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Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) image interpretation. The classification performance of a promising parametric feature and classifier learning-based algorithm is limited when the amounts of pixels from different classes vary greatly. PolSAR data from minority classes is difficult to recognize correctly owing to a strong learning bias toward the majority classes, resulting in under-performing features for minority classes. To address this issue, a cost-sensitive latent space learning network based on the feature and classifier learning framework is proposed to reduce the learning bias for supporting the classification of imbalanced data in PolSAR images. First, a new cost-sensitive method is developed by adaptively computing the cost coefficient from predicted labels in the optimization process. Thus, the imbalanced distribution of PolSAR data can be obtained for both the labeled and unlabeled pixels rather than a predefined misclassifying matrix for labeled pixels. Second, latent space learning is used as an auxiliary task to assist the main task of classifier learning. By weighting the distance between the learned feature and the basis of the latent space with a different cost-sensitive coefficient, pixels in minority and majority classes are promoted to be more separable. Thus, the strong bias to majority classes is reduced from both the feature learning and classification process. Finally, the proposed method is studied through experiments on three different PolSAR images with several existing state-of-the-art methods. The experiments validate the effectiveness of the proposed method for balanced and imbalanced PolSAR land cover classification. [ABSTRACT FROM AUTHOR]

A new method based on the Looking-Around-and-Into (LAaI) mode is proposed for the task of change detection in large-scope Polarimetric Synthetic Aperture Radar (PolSAR) image. Specifically, the LAaI mode consists of two processes named Look-Around and Look-Into, which are accomplished by attention proposal network (APN) and recurrent convolutional neural network (CNN) (Recurrent CNN), respectively. The former provides certain subregions efficiently, and the latter detects changes in subregions accurately. In Look-Around, difference image (DI) of whole PolSAR images is calculated first to get global information; then, APN is established to locate the position of interested subregions intentionally by paying special attention to; next interested subregions that contain changed area in high probability are picked out as candidate-regions. Moreover, candidate-regions are sorted in importance descending order so that highly interested regions have priority to be detected. In Look-Into, candidate-regions of different scales are selected at first; then, Recurrent CNN is constructed and employed to deal with multiscale PolSAR subimages so that clearer and finer change detection results are generated. The process is repeated until all candidate-regions are detected. As a whole, the proposed algorithm based on the LAaI mode looks around whole images first to find out the possible position of changes (candidate-regions generation in Look-Around) and then reveal the exact shape of changes in different scales (multiscale change detection in Look-Into). The effect of APN and Recurrent CNN is verified in experiments, and it shows that the proposed method performs well in the task of change detection in the large-scope PolSAR image. [ABSTRACT FROM AUTHOR]

Compact-hybrid SAR (CP) is a dual-polarization (dual-pol) SAR mode that transmits a circular polarization (CirP) and measures the received signal at the horizontal and vertical antenna polarization. It is now admitted that the actual SAR technology does not permit the generation of a perfectly CirP and this may significantly affect CP radiometric and phase information. Since all the existing CP calibration models assume a perfectly transmitted CirP, there is an immediate need for the development of a new model that permits efficient assessment and calibration of CP non-circularity. In this article, a new general polarimetric hybrid SAR model (PolHyb) is introduced for both dual- (CP) and quad-polarization hybrid SAR modes. PolHyb explicitly includes the transmitted polarization non-circularity, in addition to conventional radar transmit and receive distortion matrices, channel imbalances and Faraday rotation contamination. The non-circularity of transmitted polarization is expressed in terms of the axial ratio (AR), which used to be popular in the 1960s for characterization of circularly polarized (transmit and receive) dual- and quad-polarization radar. The new CP model derived from PolHyb is adapted to PALSAR2-CP and used as the basis of an efficient method for an assessment of CP non-circularity using Amazonian rainforests. PALSAR2-CP data collected at four different beams (H2–6 to H2–9), with incidence angle varying between 30° and 45°, allows for the first ever demonstration of non-circularity of PALSAR2 CP transmitted polarization. Although it is lower than 0.5 dB for H2–6 and H2–7, the AR of PALSAR2-CP transmitted polarization increases significantly with incidence angle to reach up to 1 dB at Beam H2–8, and 2.3 dB at the highest incidence angles of Beam H2–9. [ABSTRACT FROM AUTHOR]

