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Dielectric constant for the earth and planetary surfaces has been estimated using reflection coefficients in the past. A recent trend is to use model-based decomposition for dielectric constant retrieval from polarimetric synthetic aperture radar (polSAR) data. We examine the reported literature in this regard and propose a unique dielectric constant estimation (UDCE) algorithm using three-component decomposition technique. In UDCE, the dielectric constant is obtained directly from one of the elements of the measured coherency matrix in a single step. The dielectric constant estimate from the UDCE is independent of the volume scattering model when single-bounce or double-bounce scattering is dominant. This avoids error propagation from overestimation of volume scattering to the copolarization ratios, and in turn, to the dielectric constant, inherent in reported algorithms that use model-based decomposition. Consequently, a unique solution is obtained. We also demonstrate that the solution from the UDCE is unaffected by using a higher-order model-based decomposition. We evaluate the performance of the proposed UDCE algorithm over three Apollo 12, Apollo 15, and Apollo 17 landing sites on the lunar surface using Chandrayaan- 2 dual-frequency synthetic aperture radar (DFSAR) datasets. An excellent convergence rate for dielectric constant estimation is maintained over all three test sites. Using the proposed UDCE algorithm, the dielectric constant maps are produced for the lunar surface using full polSAR data for the first time. We observe that the generated dielectric constant maps capture all the ground truth features, previously unseen with such clarity. [ABSTRACT FROM AUTHOR]

Dielectric constant for the earth and planetary surfaces has been estimated using reflection coefficients in the past. A recent trend is to use model-based decomposition for dielectric constant retrieval from polarimetric synthetic aperture radar (polSAR) data. We examine the reported literature in this regard and propose a unique dielectric constant estimation (UDCE) algorithm using three-component decomposition technique. In UDCE, the dielectric constant is obtained directly from one of the elements of the measured coherency matrix in a single step. The dielectric constant estimate from the UDCE is independent of the volume scattering model when single-bounce or double-bounce scattering is dominant. This avoids error propagation from overestimation of volume scattering to the copolarization ratios, and in turn, to the dielectric constant, inherent in reported algorithms that use model-based decomposition. Consequently, a unique solution is obtained. We also demonstrate that the solution from the UDCE is unaffected by using a higher-order model-based decomposition. We evaluate the performance of the proposed UDCE algorithm over three Apollo 12, Apollo 15, and Apollo 17 landing sites on the lunar surface using Chandrayaan- 2 dual-frequency synthetic aperture radar (DFSAR) datasets. An excellent convergence rate for dielectric constant estimation is maintained over all three test sites. Using the proposed UDCE algorithm, the dielectric constant maps are produced for the lunar surface using full polSAR data for the first time. We observe that the generated dielectric constant maps capture all the ground truth features, previously unseen with such clarity. [ABSTRACT FROM AUTHOR]

Influenced by the imaging mechanism, the occurrence of interference clutter in synthetic aperture radar (SAR) renders the identification of false alarms using detectors challenging. Polarimetric SAR has the potential to improve ship detection performance owing to its distinctive polarization characteristics. The present study proposes a dualistic cascade convolutional neural network (DCCNN) algorithm driven by polarization characteristics for ship detection with fully PolSAR data. First, the new characterizations of fully PolSAR data—Optimized SPAN (OSPAN) and 6-D polarization vector (P 6), were mined and defined based on the polarization coherence matrix to introduce more intact information of targets. Then, a backbone feature extraction network with parallel dualistic cascade architecture, basic geometric feature extraction network (BGFENet), and polarization feature enhancement network (PFENet) was specifically constructed, which provided the comprehensive feature representation of targets via feature fusion. Finally, the classification and regression tasks were accomplished in the fully convolutional detection subnetwork relying on extracted multi-scale fusion feature maps, while focusing on target location regression, leading to a decrease in the cost of detection efficiency caused by redundant PFENet. In addition, the corresponding training strategy according to the special architecture of DCCNN was designed to overcome the problem of labeled fully PolSAR data insufficiency and migrate refined geometric knowledge of targets in the single-polarization SAR amplitude image. Experimental results on the established fully polarized SAR dataset show that DCCNN outperforms the competitive CNN-based target detection methods by at least 6.79% in terms of average precision. Moreover, experimental results on the typical large scenes show that DCCNN is at least 0.88% higher than the well-known conventional methods in terms of F 1 score. [ABSTRACT FROM AUTHOR]

