This paper proposes FuzzyCAM 1, a novel Class Activation Mapping (CAM) method that enhances deep neural network interpretability by exploiting impulse features in gra dient maps. Within this framework, we propose constructing CAMs based on specific features and establish the relationship between impulse noise and the CAM method through two theorems: Theorem 1 proves the existence of gradient impulses during backpropagation, and Theorem 2 demonstrates gradient impulses origin in misclassified samples and intrinsic link to model learning. FuzzyCAM detects these impulses via a fuzzy impulse detection algorithm, converts them into adaptive weights for feature map fusion, and generates targeted class activation maps. To address the shortcomings of existing CAM evaluation metrics, a dynamic threshold segmentation method based on the σ principle and SRNA have been proposed. Visualizations con firm its superiority in highlighting semantically critical regions, advancing reliable model interpretation.

Quad polarimetric (Quad-Pol) and compact polarimetric (Compact-Pol) Inverse Synthetic Aperture Radar (ISAR) are two main configuration modes for space targets imaging. Compared with Quad-Pol ISAR mode, Compact-Pol ISAR mode can reduce radar system complexity at the price of polarimetric information loss. In order to fulfill this gap, this work dedicates to reconstructing the Quad-Pol information of space targets from the Compact-Pol mode, thereby reconciling the need for system simplicity with the retention of abundant Quad-Pol data. The main idea is to design a quad polarimetric reconstruction network (QPRNet) based on the Compact-Pol ISAR data characteristics. Firstly, a group feature fusion (GFF) module is designed to collect the coupling polarimetric features between channels of Compact-Pol ISAR data, making the network better learn the implicit mapping relationships between polarimetric channels. Then, the receptive field expansion (RFE) module is used to obtain large-scale spatial features through the network, which is beneficial to extract polarimetric modulation mechanism between adjacent components of spatial targets. Experimental studies have been carried out in Quad-Pol ISAR data reconstruction. Comparison results show that the Quad-Pol ISAR data reconstructed by proposed method is more similar to the truth. Moreover, compared with the state-of-the-arts, the mean absolute error (MAE), coherence index (COI) and peak signal-to-noise ratio (PSNR) have improved by 4.22%, 4.64% and 2.01%, respectively.

Man-made targets subjected to different polarized waves will produce different depolarization effects, and these differences contain abundant information beneficial for recognition. However, traditional manually designed features struggle to fully utilize polarimetric information for scattering characterization. This letter proposes a target scattering characteristic learning network based on the Null-Pol response, which adaptively extracts the proportions of typical scattering mechanisms from mixed scattering mechanisms. Firstly, by leveraging polarimetric modulation, the Discrete Null-Pol Synthesis Pattern (DNSP) is designed to fully reveal the differences in target scattering mechanisms. On this basis, we propose an end-to-end scattering inversion network module to learn the DNSPs of different typical targets under scattering ambiguity conditions, obtaining polarimetric scattering contribution of 10 typical structures. Finally, we conduct structure recognition experiments to demonstrate the effectiveness of the proposed module. The results show that the proposed method can effectively characterize scattering behavior and significantly improve the performance of target structure recognition.

Identifying satellite components in polarimetric inverse synthetic aperture radar (ISAR) images is beneficial for monitoring their operation and health status. Most target recognition methods rely on network structures designed for optical images and fail to consider the inherent polarimetric scattering characteristics. Furthermore, the aliasing of scattering mechanisms caused by the complex structure of man-made targets, along with the scattering diversity resulting from observation perspectives, poses challenges to target polarimetric interpretation. To address these challenges, this study proposes a structure recognition framework embedded within scattering mechanism to achieve pixel-level to component-level structure (CS) recognition. First, through semi-supervised representation learning, the 3-D polarimetric correlation pattern (3-D PCP) of typical polarimetric scattering structures (PSSs) is used as expert knowledge to guide a deep-learning network, enabling pixel-level scattering mechanism separation. On this basis, a relation module is employed to explore the relationships between different pixels’ scattering mechanisms to accomplish component-level recognition. Finally, polarimetric ISAR satellite images and component annotation datasets are constructed. Pixel-level and component-level comparisons verify the advantages of the proposed method.