Freeman–Durden decomposition is a frequently used technique to analyze the scattering characteristics of multilook Polarimetric Synthetic Aperture Radar (POLSAR) data. When it is applied to the real POLSAR data, two problems emerge, namely, the volume scattering overestimation and negative powers. Many researchers think these two problems are caused by the insufficient decomposition algorithm, and several improvements are proposed. However, the improved decomposition algorithms become more and more complicated, and some new problems such as the decomposed component is not model based also emerge. In this paper, we try to solve the two problems through another way. We think they are caused not by the insufficient decomposition algorithm but by the dogmatic input. Freeman–Durden decomposition explicitly assumes reflection symmetry. Its input is a direct truncation of the measured coherency matrix. The truncation can be regarded as a reflection symmetry approximation (RSA) of the measured coherency matrix. We first show some reasons why we do not think the truncation is a good RSA. Then, a new RSA is proposed based on the sum of three reflection symmetry components derived from the measured coherency matrix. Experimental results with several real POLSAR images show that, if the new RSA is used as the input of Freeman–Durden decomposition, the above-mentioned two problems no longer exist. [ABSTRACT FROM AUTHOR]

The classification of urban environments poses significant challenges for polarimetric synthetic aperture radar (SAR), since rotated urban blocks violate the assumption of reflection symmetry held by many decomposition approaches. Vast sections of cities appear to be dominated by volume scattering due to the introduction of coherent depolarization. Attempts to model this slanted double bounce must contend with the fact that dihedrals rotated about the vertical axis do not support significant backscatter and require a rough ground surface to affect a return path. In this paper, the concept of an effective dihedral is introduced, with one plate coincident with the building wall and one plate associated with some ground facet, oriented so as to support double bounce. The rotation of this effective dihedral can be readily related to the polarization orientation angle and used to quantify co-pol and cross-pol phases. Theoretical results are confirmed with L-band and C-band polarimetric SAR imagery of San Francisco, as well as by broadband laboratory experiments with modeled buildings. In addition, the utility of using cross-pol phase to classify urban regions in the Los Angeles basin is demonstrated with ascending and descending dual-pol Radarsat data. [ABSTRACT FROM AUTHOR]

Two novel aspects of polarimetric calibration for fully polarimetric imaging radar systems are addressed. First, the radar system model is formulated in the context of two generic transmitter designs, either a single amplifier followed by a high-power switch or a low-power switch followed by two amplifiers. In the latter case, it is shown that a particular factorization of the polarimetric distortion matrix leads to a significant simplification of the cross-talk representation, from the standard four parameters to two reciprocal parameters, one for each of the antennas. Various system models from the literature are thus placed in a unified framework. Second, calibration techniques for circularly polarized antennas are derived, using either corner reflectors or clutter. However, where standard linear-basis algorithms estimate the cross-talk by its first-order distortion of reflection-symmetric clutter, no equivalent algorithm has been found for the circular basis; indeed, it is shown that the distortion caused, to first-order, by circular-basis cross-talk does not permit the individual cross-talk parameters to be identified. The calibration techniques are applied to fully polarimetric data acquired by the Ingara L-band radar using left- and right-polarized helical antennas. [ABSTRACT FROM PUBLISHER]

This paper addresses the impact of mixtures between classes on equivalent number of looks (ENL) estimation. We propose an unsupervised ENL estimator for polarimetric synthetic aperture radar (PolSAR) data, which is based on small sample estimates but incorporates a mixture-eliminating (ME) procedure to automatically assess the uniformity of the estimation windows. A statistical feature derived from a combination of linear and logarithmic moments is investigated and adopted in the procedure, as it has different mean values for samples from uniform and nonuniform windows. We introduce an approach to extract the approximated sampling distribution of this test statistic for uniform windows. Then the detection is conducted by a hypothesis test with adaptive thresholds determined by a nonuniformity ratio. Finally the experiments are performed on both simulated and real SAR data. The capability of the unsupervised ME procedure is verified with simulated data. In the real data experiments, the ENL estimates of Flevoland and San Francisco PolSAR images are analyzed, which show the robustness of the proposed ENL estimation for SAR scenes with different complexities. [ABSTRACT FROM PUBLISHER]

