the reconstruction space, where SR-based recognition is realized. In the testing stage, two structure preserving factors based on the testing samples are embedded into the SR model to enhance structure preserving performance. The first one is constructed to preserve the local structure of the testing samples, which can guarantee the samples that are close to each other in the original space will also be close to each other in the sparse space. And the second one is established to preserve the distant structure of the testing samples, which can ensure the samples that are far from each other in the original space will also be far from each other in the sparse space. Manifold structure of the data is well captured and preserved by two stages. Experimental results on the moving and stationary target acquisition and recognition database demonstrate the effectiveness of the proposed algorithm.

Description: Due to the very high orbit, the apparent features of geosynchronous synthetic aperture radar (GEOSAR) are the curved trajectory and long integration time, which can lead to severe coupling between the azimuth and the range directions and, therefore, complicates the resolution evaluation. The traditional analytical approach based on the 2-D division may produce large resolution error, and the numerical approach may suffer from huge computation burden. Therefore, an analytical resolution evaluation approach for GEOSAR based on the local feature of the ambiguity function is studied in this paper. The proposed approach is validated with simulation data to be of high efficiency and accuracy. In addition, the proposed approach is also demonstrated to be capable of evaluating the resolution for other complex platforms, and of evaluating the 3-D resolution of a SAR system.

Description: The accurate reconstruction of areas obscured by clouds is among the most challenging topics for the remote sensing community since a significant percentage of images archived throughout the world are affected by cloud covers which make them not fully exploitable. The purpose of this paper is to propose new methods to recover missing data in multispectral images due to the presence of clouds by relying on a formulation based on an autoencoder (AE) neural network. We suppose that clouds are opaque and their detection is performed by dedicated algorithms. The AE in our methods aims at modeling the relationship between a given cloud-free image (source image) and a cloud-contaminated image (target image). In particular, two strategies are developed: the first one performs the mapping at a pixel level while the second one at a patch level to take profit from spatial contextual information. Moreover, in order to fix the problem of the hidden layer size, a new solution combining the minimum descriptive length criterion and a Pareto-like selection procedure is introduced. The results of experiments conducted on three different data sets are reported and discussed together with a comparison with reference techniques.

Description: Differential interferometry using ground-based radar systems permits to monitor displacements in natural terrain with high flexibility in location, time of acquisition, and revisit time. In combination with polarimetric imaging, discrimination of different scattering mechanisms present in a resolution cell can be obtained simultaneously with the estimation of surface displacement. In this paper, we present the preprocessing steps and the calibration procedure required to produce high-quality calibrated polarimetric single-look complex imagery with KAPRI, a new portable Ku-band polarimetric radar interferometer. The processing of KAPRI data into single look complex images is addressed, including the correction of beam squint and of azimuthal phase variations. A polarimetric calibration model adapted to the acquisition mode is presented and used to produce calibrated polarimetric covariance matrix data. The methods are validated by means of a scene containing five trihedral corner reflectors. Data preprocessing is assessed by analyzing the oversampled response of a corner reflector, and the polarimetric calibration quality is verified by computing polarimetric signatures and residual calibration parameters.

Description: Soil moisture (SM) plays an important role in the land surface energy balance and water cycle. Microwave remote sensing has been applied widely to estimate SM. However, the application of such data is generally restricted because of their coarse spatial resolution. Downscaling methods have been applied to predict fine-resolution SM from original data with coarse spatial resolution. Commonly, SM is highly spatially variable and, consequently, such local spatial heterogeneity should be considered in a downscaling process. Here, a hybrid geostatistical approach, which integrates geographically weighted regression and area-to-area kriging, is proposed for downscaling microwave SM products. The proposed geographically weighted area-to-area regression kriging (GWATARK) method combines fine-spatial-resolution optical remote sensing data and coarse-spatial-resolution passive microwave remote sensing data, because the combination of both information sources has great potential for mapping fine-spatial-resolution near-surface SM. The GWATARK method was evaluated by producing downscaled SM at 1-km resolution from the 25-km-resolution daily AMSR-2 SM product. Comparison of the downscaled predictions from the GWATARK method and two benchmark methods on three sets of covariates with in situ observations showed that the GWATARK method is more accurate than the two benchmarks. On average, the root-mean-square error value decreased by 20%. The use of additional covariates further increased the accuracy of the downscaled predictions, particularly when using topography-corrected land surface temperature and vegetation–temperature condition index covariates.

Description: Seismic data interpolation and reconstruction play an important role in seismic data processing. Seismic data are often inadequately sampled along various spatial axes. We have developed a new approach to interpolate aliased multidimensional seismic data based on the multidimensional adaptive prediction-error filter in frequency domain. First, we estimate the adaptive prediction-error filter coefficients, then interpolate missing traces using the estimated coefficients. Shaping regularization is used to control the smoothness of frequency-domain multidimensional adaptive prediction-error filter coefficients. Instead of estimating prediction-error filter coefficients only along one direction space, we estimate the prediction-error filter coefficients using more information along different direction spaces. So, multidimensional adaptive prediction-error filter using regularized nonstationary autoregression can adaptively estimate seismic events whose slopes vary in multidimensional space. The frequency-domain multidimensional interpolation method can input data at temporal frequency, which can save computer memory and time. For multidimensional seismic data, which miss different number of traces regularly in different axes, the proposed method can be used to interpolate missing traces to obtain more accurate results. The proposed method improves the calculation efficiency by applying shaping regularization and implementation in the frequency domain. The applicability and effectiveness of the proposed method are examined by synthetic and field data examples.

Description: Most traditional approaches classify hyperspectral image (HSI) pixels relying only on the spectral values of the input channels. However, the spatial context around a pixel is also very important and can enhance the classification performance. In order to effectively exploit and fuse both the spatial context and spectral structure, we propose a novel two-stream deep architecture for HSI classification. The proposed method consists of a two-stream architecture and a novel fusion scheme. In the two-stream architecture, one stream employs the stacked denoising autoencoder to encode the spectral values of each input pixel, and the other stream takes as input the corresponding image patch and deep convolutional neural networks are employed to process the image patch. In the fusion scheme, the prediction probabilities from two streams are fused by adaptive class-specific weights, which can be obtained by a fully connected layer. Finally, a weight regularizer is added to the loss function to alleviate the overfitting of the class-specific fusion weights. Experimental results on real HSIs demonstrate that the proposed two-stream deep architecture can achieve competitive performance compared with the state-of-the-art methods.