ISAR enables continuous space surveillance irrespective of weather conditions, while the compact polarization (CP) mode balances hardware costs with the provision of polarization information. Recognizing components of space targets provides valuable insights into attitude inversion and the detection of abnormal motion. However, existing ISAR space target recognition methods lack the capability to transition from target-level classification to component-level recognition. Additionally, components with weak and non-uniform scattering intensity distributions pose challenges to their precise positioning and classification in ISAR images. To address these limitations, a coarse-to-fine dual-branch correlation aggregation graph attention network (DCA-Net) is proposed, featuring a novel graph neural network construction strategy. Considering target sparsity, a multi-channel graph node filter (MGNF) module combining compact polarimetric features is devised to enhance computation efficiency. Subsequently, a dual-branch correlation aggregation graph (DCAG) module is constructed concerning the local correlation and global topology. Information suppressed by the non-maximum suppression (NMS) algorithm is reutilized to construct a local graph, which is then aggregated to improve the recognition probability of weak scattering intensity components. Meanwhile, a global graph is constructed, allowing the utilization of structural topology relationships for robust inference in heterogeneous scattering scenarios. Experimental results on ISAR dataset demonstrate that the proposed method achieves superior performance, with at least a 5.38% improvement in the F1 score index and 5.21% improvement in the mean average precision (mAP) index.

Recently, integrated deep learning methods cascading speckle filtering and target detection have garnered increasing attention in synthetic aperture radar (SAR) image target detection. These methods use despeckling networks to suppress speckle noise, enhancing the performance of followed detectors. However, recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, where adding imperceptible perturbations to benign examples can cause incorrect predictions. This phenomenon raises serious concerns about the security of current integrated deep learning algorithms in SAR images. To this end, this work conducts adversarial attack research on integrated speckle filtering and detection methods to assess its adversarial robustness. Specifically, a novel meta-adversarial despeckling network (Meta-ADNet) architecture is proposed, which leverages the despeckling network as a potential attack pathway to inject perturbations, generating adversarial despeckled examples that invalidate subsequent detection processes. Meta-ADNet consists of two core components: the baseline model adversarial despeckling network (ADNet) and the corresponding meta-adversarial attack framework. ADNet first generates benign despeckled SAR images through a despeckling network, which are then fed into the perturbation injection branch. This branch guides the despeckling network in producing adversarial examples by backpropagating the confidence loss of the surrogate detection model. The meta-adversarial attack framework constructs different tasks by selecting multiple surrogate detection models and iteratively simulates white-box ensemble attacks and black-box attacks within each task to enhance the transfer attack capability of ADNet on black-box models. Extensive experimental results on the SAR detection datasets SSDD and HRSID demonstrate that the proposed algorithm can effectively attack white-box surrogate detectors and exhibits strong black-box transfer attack capabilities.

Urban damage investigation is an important application for polarimetric synthetic aperture radar (SAR), which is capable of sensing the target scattering mechanism changes before and after a natural disaster. Quad-pol SAR with fully polarimetric acquisition capability can better sense the scattering mechanism changes. Meanwhile, dual-pol SAR with wider swath is suitable for large area monitoring. In this vein, this work dedicates to generating pseudo quad-pol SAR data from dual-pol SAR mode to partially reconstruct fully polarimetric information. The main contributions contain two aspects. Firstly, a multi-scale feature aggregation convolutional neural network (CNN) has been proposed to reconstruct quad-pol SAR data, which includes a feature extraction (FE) module to collect multi-scale features from dual-pol SAR data in spatial and polarimetric domain, and a feature translation (FT) network aggregated with attention modules to deeply fuse the stacked multi-scale features and map them to quad-pol SAR covariance matrices. Then, a urban damage level estimation approach has been established with reconstructed quad-pol SAR data based on polarimetric coherence pattern interpretation tool. Experimental studies have been carried out in terms of both pseudo quad-pol SAR data reconstruction and urban damage level estimation. Comparison results demonstrate that the proposed method achieves better quad-pol SAR data reconstruction accuracy and urban damage level estimation accuracy. Moreover, compared with the urban damage level estimated by real quad-pol SAR data, the proposed method can achieve 99.83% estimation consistency within 5% error tolerance.