This paper addresses the unsupervised classification problem for multilook polarimetric synthetic aperture radar (PolSAR) images via proposing a new spatially variant G p 0 mixture model based on the extended variational inference (SV G p 0 MM-EVI). Specifically, an SV G p 0 MM is constructed by associating data points with their own mixing coefficient vectors rather than sharing the same vector, which provides the additional flexibility to incorporate the spatial context and further effectively deal with the more heterogeneous areas than G p 0 MM. Then, an extended variational inference (EVI) algorithm is derived to facilitate the assignment of the conjugate prior distributions and accurately estimate the underlying parameters, in which two "help" functions are specially designed to solve the intractable variational lower bound. Meanwhile, dual spatial constraints based on the Bartlett distance and the mean template are also explored on polarimetric matrix and the hyperparameter in the posteriori distribution of mixing coefficients, respectively, thus adequately capturing the local correlation from the polarimetric matrix and the geometry perspectives. Furthermore, the learning algorithm with all the closed-form updates is developed and the cluster number could be automatically determined. Five PolSAR data sets (including two labeled data sets), which are obtained by different sensors, are used to verify the effectiveness of the proposed model. Experimental results illustrate that the proposed SV G p 0 MM-EVI is beneficial to classification task of PolSAR images, and achieves higher accuracy (e.g., 95.65% and 97.41% OA values) compared with some widely-used methods (i.e., H/ α -Wishart, Chernoff–Wishart, G p 0 MM, SVWMM and CK-HDRF methods). [ABSTRACT FROM AUTHOR]

The Chinese Gaofen-3 satellite was launched into space in 2016. Based on the corner reflector (CR) evaluation, it was found that the co-pol channel imbalance phase may exceed the maximum tolerance in the distributed standard product, i.e., ± 10°. The ground-deployed CRs are the most precise way to calibrate the phase error. However, the Mongolia calibration site of Gaofen-3 is a temporal calibration field, and the calibration time is limited for the periodic evaluation over the 8-year satellite life. In this paper, to accelerate the Gaofen-3 calibration mission, we propose an improved unitary zero helix (IUZH) framework to fully replace the use of CRs when estimating the co-pol channel imbalance phase by the use of natural media. Compared with our previous works (Shi et al., 2014; Shi et al., 2020), this novel algorithm presents three main contributions to remote sensing society. Firstly, we believe that this natural media calibration technique could replace the use of CRs, to satisfy the radar design specification, and that this natural media calibration technique could be more than just a substitute in an emergency event when there are no CRs deployed in-scene. Secondly, the IUZH framework is less dependent on choosing the particular reference targets, such as isolated point-like objects or Amazon forest. As a result, it will help radar researchers to relax the constraint of selecting references, and will make the non-CR calibration task easier in satellite or airborne cases. Last but not least, the proposed technique simultaneously derives the calibration phase after the sensor has completed a long-strip observation, which will significantly accelerate the Gaofen-3 external calibration mission, at little cost and time-consuming. The experiments with more than 500 images demonstrate that the phase bias of the Gaofen-3 standard product can be robustly calibrated, even when CRs are unavailable in the scene. The HH and VV imbalance phase with the IUZH method is generally found to be less than 4°. The potential phase error of the Gaofen-3 product is thus significantly suppressed, and the calibration accuracy satisfies the 10° design specification. [ABSTRACT FROM AUTHOR]