Description: To monitor a space target, 3-D reconstruction from a multiview sequence of the inverse synthetic aperture radar (ISAR) imaging is developed. Scattering of a complex electric-large target, e.g., the ENVISAT satellite model, is numerically calculated, and multiview 2-D ISAR imaging can be simulated. Under the sparse sampling ISAR imaging via compressed sensing, the Kanade-Lucas–Tomasi feature tracker is applied to extraction of target feature points. Then, using the orthographic factorization method, 3-D reconstruction of those feature points is produced. A simple hexagonal frustum is first tested for the feasibility analysis. Two sequences of multiview ISAR imaging, one is the ENVISAT model and another real measurements of a space shuttle, are then presented for 3-D reconstruction. Furthermore, a complex multistructure model of the International Space Station is also studied from multiview ISAR imaging under different sparse sampling rates. All results demonstrate good feasibility of the 3-D reconstruction for those target components, e.g., solar panel and antenna.

Description: In this paper, the synthetic aperture radar (SAR) calibration for low-frequency missions by means of stable point targets is presented. Calibration at low frequency involves the absolute radiometric calibration, the antenna pattern and pointing characterization and validation, and the distortion system parameters’ estimation. The use of traditional instrumentation, such as a polarimetric active radar calibrator, a corner reflector, or an active transponder, may be costly and can reduce the time the instrument is used for operational acquisitions. The purpose of this paper is to evaluate the potentiality in calibration of point targets for which the radar cross section and the time stability have been characterized. Given a calibration site, once that a set of the stable point targets have been detected by the analysis of an interferometric stack of SAR acquisitions, they may be used as passive calibrators for the validation of radiometry, elevation antenna pattern, and pointing estimation. We show that, although less targets are expected to be found in P- or L- band than in C- or X-band, a sufficient amount (about 250 targets per acquisition) can provide an accuracy in antenna pattern estimation of about 0.04 dB, if the target accuracy is 0.1 dB at $1sigma$ .

Description: The performance of scene classification relies heavily on the spatial and structural features that are extracted from high spatial resolution remote-sensing images. Existing approaches, however, are limited in adequately exploiting latent relationships between scene images. Aiming to decrease the distances between intraclass images and increase the distances between interclass images, we propose a latent relationship learning framework that integrates an adaptive graph with the constraints of the feature space and label propagation for high-resolution aerial image classification. To describe the latent relationships among scene images in the framework, we construct an adaptive graph that is embedded into the constrained joint space for features and labels. To remove redundant information and improve the computational efficiency, subspace learning is introduced to assist in the latent relationship learning. To address out-of-sample data, linear regression is adopted to project the semisupervised classification results onto a linear classifier. Learning efficiency is improved by minimizing the objective function via the linearized alternating direction method with an adaptive penalty. We test our method on three widely used aerial scene image data sets. The experimental results demonstrate the superior performance of our method over the state-of-the-art algorithms in aerial scene image classification.

Description: Faraday rotation can be significant at L-band and needs to be considered in remote sensing from space using the spectrum window at 1.413 GHz protected for passive observations. This is especially so for a conical scanner such as SMAP because the variation of the rotation angle with position around the scan is of the same order of magnitude as the change with geographic position as the sensor travels in its orbit around the globe. Furthermore, the angle retrieved in situ by the radiometer is particularly noisy over land raising additional issues for remote sensing of soil moisture. Research is reported here assessing the magnitude of the problem and suggesting an approach for treating Faraday rotation in the context of remote sensing of soil moisture with a conical scanner like SMAP.

Description: In remote sensing image classification, active learning aims to learn a good classifier as best as possible by choosing the most valuable (informative and representative) training samples. Multiview is a concept that regards analyzing the same object from multiple different views. Generally, these views show diversity and complementarity of features. In this paper, we propose a new multiview active learning (MVAL) framework for hyperspectral image classification. First, we generate multiple views by extracting different attribute components from the same image data. Specifically, we adopt the multiple morphological component analysis to decompose the original image into multiple pairs of attribute components, including content , coarseness , contrast , and directionality , and the smooth component from each pair is chosen as one single view. Second, we construct two multiview intensity-based query strategies for active learning. On the one hand, we exploit the intensity differences of multiple views along with the samples’ uncertainty to choose the most informative candidates. On the other hand, we consider the clustering distribution of all unlabeled samples, and query the most representative candidates in addition to the highly informative ones. Our experiments are performed on four benchmark hyperspectral image data sets. The obtained results show that the proposed MVAL framework can lead to better classification performance than the traditional, single-view active learning schemes. In addition, compared with the conventional disagree-based MVAL scheme, the proposed query selection strategies show competitive classification accuracy.

Description: The concept of information fusion has gained a widespread interest in many fields due to its complementary properties. It makes systems more robust against uncertainty. This paper presents a new approach for the well-logging estimation problem by using a fusion methodology. The natural gamma-ray tool (NGT) is considered as an important instrument in the well logging. The NGT detects changes in natural radioactivity emerging from the variations in concentrations of micronutrients as uranium (U), thorium (Th), and potassium (K). The main goal of this paper is to have precise estimation of the concentrations of $U$ , $Th$ , and $K$ . Four types of Kalman filters are designed to estimate the elements using the NGT sensor. Then, a fusion of the Kalman filters is utilized into an integrated framework by an ordered weighted averaging (OWA) operator to enhance the quality of the estimations. A real covariance of the output error based on the innovation matrix is utilized to design weighting factors for the OWA operator. The simulation studies indicate not only a reliable performance of the proposed method compared with the individual Kalman filters but also a better response in contrast with previous fusion methodologies.