Quadratic forms play an important role in the development of several polarimetric and interferometric synthetic aperture radar (Pol-InSAR) methodologies, which are very powerful tools for earth observation. This paper investigates integrals of Pol-InSAR operators based on quadratic forms, with special interest on the Pol-InSAR coherence. A new operator, namely Trace Coherence, is introduced, which provides an approximation for the center of mass of the coherence region (CoRe). The latter is the locus of points on the polar plot containing all the possible coherence values. Such center of mass can be calculated as the integral of Pol-InSAR coherences over the scattering mechanisms (SMs). The trace coherence provides synthetic information regarding the partial target as one single entity. Therefore, it provides a representation, which is not dependent on the selection of one specific polarization channel. It may find application in change detection (e.g., coherent change detection and differential DEM), classification (e.g., building structure parameters), and modeling (e.g., for the retrieval of forest height). In calculating the integral of the Pol-InSAR coherences, an approximate trace coherence expression is derived and shown to improve the calculation speed by several orders of magnitude. The trace coherence approximation is investigated using Monte Carlo simulations and validated ESA (DLR) L-band quad-polarimetric data acquired during the AGRISAR 2006 campaign. The result of the analysis using simulated and real data is that the average error in approximating the integral of the coherence region is 0.025 in magnitude and 3° in phase (in scenarios with sufficiently high coherence). [ABSTRACT FROM PUBLISHER]

In this paper, a theoretical and experimental analysis of polarimetric synthetic aperture radar (SAR) architectures is undertaken for sea oil slick observation purposes. Reference is made to the conventional full-polarimetric (FP) SAR that is here contrasted with new-generation polarimetric SAR architectures, known as compact-polarimetric (CP) SAR. Two CP modes are considered, i.e., the hybrid-polarity and $\pi/4$ modes, whose measurements are emulated from actual L- and C-band FP SAR data. Polarimetric sea surface scattering is predicted according to an extended version of the Bragg scattering model (X-Bragg) in order to point out the differences exhibited between FP and CP SAR architectures and among CP SAR modes. Theoretical predictions are then contrasted with experiments undertaken on actual polarimetric SAR data collected over well-known oil slicks and weak-damping surfactants. Results confirm model prediction, showing that differences mainly apply when polarimetric features are estimated over slick-free sea surface using different SAR architectures, with the $\pi/4$ mode behaving closer to FP SAR. Although CP SAR architectures measure only a subset of the FP information content, they represent an interesting operational alternative for both detecting oil slicks and discriminating them from weak-damping surfactants. [ABSTRACT FROM AUTHOR]

This paper presents an alternative approach for polarimetric incoherent target decomposition (ICTD) dedicated to the analysis of very high-resolution polarimetric synthetic aperture radar (POLSAR) images. Given the non-Gaussian nature of the heterogeneous POLSAR clutter due to the increase in spatial resolution, the conventional methods based on the eigenvector target decomposition can ensure uncorrelation of the derived backscattering components at most. By introducing the independent component analysis (ICA) in lieu of the eigenvector decomposition, our method is rather deriving statistically independent components. The adopted algorithm, i.e., FastICA, uses the non-Gaussianity of the components as the criterion for their independence. Considering the eigenvector decomposition as being analogs to the principal component analysis (PCA), we propose the generalization of the ICTD methods to the level of the blind source separation (BSS) techniques (comprising both PCA and ICA). The proposed method preserves the invariance properties of the conventional ones, appearing to be robust both with respect to the rotation around the line of sight and to the change of the polarization basis. The efficiency of the method is demonstrated comparatively using POLSAR RAMSES X-band and ALOS L-band data sets. The main differences with respect to the conventional methods are mostly found in the behavior of the second most dominant component, which is not necessarily orthogonal to the first one. The potential of retrieving nonorthogonal mechanisms is moreover demonstrated using synthetic data. On the expense of a negligible entropy increase, the proposed method is capable of retrieving the edge diffraction of an elementary trihedral by recognizing dipole as the second component. [ABSTRACT FROM PUBLISHER]