Abstract Polarimetric calibration is a critical step to suppress the potential system distortion before implementing any applications for polarimetric synthetic aperture radar (PolSAR). Among all the distortion elements, the crosstalk and cross-pol channel imbalance are generally estimated by the use of natural media, and the co-pol channel imbalance is traditionally solved by the use of corner reflectors (CRs). However, the deployment of ground CRs is costly and may even be impossible in some areas. Many bright point targets, such as poles, lamps, and corner points of structures, are commonly found in manmade regions. In particular, if the object orientation is parallel or perpendicular to the radar flight direction, some points will present similar polarimetric responses to trihedral or dihedral CRs. These points, which are referred to here as "CR-like targets", can be treated as a supplement to approximately solve the system distortion elements when CRs are unavailable. In this paper, we propose a novel step-by-step algorithm to determine the CR-like targets and estimate the co-pol channel imbalance phase in uncalibrated PolSAR imagery. Chinese X-band airborne and C-band satellite PolSAR data were used to test the proposed method. Compared with the CR-derived co-pol channel imbalance phase, the solution errors of the CR-like targets were 1.305° and 0.03° for the X- and C-band experiments, respectively. The results of the experiments confirm that the solutions of the CR-like targets are very close to those of ground-deployed CRs, and the proposed method can be considered as an effective way to calibrate PolSAR images when sufficient CR-like point targets are detected in manmade regions. [ABSTRACT FROM AUTHOR]

Highlights • Spatial correlation of objects in PolSAR image is helpful for target detection. • Globally weighted Perturbation Filters with saliency outperform single PNF. • Two initializations of GWPF achieve different performance, but both acceptable. Abstract In this paper, a saliency detection for Polarimetric Synthetic Aperture Radar (PolSAR) images is proposed based on weighted perturbation filters. Auxiliary data is demanded to identify polarimetric vector of targets, for a canonical perturbation filter. Only if the target signature was available and accurate, it would be satisfiable to apply the filter in practice. Besides, not every target can usually be detected by an individual filter, because of variant polarimetric characteristics of targets with respect to different aspects or shapes. To overcome these drawbacks, several perturbation filters are combined in the proposed method. By initializing with different parameters, these filters decompose PolSAR data into their index maps. Then, aiming to find out filters of interest, i.e., ones related to target pixels, we assume that targets to detect are sparse in PolSAR image. Thus, saliency weights are assigned to the filters, based on Jaccard distances of their index maps. Therein, the spatial sparseness between objects and their surrounding derives high weights for corresponding filters. And then, after globally fusion of refined filtering responses with the weights, saliency map is generated for every local pattern in PolSAR image. Finally, the target regions are extracted from this map, by thresholding and morphological operation. Experiments performed on real and simulated PolSAR data verify the performance of this method, in comparison with several common PolSAR detectors. Also, the proposed method finds out most targets in ground truth, without auxiliary polarimetric information provided. [ABSTRACT FROM AUTHOR]

Wetlands are important ecosystems around the world, although they are degraded due both to anthropogenic and natural process. Newfoundland is among the richest Canadian province in terms of different wetland classes. Herbaceous wetlands cover extensive areas of the Avalon Peninsula, which are the habitat of a number of animal and plant species. In this study, a novel hierarchical object-based Random Forest (RF) classification approach is proposed for discriminating between different wetland classes in a sub-region located in the north eastern portion of the Avalon Peninsula. Particularly, multi-polarization and multi-frequency SAR data, including X-band TerraSAR-X single polarized (HH), L-band ALOS-2 dual polarized (HH/HV), and C-band RADARSAT-2 fully polarized images, were applied in different classification levels. First, a SAR backscatter analysis of different land cover types was performed by training data and used in Level-I classification to separate water from non-water classes. This was followed by Level-II classification, wherein the water class was further divided into shallow- and deep-water classes, and the non-water class was partitioned into herbaceous and non-herbaceous classes. In Level-III classification, the herbaceous class was further divided into bog, fen, and marsh classes, while the non-herbaceous class was subsequently partitioned into urban, upland, and swamp classes. In Level-II and -III classifications, different polarimetric decomposition approaches, including Cloude-Pottier, Freeman-Durden, Yamaguchi decompositions, and Kennaugh matrix elements were extracted to aid the RF classifier. The overall accuracy and kappa coefficient were determined in each classification level for evaluating the classification results. The importance of input features was also determined using the variable importance obtained by RF. It was found that the Kennaugh matrix elements, Yamaguchi, and Freeman-Durden decompositions were the most important parameters for wetland classification in this study. Using this new hierarchical RF classification approach, an overall accuracy of up to 94% was obtained for classifying different land cover types in the study area. [ABSTRACT FROM AUTHOR]