Description: Since it is usually difficult and time-consuming to obtain sufficient training samples by manually labeling, feature extraction, which investigates the characteristics of hyperspectral images (HSIs), such as spectral continuity and spatial locality of surface objects, to achieve the most discriminative feature representation, is very important for HSI classification. Meanwhile, due to the spatial regularity of surface materials, it is desirable to improve the classification performance of HSIs from the superpixel viewpoint. In this paper, we propose a novel local binary pattern (LBP)-based superpixel-level decision fusion method for HSI classification. The proposed framework employs uniform LBP (ULBP) to extract local image features, and then, a support vector machine is utilized to formulate the probability description of each pixel belonging to every class. The composite image of the first three components extracted by a principal component analysis from the HSI data is oversegmented into many homogeneous regions by using the entropy rate segmentation method. Then, a region merging process is applied to make the superpixels obtained more homogeneous and agree with the spatial structure of materials more precisely. Finally, a probability-oriented classification strategy is applied to classify each pixel based on superpixel-level guidance. The proposed framework “ULBP-based superpixel-level decision fusion framework” is named ULBP-SPG. Experimental results on two real HSI data sets have demonstrated that the proposed ULBP-SPG framework is more effective and powerful than several state-of-the-art methods.

Description: Global Navigation Satellite System (GNSS) radio occultation (RO) has been widely used in the prediction of weather, climate, and space weather, particularly in the area of tropospheric analyses. However, one of the issues with GNSS RO measurements is that they are interfered with by the signals reflected from the earth’s surface. Many RO events are subject to such interfered GNSS measurements, which are considerably difficult to extract from the GNSS RO measurements. To precisely identify interfered RO events, an improved machine learning approach—a gradient descent artificial neural network (ANN)-aided radio-holography method—is proposed in this paper. Since this method is more complex than most other machine learning methods, for improving its efficiency through the reduction in computational time for near-real-time applications, a scale factor and a regularization factor are also adjusted in the ANN approach. This approach was validated using Constellation Observing System for Meteorology, Ionosphere, and Climate/FC-3 atmPhs (level 1b) data during the period of day of year 172–202, 2015, and its detection results were compared with the flag data set provided by Radio Occultation Meteorology Satellite Application Facilities for the performance assessment and validation of the new approach. The results were also compared with those of the support vector machine method for improvement assessment. The comparison results showed that the proposed method can considerably improve both the success rate of GNSS RO reflection detection and the computational efficiency.

Description: A technique using satellite-link signal attenuation measurements for estimating rainfall accumulation along the link path is evaluated. Power law relationships between attenuation rate $A$ and rainfall rate $R$ are used to estimate $R$ and rainfall accumulation with a satellite link operating at Ku-band (12.3 GHz). Polarimetric radar measurements obtained from a National Weather Service Weather Surveillance Radar—1988 Doppler system near State College, Pennsylvania, are utilized to provide a comparison of rainfall accumulation estimates. A tipping-bucket rain gauge, colocated with the satellite receiver, is also used for comparison. A method based on bit error ratio measurements for the satellite link is used to identify periods of rain during which the rainfall rate is estimated from signal attenuation measurements. The effective rain height used in converting the attenuation rate along the link path into the rainfall rate is estimated from polarimetric radar observations. The Ku-band link is not very sensitive to light rain below 1.5 mm/h. Rainfall accumulation estimates obtained for 11 different days using satellite link attenuation show good comparisons with radar (within 19%) for accumulations greater than 6 mm and not so good (within 43%) for accumulations below 3 mm. The results presented in this paper show that using satellite-link attenuation measurements to estimate rainfall accumulations is a promising technique that requires further testing and refinement.

Description: Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists for some of the classes. ZSL aims to build a recognition model for new unseen categories by relating them to seen classes that were previously learned. We establish this relation by learning a compatibility function between image features extracted via a convolutional neural network and auxiliary information that describes the semantics of the classes of interest by using training samples from the seen classes. Then, we show how knowledge transfer can be performed for the unseen classes by maximizing this function during inference. We introduce a new data set that contains 40 different types of street trees in 1-ft spatial resolution aerial data, and evaluate the performance of this model with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information. The experiments show that the proposed model achieves 14.3% recognition accuracy for the classes with no training examples, which is significantly better than a random guess accuracy of 6.3% for 16 test classes, and three other ZSL algorithms.

Description: A cross-borehole pulse radar system was operated to detect an intrusive man-made tunnel terminated just 1.2 m away from the line of sight between a newly drilled borehole pair at a tunnel test site. Unlike conventional radar signatures on a fully penetrated air-filled tunnel, the relatively fast arrival in the measured time-of-arrival (TOA) profile was highly suppressed at the depth of the terminated tunnel. To analyze the TOA contraction at a terminated tunnel without drilling additional borehole pairs, a finite-difference time-domain (FDTD) simulator is implemented using the accurately measured location information on the terminated tunnel and borehole pair. The relation curves between the time advance in the TOA profile and the penetration length of the terminated tunnel are plotted in the high and low limits of electrical properties of background rock. To verify the accuracy of our FDTD simulated results, the wideband complex permittivity profiles of the core rock samples’ boring at the tunnel test site are measured using an open-ended coaxial probe method. The calculated time advances agree well with the measured values in both cases of fully penetrated and closely terminated borehole pairs in the test site. The presented time advance curves for various penetration lengths will be a valuable guideline on detection of a terminated tunnel in site.

Description: Recently, many nonlinear spectral unmixing algorithms that use various bilinear mixture models (BMMs) have been proposed. However, the high computational complexity and intrinsic collinearity between true endmembers and virtual endmembers considerably decrease these algorithms’ unmixing performances. In this paper, we come up with a novel abundance estimation algorithm based on the BMMs. Motivated by BMMs’ geometric characteristics that are related to collinearity, we conduct a unique nonlinear vertex ${p}$ to replace all the virtual endmembers. Unlike the virtual endmembers, this vertex ${p}$ actually works as an additional true endmember that gives affine representations of pixels with other true endmembers. When the pixels’ normalized barycentric coordinates with respect to true endmembers are obtained, they will be directly projected to be their approximate linear mixture components, which removes the collinearity effectively and enables further linear spectral unmixing. After that, based on the analysis of projection bias, two strategies using the projected gradient algorithm and a traditional linear spectral unmixing algorithm, respectively, are provided to correct the bias and estimate more accurate abundances. The experimental results on simulated and real hyperspectral data show that the proposed algorithm performs better compared with both traditional and state-of-the-art spectral unmixing algorithms. Both the unmixing accuracy and speed have been improved.