We have performed simulations of random rough surface scattering using 3-D numerical solution of Maxwell equations (NMM3D) using surface size up to 32 × 32 squared wavelengths. The rough surfaces are characterized by exponential correlation functions. The simulation results of crossand copolarization backscattering coefficients were in good agreement with experimental measurements of bare soils at L-band. Because in numerical solutions of Maxwell equations the electric fields of the scattered wave are calculated for each realization, scattering matrices can be simulated by NMM3D, and such simulations are performed in this paper. For a given RMS height, correlation length, soil permittivity, and incident angle, we calculated the radar scattering matrix up to 958 independent realizations. For each realization, the components of the scattering matrix, namely, SHH, SVV, SHV, and SVH, are calculated. Using the simulated scattering matrices, we calculate the polarimetric speckle statistics (amplitude and phase difference), followed by a comparison with theoretical distributions. For fully developed speckle from the homogeneous rough surface, the results are examined and validated to ensure the simulated data quality as far as polarimetric properties are concerned. By taking ensemble averages, we calculate the coherency matrix from which the eigenvalues, entropy, anisotropy, and alpha angle in coherent target decomposition are then calculated. In particular, characterization of polarimetric descriptors for rough surface is presented. Issues of scattering symmetry characteristics are also discussed. [ABSTRACT FROM PUBLISHER]

This paper presents a new model-based decomposition method for the polarimetric synthetic aperture radar coherency matrices. We improve the model flexibility from the following two aspects: To reach a compromise between model flexibility and computation complexity, for the volume scattering component, the elementary scatterer shape is allowed to change from sphere/flat plate to dipole, then to dihedral, whereas orientation randomness is simplified by only considering two cases. Different orientation angles are considered for each component. Since the models become more complex, new decomposition procedures are developed. The three-component decomposition is first reformulated as a constrained sparse representation problem. Then, inspired by the orthogonal matching pursuit variant developed by Bruckstein et al. in 2008, new decomposition procedures are designed. The effectiveness of the proposed method is verified using a synthetic data set and two real SAR data sets, including a RADARSAT-2 data set and the NASA/JPL AIRSAR data set over San Francisco Bay. [ABSTRACT FROM PUBLISHER]

The presence of water in arid regions is correlated to large evaporitic deposits, with high concentration of salts. Polarimetric synthetic aperture radar (SAR) observations over such areas show high variations for both the amplitude and phase of the backscattered copolarized signal. This is due to a large dynamic range for both surface roughness and dielectric constant parameters, between the wet and dry seasons: crystallized salt is rough and presents a low dielectric constant, whereas saline water corresponds to smooth and conductive surfaces. We observed a complete seasonal cycle over the chott El Djerid playa, in southern Tunisia, using the 5.6-GHz C-band polarimetric SAR on-board RADARSAT-2. One SAR image acquisition was performed every 24 days, from February to September 2009. In addition to expected variations in the radar backscattered power, we observed significant changes in the phase difference between horizontally and vertically polarized channels. In order to explain such a phase effect, we first considered the arguments of the Fresnel reflectivity coefficient when approaching the Brewster angle, for materials presenting a high loss tangent. A more complete analytical modeling derived from the integral equation model approach confirmed this hypothesis. [ABSTRACT FROM PUBLISHER]

River ice has an important effect on natural processes and human activities in northern countries. Current models for estimating river ice thickness are mostly based on environmental data. They require several inputs and yield only a global estimate of ice thickness for a large heterogeneous area. Attempts have been made intending to retrieve river ice thickness from remote sensing using monopolarized C-band radar data. No reliable maps of ice thickness have been produced. In this paper, the potential of polarimetric synthetic aperture radar (PolSAR) data for estimating river ice thickness is demonstrated, and a river ice thickness retrieval model is proposed. The C-band SAR images used in this paper were acquired by Radarsat-2 in the winter of 2009 over the Saint-François River (Southern Quebec), the Koksoak River (Northern Quebec), and the Mackenzie River (Northwest Territories) in Canada. Field campaigns were carried out to obtain ice thickness validation data at 70 locations. Polarimetric entropy was used to obtain ice thickness estimates. This approach results in spatially distributed ice thickness maps for selected ice types. [ABSTRACT FROM PUBLISHER]