Physical parameter estimation of satellite is crucial in space situation awareness as it reflects valuable information. Polarimetric inverse synthetic aperture radar (Pol-ISAR) is a powerful sensor for space surveillance, providing rich information for satellite physical parameter estimation. Parabolic antennas, which are widely loaded in remote sensing and communication satellites, have received great attention recently. Dedicated to the space situation awareness issue using Pol-ISAR, a physical parameter joint estimation method of satellite parabolic antenna with key frame Pol-ISAR images is developed in this work. The core idea is to utilize the mapping relationship between parabolic antenna in 3-D space and its projection ellipse in 2-D ISAR image. Under special observation geometry, the closed-form expressions of parabolic antenna physical parameters are deduced for the first time, providing an efficient way for parameter estimation. Moreover, the abundant information within Pol-ISAR images is mined and utilized. Various polarimetric features are adopted for ellipse extraction and the subsequent physical parameter estimation. Compared with single-polarization channel data, the superiorities of polarimetric feature are validated using electromagnetic simulation data.

Speckle reduction is a pre-processing for synthetic aperture radar (SAR) image interpretation and application. With the advances of convolutional neural network (CNN) models, excellent speckle filters have been continuously developed. However, supervised learning based models suffer from a generalization deficiency due to the lack of clean SAR images. Additionally, other self-supervised learning based methods rely on multiple independent SAR images of the same scene to generate the filtered SAR image. In practice, these additional auxiliary datasets are not always available, which limits the application of these methods. To fulfill this gap, a novel self-supervised framework named Sublook2Sublook is proposed for single SAR images speckle filtering. The main contribution of this work lies in that a new theorem is founded which guarantee the cost function defined on the paired sublook SAR images is statistically equivalent to the supervised counterpart based on the speckled-clean SAR image pairs. Thereby, the sublook SAR images can be alternatively used for model training instead of the clean SAR images or additional auxiliary datasets. From this fashion, a complete self-supervised speckle filter is developed. Firstly, sublook decomposition is performed in both azimuth and range directions. Then, optimal paired sublook images are selected based on a criteria of minimum L1 norm distance. Finally, the established self-supervised speckle filter can be trained with the paired sublook images. Extensive experimental studies are conducted with various SAR datasets in terms of different frequency bands and spatial resolutions from the Radarsat-2, COSMO-SkyMed, and ALOS-2 SAR satellites. Comparisons studies with four state-of-the-art despeckling methods confirm the superiority of the proposed method. The results demonstrate that the proposed Sublook2Sublook framework can better smooth speckles in homogeneous areas while well preserve image details.

Polarimetric synthetic aperture radar (PolSAR) plays an important role in remote sensing. As a valuable application, PolSAR ship detection receives great attention and obtains fruitful achievements recently. Since ship targets’ scattering responses are highly sensitive to the radar grazing angles, robust PolSAR ship detection still faces challenges especially the very low target to clutter ratio (TCR) phenomenon at large grazing angles. How to find stable polarimetric features for robust ship detection at different grazing angles becomes the key scientific problem. This work dedicates to this issue and the main idea is to explore the potentials of polarimetric roll-invariant features which are relatively independent to radar looking directions. The main contributions are threefold. First, quantitative investigations are conducted to disclose the variation law of ship targets and sea clutters’ polarimetric scattering mechanisms in terms of different grazing angles with electromagnetic computation data and PolSAR datasets. Second, the performances of polarimetric roll-invariant features are significantly examined with the TCR and the absolute difference (AD) indexes. Five optimal polarimetric roll-invariant features with stable TCRs over the wide grazing angle range are founded, which can clearly enhance the contrast between ship targets and sea clutters at large grazing angles. Finally, robust PolSAR ship detection approaches are established with the selected polarimetric roll-invariant features. Comparison studies with Gaofen-3 and Radarsat-2 PolSAR datasets of different grazing angles are carried out. Compared with traditional polarimetric features, the experimental results demonstrate that the selected polarimetric roll-invariant features exhibit superior detection performances in terms of both detection accuracy and detection robustness.