Description: Given the improvement of synthetic aperture radar (SAR) imaging technologies, the resolution of SAR image is largely improved and the variation of backscatter amplitude should be considered in SAR image processing. In this paper, considering the spatial geometric properties of SAR image in gray pixel space and the sample selection in the estimation of true signal, local directional property of each pixel is explored with the help of SAR sketching method, and two specially designed filters are integrated for adaptive speckle reduction of SAR images. Specifically, based on the sketch map of a SAR image, the orientation of the sketch point lying at each sketch segment is assigned to the corresponding pixel, and thus all pixels of the SAR image are classified as the directional pixels and the nondirectional pixels. For the directional pixels, given the significant directionality of its neighborhood, a geometric-structural block (GB) is built to center on it and GB-wised nonlocal means filter is designed to estimate the true values of all pixels contained in the GB. Moreover, using the local orientation, the whole image is adopted as the searching range to search the similar GBs. For the nondirectional pixels, based on the locally estimated equivalent number of looks, a novel pixel-based metric is proposed to determine the local adaptive neighborhood (AN) with which an AN-based filter is developed to estimate its true value. Besides, since some nondirectional pixels are contained in GBs, a Bayesian-based fusion strategy is designed for the fusion of their estimated values. In the experiments, three synthetic speckled images and five real SAR images [obtained with different resolutions (e.g., 3, 1, and 0.1 m) and different bands (e.g., X-band, C-band, and Ka-band)] are used for evaluation and analysis. Owing to the usage of local spatial geometric property and the combination of two different filters, the proposed method shows a reas- nable performance among the comparison methods, in terms of the speckle reduction and the details’ preservation.

Description: In this paper, we present the results of an ~5-h airborne gamma-ray survey carried out over the Tyrrhenian Sea in which the height range (77–3066) m has been investigated. Gamma-ray spectroscopy measurements have been performed using the AGRS_16L detector, a module of four 4L NaI(Tl) crystals. The experimental setup was mounted on the Radgyro, a prototype aircraft designed for multisensorial acquisitions in the field of proximal remote sensing. By acquiring high-statistics spectra over the sea (i.e., in the absence of signals having geological origin) and by spanning a wide spectrum of altitudes, it has been possible to split the measured count rate into a constant aircraft component and a cosmic component exponentially increasing with increasing height. The monitoring of the count rate having pure cosmic origin in the >3-MeV energy region allowed to infer the background count rates in the 40 K, 214 Bi, and 208 Tl photopeaks, which need to be subtracted in processing airborne gamma-ray data in order to estimate the potassium, uranium, and thorium abundances in the ground. Moreover, a calibration procedure has been carried out by implementing the CARI-6P and Excel-based program for calculating atmospheric cosmic ray spectrum dosimetry tools, according to which the annual cosmic effective dose to human population has been linearly related to the measured cosmic count rates.

Description: Striping effects are a common phenomenon in remote-sensing imaging systems, and they can exhibit considerable differences between different sensors. Such artifacts can greatly degrade the quality of the measured data and further limit the subsequent applications in higher level remote-sensing products. Although a lot of destriping methods have been proposed to date, a few of them are robust to different types of stripes. In this paper, we conduct a thorough feature analysis of stripe noise from a novel perspective. With regard to the problem of striping diversity and complexity, we propose a universal destriping framework. In the proposed destriping procedure, a 1-D variational method is first designed and utilized to estimate the statistical feature-based guidance. The guidance information is then incorporated into 2-D optimization to control the image estimation for a reliable and clean output. The iteratively reweighted least-squares method and alternating direction method of multipliers are exploited in the proposed approach to solve the minimization problems. Experiments under various cases of simulated and real stripes confirm the effectiveness and robustness of the proposed model in terms of the qualitative and quantitative comparisons with other approaches.

Description: The TanDEM-X mission is the first free flying bistatic SAR mission. It has the primary objective to generate within a short time frame a global digital elevation model (DEM) of 10-m absolute vertical accuracy and 2-m relative height accuracy. For that, the whole land mass has been mapped at least twice with different baselines. The success of the mission depends on the accuracy of the final DEM and therefore on the reliability of the phase unwrapping (PU) algorithm. Hence, a robust and versatile PU method, which is in accordance with the acquisition concept, is necessary. This paper presents a new method that combines bistatic high-resolution interferometric data in order to perform an accurate PU on a huge amount of data. The dual-baseline PU correction (DB-PUC) framework addresses this challenge by correcting errors that occurred during the single-baseline PU procedure. It benefits from the additional information available through the differential interferogram and the stereo-radargrammetric phase , which are used to correct region-wise the ambiguity bands of the misestimated unwrapped phases to be less sensitive to noise and possible temporal changes. The multilevel of the DB-PUC approach makes it flexible, computationally efficient, and well adapted to deal with the various PU error scenarios. This framework is used operationally for the processing of the data of the TanDEM-X mission.

Description: The existence of shadows in very high resolution satellite images obstructs image interpretation and the following applications, such as target detection and recognition. Traditional shadow detection methods consider only the pixel-level properties, such as color and intensity of image pixels, and thus, may produce errors around object boundaries. To overcome this problem, a novel shadow detection algorithm based on extended random walker (ERW) is proposed by jointly integrating both shadow property and spatial correlations among adjacent pixels. First, a set of training samples is automatically generated via an improved Otsu-based thresholding method. Then, the support vector machine is applied to obtain an initial detection map, which categorizes all the pixels in the scene into shadow and nonshadow. Finally, the initial detection map is refined with the ERW model, which can simultaneously characterize the shadow property and spatial information in satellite images to further improve shadow detection accuracy. Experiments performed on five real remote sensing images demonstrate the superiority of the proposed method over several state-of-the-art methods in terms of detection accuracy.