This study proposes a new three-component method for timely detection of land cover changes using polarimetric synthetic aperture radar (PolSAR) images. The three components are object-oriented image analysis (OOIA), change vector analysis (CVA), and post-classification comparison (PCC). First, two PolSAR images acquired over the same area at different dates are segmented hierarchically to delineate land parcels (image objects). Then, parcel-based CVA is performed with the coherency matrices of the PolSAR data to detect changed parcels. Finally, PCC based on a parcel-based classification algorithm integrating polarimetric decomposition, decision tree algorithms, and support vector machines is used to determine the type of change for the changed parcels. Compared with conventional PCC based on the widely used Wishart supervised classification, the three-component method achieves much higher accuracy for land cover change detection with PolSAR images. The contribution of each component is evaluated by excluding it from the method. The integration of OOIA in the method greatly reduces the false alarms caused by speckle noise in PolSAR images as well as improves the accuracy of PolSAR image classification. CVA contributes to the method by significantly reducing the effect of the classification errors on the change detection. The use of PCC in the method not only identifies different types of land cover change but also reduces the false alarms introduced by the change in the environment. The three-component method is validated in land development detection, which is important to many developing countries that are confronting a growing problem of unauthorized construction land expansion. The results show that the three-component method is effective in detecting land developments with PolSAR images. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the linear discriminative Laplacian eigenmaps (LDLE) dimensionality reduction (DR) algorithm is introduced to C-band polarimetric synthetic aperture radar (PolSAR) agricultural classification. A collection of homogenous areas of the same crop class usually presents physical parameter variation, such as the biomass and soil moisture. Furthermore, the local incidence angle also impacts a lot on the same crop category when the vegetation layer is penetrable with C-band radar. We name this phenomenon as the “observed variation of the same category” (OVSC). The most common PolSAR features, e.g., the Freeman–Durden and Cloude–Pottier decompositions, show an inadequate performance with OVSC. In our research, more than 40 coherent and incoherent PolSAR decomposition models are stacked into the high-dimensionality feature cube to describe the various physical parameters. The LDLE algorithm is then performed on the observed feature cube, with the aim of simultaneously pushing the local samples of the same category closer to each other, as well as maximizing the distance between local samples of different categories in the learnt subspace. Finally, the classification result is obtained by nearest neighbor (NN) or Wishart classification in the reduced feature space. In the simulation experiment, eight crop blocks are picked to generate a test patch from the 1991 Airborne Synthetic Aperture Radar (AIRSAR) C-band fully polarimetric data from of Flevoland test site. Locality preserving projections (LPP) and principal component analysis (PCA) are then utilized to evaluate the DR results of the proposed method. The classification results show that LDLE can distinguish the influence of the physical parameters and achieve a 99% overall accuracy, which is better than LPP (97%), PCA (88%), NN (89%), and Wishart (88%). In the real data experiment, the Chinese Hailaer nationalized farm RadarSat2 PolSAR test set is used, and the classification accuracy is around 94%, which is again better than LPP (90%), PCA (88%), NN (89%), and Wishart (85%). Both experiments suggest that the LDLE algorithm is an effective way of relieving the OVSC phenomenon. [Copyright &y& Elsevier]