This paper introduces an innovative approach in Synthetic Aperture Radar (SAR) polarimetry and proposes a novel descriptor called polarimetric autocorrelation matrix. Different from polarimetric covariance and coherency matrices, the polarimetric autocorrelation matrix can capture hidden Doppler information in the frequency domain and encode it in the phase using higher-order statistical methods. This matrix facilitates the joint extraction and analysis of polarization and Doppler information from fully Polarimetric SAR (PolSAR) data through matrix analysis. The paper explains that the polarimetric autocorrelation matrix can be decomposed into a Doppler-related matrix and a covariance-related matrix. The magnitudes and phases of these matrices provide insight into Doppler shifts between different polarizations and the variance of the backscattering coefficient. Our research demonstrates how the Doppler shift is influenced not only by the target’s radial velocity but also by radar polarization. Four quad-polarimetric RADARSAT-2 images are used for testing in this study, and a set of characterization features and parameters are derived from the polarimetric autocorrelation matrix. The novel descriptor, together with the new parameters, can detect man-made target and sea ice, mitigate ambiguity caused by moving targets, and indicate the motion status of ship targets and sea currents. Specifically for marine scenes, our data found Doppler differences in HH polarization and VV polarization induced by sea surface motion can be up to 100 Hz.

As the foundation of image interpretation, semantic segmentation is an active topic in the field of remote sensing. Facing the complex combination of multiscale objects existing in remote sensing images (RSIs), the exploration and modeling of contextual information have become the key to accurately identifying the objects at different scales. Although several methods have been proposed in the past decade, insufficient context modeling of global or local information, which easily results in the fragmentation of large-scale objects, the ignorance of small-scale objects, and blurred boundaries. To address the above issues, we propose a contextual representation enhancement network (CRENet) to strengthen the global context (GC) and local context (LC) modeling in high-level features. The core components of the CRENet are the local feature alignment enhancement module (LFAEM) and the superpixel affinity loss (SAL). The LFAEM aligns and enhances the LC in low-level features by constructing contextual contrast through multilayer cascaded deformable convolution and is then supplemented with high-level features to refine the segmentation map. The SAL assists the network to accurately capture the GC by supervising semantic information and relationship learned from superpixels. The proposed method is plug-and-play and can be embedded in any FCN-based network. Experiments on two popular RSI datasets demonstrate the effectiveness of our proposed network with competitive performance in qualitative and quantitative aspects.

Compared with quad polarization, dual polarization (DP) not only has twice wide-swath of observation but also decreases the synthetic aperture radar (SAR) system energy budget. In this paper, an end-to-end full convolutional neural network is proposed to achieve full polarimetric SAR image reconstruction based on dual polarimetric SAR data. Firstly, the feature extraction (FE) network is utilized to extract the multi-scale features of the dual-pol SAR data. Then, a feature translation (FT) network is proposed to achieve the stacked multi-scale features fusion and the quad-pol SAR image space mapping. The weighted cross-entropy loss function is designed to resolve the unbalanced reconstruction of different polarimetric channels. The measured ALOS/PALSAR data is utilized to validate the superiority of the proposed method.

Compared with the optical imaging system, polarimetric inverse synthetic aperture radar (ISAR) can work all-day and all-weather, which plays an important role in space surveillance. However, high-resolution (HR) ISAR images usually require large bandwidth and coherent integration angle, which is limited by the equipment’s physical conditions. In this vein, the super-resolution (SR) of ISAR images is of vital importance. At present, supervised learning methods are often used in image SR of computer vision. By constructing low-resolution (LR) and HR data pairs, the neural network can learn the mapping relationship between them. However, the low-frequency information in LR image data is less considered. In addition, to obtain different scales of SR reconstruction results, multiple network training repetitions are usually needed, which consumes time and hardware resources. Based on the idea of implicit neural representation, this paper constructs an implicit neural network representation framework for polarimetric ISAR image SR, which can obtain multiscale SR results through one training. A semi-supervised module is also constructed to make the network have the ability of supervised and unsupervised learning, which is conducive to mine and make better use of LR images. A polarimetric ISAR image SR dataset is constructed for satellite targets while four indexes are adopted for quantitative evaluation in global and local aspects. Experiments demonstrate that the proposed approach achieves better SR performance, where the PSNR index can be increased at least by 0.93dB.