Description: This paper develops a framework based on enhanced shadow-aided decision for multichannel synthetic aperture radar-based ground moving target indication system according to the relationships between the moving target and its shadow information in position, dimensions, and intensity. As a sort of feature information available, the moving target shadow may improve the ground target detection performance. A critical precondition for shadow utilization is to obtain the good detection performance for the moving target shadow. However, shadow detection performance will deteriorate inevitably as a result of target motion that blurs its shadow. To address this issue, a knowledge-aided shadow detection algorithm with adaptive thresholds is proposed to improve the shadow detection performance in the developed framework. Furthermore, the theoretical performance analysis is performed, which indicates that the proposed knowledge-aided shadow detection algorithm has a better performance than that of the conventional shadow detection algorithm with a fixed threshold. Finally, numerical simulation experiments are presented to demonstrate that the developed framework can obtain good results for extended ground moving target detection.

Description: A new iterative inversion algorithm is proposed to reconstruct the electrical conductivity profile in a stratified underground medium for the grounded electrical source airborne transient electromagnetic (GREATEM) system. In forward modeling, we simplify the mathematical expressions of the magnetic fields generated by a finite line source in the layered ground to semianalytical forms in order to save the computation time. The Fréchet derivative is derived for the electromagnetic response at the receivers due to a small perturbation of the conductivity in a certain layer underground. The initial expression of the Fréchet derivative has an expensive triple integral and contains the Bessel function in the integrand. It is simplified by partially eliminating the integration along the source line and deriving the analytical expression for the integration in the vertical direction inside the perturbed layer. In the inverse solution, we use the distorted Born iterative method (DBIM). This is the first time that the DBIM is applied to data measured by the GREATEM system. Besides, the forward and inverse procedures are carried out in the frequency domain and based on the Fréchet derivative of a line source. We demonstrate the validity of our forward model, Fréchet derivative, inverse model, and the precision as well as robustness of the inversion algorithm through numerical computation and comparisons. Finally, we apply the inversion algorithm to the measured data and compare the retrieved conductivity to the actual drilling data.

Description: Long integration time (LIT) indicates high resolution and/or large scene for spaceborne synthetic aperture radar (SAR) imaging and also means that the effects, brought by curved orbit, cannot be ignored. In this paper, considering the curved orbit caused by the relative motion between an SAR sensor in orbit and targets on a rotating planetary surface, the impacts of the LIT on the imaging results are discussed in detail. The analysis suggests that the cross-coupling phase is two-dimensional (2-D) with spatial variation. Employing the 2-D Taylor series expansion, the 2-D linear relationships between the spatially variant and invariant coefficients are derived, which are exploited to improve the echo formulation. Then, we apply the keystone transform (KT) to process the LIT spaceborne SAR data. Unlike the traditional application of the KT, our two proposed methods, which operate, respectively, in azimuth time and azimuth frequency domains, can greatly remove the spatially variant cross-coupling phase. Moreover, implementation considerations including the curved orbit of LIT spaceborne SAR, applicability of two methods, postprocessing for topography error compensation, and computational load are discussed. Simulation results verify the effectiveness of the developed focusing approaches.

Description: An oil-in-sea ice mesocosm experiment was conducted at the University of Manitoba Sea-Ice Environmental Research Facility from January to March 2016 in which geophysical and electromagnetic parameters of the ice were measured, and general observations about the oil-contaminated ice were made. From the experimental measurements, the presence of crude oil appears to affect the temperature and bulk salinity profiles as well as the normalized radar cross section (NRCS) of the contaminated young sea ice. The measured temperature and bulk salinity profiles of the ice, as well as the crude oil distribution within the ice, were used to model the permittivity profile of the oil-contaminated ice by adapting two mixture models commonly used to describe sea ice to account for the presence of oil. Permittivity modeling results were used to simulate the NRCS of the oil-contaminated sea ice in an effort to determine the accuracy of the models. In addition, the application of X-ray microtomography in modeling the dielectric profile of oil-contaminated sea ice was examined. The sensitivity of the permittivity models for oil-contaminated sea ice to changes in temperature, frequency, and oil volume fraction was also examined.

Description: As a list of remotely sensed data sources is available, how to efficiently exploit useful information from multisource data for better Earth observation becomes an interesting but challenging problem. In this paper, the classification fusion of hyperspectral imagery (HSI) and data from other multiple sensors, such as light detection and ranging (LiDAR) data, is investigated with the state-of-the-art deep learning, named the two-branch convolution neural network (CNN). More specific, a two-tunnel CNN framework is first developed to extract spectral-spatial features from HSI; besides, the CNN with cascade block is designed for feature extraction from LiDAR or high-resolution visual image. In the feature fusion stage, the spatial and spectral features of HSI are first integrated in a dual-tunnel branch, and then combined with other data features extracted from a cascade network. Experimental results based on several multisource data demonstrate the proposed two-branch CNN that can achieve more excellent classification performance than some existing methods.

Description: As one of the most challenging tasks of remote sensing big data mining, large-scale remote sensing image retrieval has attracted increasing attention from researchers. Existing large-scale remote sensing image retrieval approaches are generally implemented by using hashing learning methods, which take handcrafted features as inputs and map the high-dimensional feature vector to the low-dimensional binary feature vector to reduce feature-searching complexity levels. As a means of applying the merits of deep learning, this paper proposes a novel large-scale remote sensing image retrieval approach based on deep hashing neural networks (DHNNs). More specifically, DHNNs are composed of deep feature learning neural networks and hashing learning neural networks and can be optimized in an end-to-end manner. Rather than requiring to dedicate expertise and effort to the design of feature descriptors, we can automatically learn good feature extraction operations and feature hashing mapping under the supervision of labeled samples. To broaden the application field, DHNNs are evaluated under two representative remote sensing cases: scarce and sufficient labeled samples. To make up for a lack of labeled samples, DHNNs can be trained via transfer learning for the former case. For the latter case, DHNNs can be trained via supervised learning from scratch with the aid of a vast number of labeled samples. Extensive experiments on one public remote sensing image data set with a limited number of labeled samples and on another public data set with plenty of labeled samples show that the proposed remote sensing image retrieval approach based on DHNNs can remarkably outperform state-of-the-art methods under both of the examined conditions.