The model-based scattering decomposition pioneered by Freeman and Durden has stimulated research in characterizing polarimetric synthetic aperture radar (PolSAR) scattering phenomenon and its applications. The Freeman and Durden decomposition as originally developed is based on three scattering models for volume, surface, and double bounce. It is known that the decomposition often produces negative scattering powers for a large number of pixels. This implies that the scattering models are inconsistent with the data. In this paper, we investigate the model deficiency problem and propose several algorithms to mitigate it. To achieve this, we developed an incoherent scattering model based on the polarization orientation angle distribution of phase differences. Two approaches are taken to reduce the number of negative power pixels: 1) adopt a volume scattering model by including variable shape factor while keeping the original surface and double bounce models unchanged, and 2) incorporate the incoherent surface or double bounce model while keeping the original volume model. In addition, the combination of 1) and 2) is explored, and the effect of polarization orientation compensation on these algorithms is investigated. The effectiveness of these approaches is compared using L-band AIRSAR and E-SAR PolSAR data. It will be shown that model efficiency is improved with occurrence of negative power reduced to an insignificant level. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) data are used extensively for terrain classification applying SAR features from various target decompositions and certain textural features. However, one source of information has so far been neglected from PolSAR classification: Color. It is a common practice to visualize PolSAR data by color coding methods and thus, it is possible to extract powerful color features from such pseudocolor images so as to provide additional data for a superior terrain classification. In this paper, we first review previous attempts for PolSAR classifications using various feature combinations and then we introduce and perform in-depth investigation of the application of color features over the Pauli color-coded images besides SAR and texture features. We run an extensive set of comparative evaluations using 24 different feature set combinations over three images of the Flevoland- and the San Francisco Bay region from the RADARSAT-2 and the AIRSAR systems operating in C- and L-bands, respectively. We then consider support vector machines and random forests classifier topologies to test and evaluate the role of color features over the classification performance. The classification results show that the additional color features introduce a new level of discrimination and provide noteworthy improvement in classification performance (compared with the traditionally employed PolSAR and texture features) within the application of land use and land cover classification. [ABSTRACT FROM PUBLISHER]

This paper aims at characterizing the scattering mechanisms occurring at the ground level in a tropical forest illuminated by a P-band synthetic aperture radar (SAR). The analysis is carried out based on the multibaseline, fully polarimetric, data set collected by ONERA over Paracou, French Guyana, in the frame of the European space agency campaign TropiSAR. The favorable baseline distribution of this data set results in the possibility of removing most contributions from the vegetation layer by tomographic techniques, thus allowing the generation of a new fully polarimetric single look complex SAR image relative to scattering contributions from the ground level only. Such a ground layer image is then analyzed by considering the variation of its polarimetric signature with respect to terrain local slope and Radar look angle. Two major conclusions are drawn: 1) double bounce scattering from trunk-ground interactions is observed to be the dominant scattering mechanism at the ground level on flat terrains, whereas it rapidly tends to vanish as the topographic slope increases, and 2) the characteristic parameter that rules trunk-ground scattering is not the tree height, but rather the available free path facing the tree, as a result of the presence of nearby trees, undulating topography, or understory preventing double bounce scattering from taking place whenever the ground bounce occurs too far away from the considered tree. The mean free path length resulting from the analysis of this data-set is found to be L\simeq 7~m. Finally, we discuss how the concept of free path length can be accounted for in simple terms by assuming an equivalent extinction model characterized by a variation along the horizontal dimension. [ABSTRACT FROM AUTHOR]