Description: This paper investigated the sensitivity of passive microwave L-band soil moisture (SM) retrieval from multiangle airborne brightness temperature data obtained under morning and afternoon conditions from the National Airborne Field Experiment conducted in southeast Australia in 2006. Ground measurements at a dryland focus farm including soil texture, soil temperature, and vegetation water content were used as ancillary data to drive the retrieval model. The derived SM was then in turn evaluated with the ground-measured near-surface SM patterns. The results of this paper show that the Soil Moisture and Ocean Salinity target accuracy of 0.04 $text{m}^{3}cdot text{m}^{-3}$ for single-SM retrievals is achievable irrespective of the 6 A.M. and 6 P.M. overpass acquisition times for moisture conditions $le 0.15~text{m}^{3}cdot text{m}^{-3}$ . Additional tests on the use of the air temperature as proxy for the vegetation temperature also showed no preference for the acquisition time. The performance of multiparameter retrievals of SM and an additional parameter proved to be satisfactory for SM modeling—independent of the acquisition time—with root-mean-square errors less than 0.06 $text{m}^{3}cdot text{m}^{-3}$ for the focus farm.

Description: When dealing with forest scenario, target scattering separation using synthetic aperture radar (SAR) tomography is a challenging task for the application of biophysical parameter retrieval approaches. One important and widely popular solution used to investigate the scattering mechanism separation based on multipolarimetric multibaseline (MPMB) SAR data is the sum of Kronecker products (SKPs), which provides the basis for decomposition of the data into ground-only and canopy-only contributions. In this paper, we investigate the possibility of characterizing multiple scattering mechanisms using the SKPs of covariance matrix. In particular, we present a method for characterization of forest structure using MPMB data that adapt SKP with the generalized volume description and the physical model of interferometric cross correlation as the sum of scattering contributions. According to the Freeman–Durden model, the method expresses the estimated covariance matrix in terms of the Kronecker product of polarimetric and interferometric coherence matrices corresponding to direct, double-bounce, and random-volume scattering mechanisms. The proposed method is tested with simulated and P-band MB data acquired by ONERA over a tropical forest in French Guiana in the frame of the European Space Agency’s campaign TROPISAR. Comparison of the retrieved height of trees with a LiDAR-based canopy model as a reference showed that the proposed method has the potential to decrease root-mean-square error of forest height by up to 3.9 m with respect to SKP.

Description: Pansharpening usually refers to the fusion of a high spatial resolution panchromatic (PAN) image with a higher spectral resolution but coarser spatial resolution multispectral (MS) image. Owing to the wide applicability of related products, the literature has been populated by many papers proposing several approaches and studies about this issue. Many solutions require a preliminary spectral matching phase wherein the PAN image is matched with the MS bands. In this paper, we propose and properly justify a new approach for performing this step, demonstrating that it yields state-of-the-art performance. The comparison with existing spectral matching procedures is performed by employing four data sets, concerning different kinds of landscapes, acquired by the Pléiades, WorldView-2, and GeoEye-1 sensors.

Description: This paper describes an experiment that was carried out in the North Sea off the Sylt island in May 2012 with the aim to study the influence of the maritime boundary layer conditions on the propagation of radar signals under low grazing angle geometry and to establish a sea clutter database at different frequencies with a view to contribute to new sea clutter models. The radar measurements were carried out with the highly versatile radar called MEMPHIS operating in sea configuration at X-, Ka-, and W-band, simultaneously. As concerns the oceanographic and atmospheric characterization, the collection of measurements was done with a sophisticated suite of sensors partly mounted on the research vessel (RV) Elisabeth Mann Borgese (EMB) and onboard different types of buoys, a catamaran, and a tethered balloon. Over a period of four days, a comprehensive and valuable data set was successfully collected including clutter measurements under different geometrical configurations and propagation runs with corner reflectors mounted onboard RV EMB. An insight into the overall approach is given together with many measurement examples for a very detailed oceanographic and meteorological characterization and a vast number of multifrequency radar acquisitions, showing the complexity of different parameters that have to be considered for sensor performance assessment and prediction.

Description: The cross-track infrared sounder has been operated in the full spectral resolution (FSR) mode since December 4, 2014. To provide the FSR radiance spectra with a spectral resolution of 0.625 cm −1 for all the three bands, a new calibration algorithm has been developed and implemented for operational uses. The algorithm is an improvement over the previous algorithm that had been operationally used until March 2017. Major changes include the calibration equation, self-apodization correction and resampling matrices, and calibration filter. Compared to the previous algorithm, the improvement reduces the calibration inconsistencies among the nine fields of view and between the forward and reverse interferometer sweep directions by up to 0.5 K, and the differences between observed and simulated spectra by up to 0.4 K.

Description: In this paper, we designed an end-to-end spectral–spatial residual network (SSRN) that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification. In this network, the spectral and spatial residual blocks consecutively learn discriminative features from abundant spectral signatures and spatial contexts in hyperspectral imagery (HSI). The proposed SSRN is a supervised deep learning framework that alleviates the declining-accuracy phenomenon of other deep learning models. Specifically, the residual blocks connect every other 3-D convolutional layer through identity mapping, which facilitates the backpropagation of gradients. Furthermore, we impose batch normalization on every convolutional layer to regularize the learning process and improve the classification performance of trained models. Quantitative and qualitative results demonstrate that the SSRN achieved the state-of-the-art HSI classification accuracy in agricultural, rural–urban, and urban data sets: Indian Pines, Kennedy Space Center, and University of Pavia.

Description: Within the European Space Agency Climate Change Initiative (CCI) project Aerosol_cci, there are three aerosol optical depth (AOD) data sets of Advanced Along-Track Scanning Radiometer (AATSR) data. These are obtained using the ATSR-2/ATSR dual-view aerosol retrieval algorithm (ADV) by the Finnish Meteorological Institute, the Oxford-Rutherford Appleton Laboratory (RAL) Retrieval of Aerosol and Cloud (ORAC) algorithm by the University of Oxford/RAL, and the Swansea algorithm (SU) by the University of Swansea. The three AOD data sets vary widely. Each has unique characteristics: the spatial coverage of ORAC is greater, but the accuracy of ADV and SU is higher, so none is significantly better than the others, and each has shortcomings that limit the scope of its application. To address this, we propose a method for converging these three products to create a single data set with higher spatial coverage and better accuracy. The fusion algorithm consists of three parts: the first part is to remove the systematic errors; the second part is to calculate the uncertainty and fusion of data sets using the maximum likelihood estimate method; and the third part is to mask outliers with a threshold of 0.12. The ensemble AOD results show that the spatial coverage of fused data set after mask is 148%, 13%, and 181% higher than those of ADV, ORAC, and SU, respectively, and the root-mean-square error, mean absolute error, mean bias error, and relative mean bias are superior to those of the three original data sets. Thus, the accuracy and spatial coverage of the fused AOD data set masked with a threshold of 0.12 are improved compared to the original data set. Finally, we discuss the selection of mask thresholds.

Description: The WG $Gamma$ model has been validated as an effective model for the characteristic of polarimetric synthetic aperture radar (PolSAR) data statistics. However, due to the complexity of natural scene and the influence of coherent wave, the WG $Gamma$ model still needs to be improved to fully consider the polarimetric information. Then, we propose the WG $Gamma$ mixture model (WG $Gamma$ MM) for PolSAR data to maintain the correlations among statistics in PolSAR data. To further consider the spatial-contextual information in PolSAR image classification, we propose a novel mixture model, named mixture WG $Gamma$ -Markov random field (MWG $Gamma$ -MRF) model, by introducing the MRF to improve the WG $Gamma$ MM model for classification. In each law of the MWG $Gamma$ -MRF model, the interaction term based on the edge penalty function is constructed by the edge-based multilevel-logistic model, while the likelihood term being constructed by the WG $Gamma$ model, so that each law of the MWG $Gamma$ -MRF model can achieve an energy function and has its contribution to the inference of attributive class. Then, the mixture energy function of the MWG $Gamma$ -MRF model has the fusion of the weig- ted component, given the energy functions of every law. The mixture coefficient and the corresponding mean covariance matrix of the MWG $Gamma$ -MRF model are estimated by the expectation-maximization algorithm, while the parameters of the WG $Gamma$ model being estimated by the method of matrix log-cumulants. Experiments on simulated data and real PolSAR images demonstrate the effectiveness of the MWG $Gamma$ -MRF model and illustrate that it can provide strong noise immunity, get smoother homogeneous areas, and obtain more accurate edge locations.

Description: In this paper, a new method is proposed to refocus a ground moving target in synthetic aperture radar imagery. In this method, range migration is compensated in the 2-D frequency domain, which can easily be implemented by using the complex multiplications, the fast Fourier transform (FFT), and the inverse FFT operations. Then, the received target signal in a range gate is characterized as a quadratic frequency-modulated (QFM) signal. Finally, a novel parameter estimation method, i.e., scaled generalized high-order ambiguity function (HAF), is proposed to transform the target signal into a signal on 2-D time–frequency plane and realize the 2-D coherent integration, where the peak position accurately determines the second- and third-order parameters of a QFM signal. Compared with our previously proposed generalized Hough-HAF method, the proposed method can obtain a better target focusing performance, since it can eliminate the incoherent operations in both range and azimuth directions. In addition, the proposed method is computationally efficient, since it is free of searching in the whole target focusing procedure. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

Description: Light detection and ranging (lidar)-derived elevation data are commonly subjected to outliers due to the boundaries of occlusions, physical imperfections of sensors, and surface reflectance. Outliers have a serious negative effect on the accuracy of digital elevation models (DEMs). To decrease the impact of outliers on DEM construction, we propose a robust interpolation algorithm of multiquadric (MQ) based on a regularized least absolute deviation (LAD) technique. The objective function of the proposed method includes a regularization-based smoothing term and an LAD-based fitting term, respectively, used to smooth noisy samples and resist the influence of outliers. To solve the objective function of the proposed method, we develop a simple scheme based on the split-Bregman iteration algorithm. Results from simulated data sets indicate that when sample points are noisy or contaminated by outliers, the proposed method is more accurate than the classical MQ and two recently developed robust algorithms of MQ for surface modeling. Real-world examples of interpolating 1 private and 11 publicly available airborne lidar-derived data sets demonstrate that the proposed method averagely produces better results than two promising interpolation methods including regularized spline with tension (RST) and gridded data-based robust thin plate spline (RTPS). Specifically, the image of RTPS is too smooth to retain terrain details. Although RST can keep subtle terrain features, it is distorted by some misclassified object points (i.e., pseudooutliers). The proposed method obtains a good tradeoff between resisting the effect of outliers and preserving terrain features. Overall, the proposed method can be considered as an alternative for interpolating lidar-derived data sets potentially including outliers.

Description: The time–frequency analysis tools, which are very useful for anomaly identification, reservoir characterization, seismic data processing, and interpretation, are widely used in discrete signal analysis. Among these methods, the generalized S transform (GST) is more flexible, because its analytical window can be self-adjusted according to the local frequency components of the selected discrete signal, besides there exist another two adjustable parameters to make it superior to the S transform (ST). But the amplitude-preserving ability is a little poor near the boundary because the analytical windows do not satisfy the partition of unity, which is a sufficient condition for amplitude-preserving time–frequency transforms. In order to make the GST with the amplitude-preserving ability, we first design a new analytical window, and then propose an amplitude-preserving GST (APGST), but with a higher computational cost. To accelerate the APGST, we provide two strategies: the 3 $sigma$ criterion in the probability theory is introduced to accelerate the analytical windows summation and a convolution operator is derived to accelerate the time integral or summation, which generates an efficient APGST (EAPGST). Finally, the proposed EAPGST is used for seismic data attenuation compensation to improve the vertical resolution. Detailed numerical examples are used to demonstrate the validity of the proposed EAPGST in amplitude preserving and high efficiency. Field data attenuation compensation result further proves its successful application in improving the vertical resolution. Besides, the proposed EAPGST can be easily extended into other applications in discrete signal analysis, and remote-sensing and seismology fields.