This paper presents a new general four-component scattering power decomposition method by implementing a set of unitary transformations for the polarimetric coherency matrix. There exist nine real independent observation parameters in the 3 \times 3 coherency matrix with respect to the second-order statistics of polarimetric information. The proposed method accounts for all observation parameters in the new scheme. It is known that the existing four-component decomposition method reduces the number of observation parameters from nine to eight by rotation of the coherency matrix and that it accounts for six parameters out of eight, leaving two parameters (i.e., the real and imaginary parts of T13 component) unaccounted for. By additional special unitary transformation to this rotated coherency matrix, it became possible to reduce the number of independent parameters from eight to seven. After the unitary transformation, the new four-component decomposition is carried out that accounts for all parameters in the coherency matrix, including the remaining T13 component. Therefore, the proposed method makes use of full utilization of polarimetric information in the decomposition. The decomposition also employs an extended volume scattering model, which discriminates volume scattering between dipole and dihedral scattering structures caused by the cross-polarized HV component. It is found that the new method enhances the double-bounce scattering contributions over the urban areas compared with those of the existing four-component decomposition, resulting from the full utilization of polarimetric information, which requires highly improved acquisitions of the cross-polarized HV component above the noise floor. [ABSTRACT FROM PUBLISHER]

We present an efficient algorithm for retrieving the ocean-surface wind vector from C-band Radar Satellite RADARSAT-2 fully polarimetric synthetic aperture radar (SAR) measurements based upon the copolarized geophysical model function, i.e., CMOD5.N, and the cross-polarized ocean backscatter model, i.e., C-2PO. The analysis of fine quad-polarization mode single-look complex SAR data and collocated in situ moored buoy observations reveals that the polarimetric correlation coefficient between co- and cross-polarization channels has odd symmetry with respect to the wind direction. This characteristic is different from the feature that normalized radar cross sections for quad-polarization have even symmetry regarding the wind direction. We first use the C-2PO model to directly retrieve wind speeds without any external wind-direction and radar-incidence-angle inputs. Subsequently, the retrieved wind speeds, along with incidence angles and CMOD5.N, are employed to invert the wind direction, still with ambiguities. The odd-symmetry property is then applied to remove the wind direction ambiguities. Thus, it is shown that fully polarimetric SAR measurements provide complementary directional information for the ocean-surface wind fields. This method has the potential to improve wind vector retrievals from space. [ABSTRACT FROM AUTHOR]

The TropiSAR campaign has been conducted in August 2009 in French Guiana with the ONERA airborne radar system SETHI. The main objective of this campaign was to collect data to support the Phase A of the 7th Earth Explorer candidate mission, BIOMASS. Several specific questions needed to be addressed to consolidate the mission concept following the Phase 0 studies, and the data collection strategy was constructed accordingly. More specifically, a tropical forest data set was required in order to provide test data for the evaluation of the foreseen inversion algorithms and data products. The paper provides a description of the resulting data set which is now available through the European Space Agency website under the airborne campaign link. First results from the TropiSAR database analysis are presented with two in-depth analyses about both the temporal radiometric variation and temporal coherence at P-band. The temporal variations of the backscatter values are less than 0.5 dB throughout the campaign, and the coherence values are observed to stay high even after 22 days. These results are essential for the BIOMASS mission. The observed temporal stability of the backscatter is a good indicator of the expected robustness of the biomass estimation in tropical forests, from cross-polarized backscatter values as regarding environmental changes such as soil moisture. The high temporal coherence observed after a 22-day period is a prerequisite for SAR Polarimetric Interferometry and Tomographic applications in a single satellite configuration. The conclusion then summarizes the paper and identifies the next steps in the analysis. [ABSTRACT FROM PUBLISHER]