Description: We focus on the detection of sporadic low-power acoustic/seismic signals of unknown structure and statistics, such as the detection of sound produced by marine mammals, low-power underground signals, or the discovery of events such as volcano eruptions. In these cases, since the ambient noise may be fast time varying and may include many noise transients, threshold-based detection may lead to a significant false alarm rate. Instead, we propose a detection scheme that avoids the use of a decision threshold. Our method is based on clustering the samples of the observed buffer according to a binary hidden Markov model to discriminate between “noise” and “signal” states. Our detector is a modification of the Baum–Welch algorithm that takes into account the expected continuity of the desired signal and obtains a robust detection using the complex but flexible general Gaussian mixture model. The result is a combination of a constrained expectation-maximization algorithm with the Viterbi algorithm. We evaluate the performance of our scheme in numerical simulations, in a seimic test, and in an ocean experiment. The results are close to the hybrid Cramér–Rao lower bound and show that, at the cost of some additional complexity, our proposed algorithm outperforms common benchmark methods in terms of detection and false alarm rates, and also achieves a better accuracy of the time of detection. To allow reproducibility of the results, we publish our code.

Description: A new algorithm called the Mondrian detector has been developed for object detection in high-frequency synthetic aperture sonar (SAS) imagery. If a second (low) frequency-band image is available, the algorithm can seamlessly exploit the additional information via an auxiliary prescreener test. This flexible single-band and multiband functionality fills an important capability gap. The algorithm’s overall prescreener component limits the number of potential alarms. The main module of the method then searches for areas that pass a subset of pixel-intensity tests. A new set of reliable classification features has also been developed in the process. The overall framework has been kept uncomplicated intentionally in order to facilitate performance estimation, to avoid requiring dedicated training data, and to permit delayed real-time detection at sea on an autonomous underwater vehicle. The promise of the new algorithm is demonstrated on six substantial data sets of real SAS imagery collected at various geographical sites that collectively exhibit a wide range of diverse seafloor characteristics. The results show that—as with Mondrian’s art—simplicity can be powerful.

Description: Image change detection (CD) is a challenging problem, particularly when images come from different sensors. In this paper, we present a novel and reliable CD model, which is first based on the estimation of a robust similarity-feature map generated from a pair of bitemporal heterogeneous remote sensing images. This similarity-feature map, which is supposed to represent the difference between the multitemporal multisensor images, is herein defined, by specifying a set of linear equality constraints, expressed for each pair of pixels existing in the before-and-after satellite images acquired through different modalities. An estimation of this overconstrained problem, also formulated as a nonlocal pairwise energy-based model, is then carried out, in the least square sense, by a fast linear-complexity algorithm based on a multidimensional scaling mapping technique. Finally, the fusion of different binary segmentation results, obtained from this similarity-feature map by different automatic thresholding algorithms, allows us to precisely and automatically classify the changed and unchanged regions. The proposed method is tested on satellite data sets acquired by real heterogeneous sensor, and the results obtained demonstrate the robustness of the proposed model compared with the best existing state-of-the-art multimodal CD methods recently proposed in the literature.

Description: Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine-learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to the state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for large-scale applications, and constitutes the main obstacle precluding wide adoption. This paper tackles this problem by introducing two novel efficient methodologies for GP classification. We first include the standard random Fourier features approximation into GPC, which largely decreases its computational cost and permits large-scale remote sensing image classification. In addition, we propose a model which avoids randomly sampling a number of Fourier frequencies and alternatively learns the optimal ones within a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery and infrared sounding data. Excellent empirical results support the proposal in both computational cost and accuracy.

Description: Prestack seismic inversion is an ill-posed problem and must be regularized to stabilize the inverted results. In particular, edge-preserving regularization with prior constraints based on Markov random field (MRF) has proved to be an effective technique for reconstructing subsurface models. However, regularized seismic inversion, based on the standard MRF scheme, typically makes use of isotropic MRF neighborhoods, in which the weighting coefficients of the model gradients are equivalent in all directions. Considering real geological conditions, subsurface formations are expected to be laterally continuous and vertically stratified. Therefore, the anisotropic effects caused by model gradients which vary along different directions should not be ignored. In this paper, we proposed a new prestack seismic inversion method based on anisotropic MRF (AMRF). In this method, AMRF coefficients are incorporated into the standard MRF scheme. These coefficients demonstrate directional variations and gradient dependencies, intended to directly correct the errors caused by the anisotropic model gradients on the prior constraints. In particular, we introduced the anisotropic diffusion method to calculate the AMRF coefficients. The proposed inversion method can effectively remove the anisotropic features of the model gradients and significantly improve the inversion results, especially for geologically layered formations. We demonstrated the effectiveness of the inversion method by both 2-D synthetic test and field data example, which presented encouraging results.

Description: The Terra and Aqua Moderate-Resolution Imaging Spectroradiometer (MODIS) scan mirror reflectance is a function of the angle of incidence (AOI) and was characterized prior to launch by the instrument vendor. The relative change of the prelaunch response versus scan angle (RVS) is tracked and linearly scaled on-orbit using observations at two AOIs of 11.2° and 50.2° corresponding to the moon view and solar diffuser, respectively. As the missions continue to operate well beyond their design life of six years, the assumption of linear scaling between the two AOIs is known to be inadequate in accurately characterizing the RVS, particularly at short wavelengths. Consequently, an enhanced approach of supplementing the on-board measurements with response trends from desert pseudoinvariant calibration sites (PICS) was formulated in MODIS Collection 6 (C6). An underlying assumption for the continued effectiveness of this approach is the long-term (multiyear) and short-term (month to month) stability of the PICS. Previous work has shown that the deep convective clouds (DCC) can also be used to monitor the on-orbit RVS performance with less trend uncertainties compared with desert sites. In this paper, the raw sensor response to the DCC is used to characterize the on-orbit RVS on a band and mirror-side basis. These DCC-based RVS results are compared with those of C6 PICS-based RVS, showing an agreement within 2% observed in most cases. The pros and cons of using a DCC-based RVS approach are also discussed in this paper. Although this reaffirms the efficacy of the C6 PICS-based RVS, the DCC-based RVS approach presents itself as an effective alternative for future considerations. Potential applications of this approach to other instruments, such as Suomi National Polar-orbiting Partnership, Joint Polar Satellite Systems, and Visible Infrared Imaging Radiometer Suite, are also discussed.