We propose a novel method to select long-term coherent targets from a set of repeated-pass interferometric acquisitions. The major assumption is that targets are so close together to share the same optical path. This leads to an efficient singular-value-decomposition-based estimator of the targets' phase and, then, to PS detection. The estimated phase can be used to define the PS coherence for each image. Being sensitive to acquisition and processing artifacts, the PS coherence is a valuable tool for quality assessment of the interferometric stack. Results are shown with real data sets in both single and full polarizations. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method for a measure of coherent similarity between temporal multichannel synthetic aperture radar (SAR) images and its implementation to change detection application. The method is based on mutual information (MI) from information theory. The MI measures the amount of information in common between coherent temporal multichannel SAR acquisitions. In order to develop an algorithm for all kinds of SAR images, such as interferometric SAR, polarimetric-interferometric SAR (PolInSAR), and partial PolInSAR, first, the joint density function of temporal multichannel images based on their second-order statistics has been derived. Then, the derived joint density function is used to calculate an analytical expression for the MI between temporal images, which is assumed to be maximal if the temporal images are identical. Although, in this paper, a new coherent similarity measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can equally well be implemented into any kind of multivariate remote sensing data, such as multispectral optical and interferometric images after small continuation. This derived quantity has been implemented for change detection application whose aim is to characterize the temporal behavior of the acquisitions. A comparison between the proposed and the other well-known change detection methods by means of scene characterization is shown, describing the advantages due to the fact that the proposed change detector involves almost every facet of applied change detection. [ABSTRACT FROM AUTHOR]

This paper proposes a new Markov-chain-based constant false alarm rate (CFAR) detector for polarimetric data using low-level data fusion and high-level decision fusion. The Markov-chain-based CFAR detector extends traditional probability density function (pdf) based CFAR detection to first-order Markov chain model by considering both correlation between neighboring pixels and pdf information in CFAR detection. With the additional correlation information, the proposed approach results in advancing the performance of conventional CFAR detectors. Moreover, to take advantage of full polarizations of polarimetric data, various data fusion methods are considered to improve detection performance, including polarimetric transformation, principal component analysis, and decision fusion. Our experimental results confirm the superiority of the new Markov chain polarimetric CFAR detector over conventional pdf-based CFAR detectors. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel scheme of polarimetric synthetic aperture radar (PolSAR) image classification. We apply Laplacian eigenmaps (LE), a nonlinear dimensionality reduction (NDR) technique, to a high-dimensional polarimetric feature representation for PolSAR land-cover classification. A wide variety of polarimetric signatures are chosen to construct a high-dimensional polarimetric manifold which can be mapped into the most compact low-dimensional structure by manifold-based dimensionality reduction techniques. This NDR technique is employed to obtain a low-dimensional intrinsic feature vector by the LE algorithm, which is beneficial to PolSAR land-cover classification owing to its local preserving property. The effectiveness of our PolSAR land-cover classification scheme with LE intrinsic feature vector is demonstrated with the RadarSat-2 C-band PolSAR data set and the 38th Research Institute of China Electronics Technology Group Corporation X-band PolInSAR data set. The performance of our method is measured by the separability in the feature space and the accuracy of classification. Comparisons on the feature space show that the LE intrinsic feature vector is more separable than different original feature vectors. Our LE intrinsic feature vector also improves the classification accuracy. [ABSTRACT FROM AUTHOR]

This paper focuses on assessing the effectiveness of applying orientation angle calibration to polarimetric synthetic aperture radar (PolSAR) data for soil moisture estimation. We employ Cloude-decomposition-based method to estimate the orientation angle because it can relate a scatter-distributed pixel to its major component of an equivalent "pure target," use the Jet Propulsion Laboratory/Airborne Synthetic Aperture Radar L-band fully polarimetric data to validate the proposed method, and observe results in good agreement after orientation angle compensation is employed. Specifically, root mean square errors of measured radar backscattering coefficients σhh0 and σvv0 and copolarization ratio versus advanced integral equation model predictions are reduced significantly from 1.95, 1.33, and 2.03 dB to 1.30, 1.15, and 1.43 dB, respectively. The compensated copolarized backscattering coefficients are also used as inputs to a novel inversion model to estimate the dielectric factor Rhh and volumetric soil moisture mv. The results show that the estimation errors are reduced significantly from 0.075 to 0.054 and 0.056 to 0.041 for Rhh and mv, respectively. This paper demonstrates the advantage of orientation angle calibration as a preprocessing for estimating bare soil moisture, particularly in agricultural areas, and the preponderance of fully PolSAR data on soil moisture estimation over dual and single polarizations. [ABSTRACT FROM AUTHOR]

Recent advances in synthetic aperture radar (SAR) technology have revived meteorological applications with this type of radar. SARs are designed for surface imaging, but now that several X-band multipolarization SAR satellites are in orbit, the attenuation and backscatter caused by precipitation can be better studied. The results presented here demonstrate some of the possibilities by analyzing observations from dual-polarization (HH, VV) TerraSAR-X (TSX) acquisitions over central Florida surrounding severe storms in August 2008. Simultaneous to the SAR acquisitions, WSR-88D ground weather radars in Melbourne and Tampa Bay, FL, collected reflectivity and radial velocity data; the observed strong precipitation cells from convective storms are colocated with severe attenuation in the corresponding SAR images. The observations from SAR measurements are explained quantitatively by converting ground radar reflectivity into spaceborne radar attenuation via a theoretical model. In addition, polarization analysis comparing the SAR image to two additional TSX acquisitions 11 days apart and without rain provides an indication of storm-induced propagation effects on X-band SAR. Specifically, the copolar ratio Zdr and the copolar correlation differences exhibit behavior that is better explained by the precipitation impact versus surface changes. Multiple regions with varying ground cover, including urban, and storm characteristics are analyzed to highlight the complexity of meteorological research using SAR while revealing a potential use of the technology to investigate the storm structure. [ABSTRACT FROM AUTHOR]

This paper compares ALOS PALSAR fully polarimetric and dual-polarized data in the application area of land cover mapping. To assure versatile comparison of the data, different classification methods and different features of data are used. Two of the classification methods used are based on supervised classification and two on unsupervised classification. Polarimetric data are used in three ways: 1) as fully polarimetric data; 2) features calculated from fully polarimetric data; and 3) intensity data of selected channels. Combinations of six (water, field, sparse forest, dense forest, peat land, and urban areas), five, four, and three classes were used for classification. Fully polarimetric data gave better results (87.5%–84.7% with three classes; open land areas, forest, and water) than intensity data only (83.6%–78.6%), but the differences in the overall accuracies between the methods were not more than 7.6%. Kappa coefficients of agreement are moderate for all the classifications. Supervised classification can be expected to perform better than unsupervised classification, given that the training areas can be selected accurately. Dual polarization data were found to be an attractive alternative in cases where fully polarimetric data are not available or it is of low resolution. With intensities of selected polarimetric features, it was possible to obtain a high classification accuracy as with fully polarimetric data. This also opens possibilities for nonspecialist users to benefit from polarimetric information in classification. [ABSTRACT FROM AUTHOR]

In this paper, a new method to filter coherency matrices of polarimetric or interferometric data is presented. For each pixel, an adaptive neighborhood (AN) is determined by a region growing technique driven exclusively by the intensity image information. All the available intensity images of the polarimetric and interferometric terms are fused in the region growing process to ensure the validity of the stationarity assumption. Afterward, all the pixels within the obtained AN are used to yield the filtered values of the polarimetric and interferometric coherency matrices, which can be derived either by direct complex multilooking or from the locally linear minimum mean-squared error (LLMMSE) estimator. The entropy/alpha/anisotropy decomposition is then applied to the estimated polarimetric coherency matrices, and coherence optimization is performed on the estimated polarimetric and interferometric coherency matrices. Using this decomposition, unsupervised classification for land applications by an iterative algorithm based on a complex Wishart density function is also applied. The method has been tested on airborne high-resolution polarimetric interferometric synthetic aperture radar (POL-InSAR) images (Oberpfaffenhofen area—German Space Agency). For comparison purposes, the two estimation techniques (complex multilooking and LLMMSE) were tested using three different spatial supports: a fix-sized symmetric neighborhood (boxcar filter), directional nonsymmetric windows, and the proposed AN. Subjective and objective performance analysis, including coherence edge detection, receiver operating characteristics plots, and bias reduction tables, recommends the proposed algorithm as an effective POL-InSAR postprocessing technique. [ABSTRACT FROM AUTHOR]