Attitude estimation of space targets can reveal crucial details about payload orientation, movement intentions, and observation area, all of which are vital in space situational awareness. Till now, inverse synthetic aperture radar (ISAR) has become a mainstream sensor for space target observation, providing rich information for space target attitude estimation. Based on projection matrix and linear structure extracted from ISAR images, a space target attitude estimation method is proposed in this work. The main contribution falls on two parts. On the one hand, linear structure is derived based on the peak accumulation values of original ISAR images rather than binary images. On the other hand, the space target attitude information is effectively estimated based on the projection matrix theory and linear structure extraction results. Experimental studies with measured and simulated data demonstrate the effectiveness of the proposed method.

With the development of deep learning (DL), research on ship classification in synthetic aperture radar (SAR) images has made remarkable progress. However, such research has primarily focused on classifying large ships with distinct features, such as cargo ships, containers, and tankers. The classification of SAR fishing vessels is extremely challenging because of two main reasons: 1) the small size and minor interclass differences of fishing vessels make learning fine-grained features difficult, and 2) determining fishing vessel types is difficult, resulting in a lack of labeled data. Hence, after designing a process framework for vessel tagging, we construct a high-resolution fine-grained fishing vessel classification dataset (FishingVesselSAR), which contains 116 gillnetters, 72 seiners, and 181 trawlers. We then propose a novel DL model (FishNet) that aims to strengthen feature extraction and utilization. In FishNet, we introduce four innovative modules to ensure superior performance in SAR fishing vessel classification: a multipath feature extraction (MUL) module, a feature fusion (FF) module, a multilevel feature aggregation (MFA) module and a parallel channel and spatial attention (PCSA) module. Furthermore, we design an adaptive loss function to achieve better classification performance by mitigating the effects of class imbalance. In this paper, we report extensive ablation studies conducted to confirm the efficacy of the five improvements listed above. Sufficient comparisons with 33 advanced methods from the DL and SAR target classification communities demonstrate that FishNet achieves a SAR fishing vessel classification accuracy of 89.79%, which is 6.77% higher than that of the second-best method.

Polarimetric inverse synthetic aperture radar (ISAR) can operate all-day and all-weather, making it crucial for space surveillance. The compact polarimetric mode balances hardware complexity and polarimetric information, which is commonly equipped with ISAR systems. Given the constraints of limited physical conditions, exploring ISAR image super-resolution is worthwhile. Currently, deep learning models have been employed for enhancing ISAR image super-resolution. However, the super-resolution performance is limited by local interpolation and the occurrence of artifacts. To address these limitations, this work presents a Non-Local Scene Awareness Network (NLSAN), which incorporates a non-local interpolation approach to capture global textures. Furthermore, a scene awareness scheme is established by integrating semantic and super-resolution information, concerning the varying levels of artifacts in different regions. The training process can be regulated by a designed penalty function to mitigate potentially generated artifacts. A dataset of compact polarimetric ISAR images of satellite targets is constructed for comparison analysis. The proposed NLSAN method yields more elaborate super-resolution results with fewer artifacts. Quantitative evaluations are also carried out using global and local indexes such as the Peak-Signal-to-Noise (PSNR), the image entropy, and the 3dB width of strong scatters. Compared with the typical state-of-the-art methods, the proposed approach achieves superior super-resolution performance, with an overall performance improvement of at least 9.2% and enhanced generalization capabilities.

A challenging issue in the field of marine remote sensing is the application of polarimetric synthetic aperture radar (PolSAR) to small ship detection in complicated environments. Several outstanding polarimetric detectors (such as the optimal polarimetric detector, polarimetric whitening filter, and polarimetric notch filter, etc.), have been effectively implemented in practical applications. A linear combination model based on quadratic optimization is summarized to establish a general framework for polarimetric detectors, transitioning the PolSAR ship target detection from a model driven approach to a hybrid (model/data)-driven approach. However, the dimension of the covariance matrix may be high, and the computation cost will be large. The higher dimension of the covariance matrix requires a bigger the data demand. As a result, when the sample size is small, the model performance will degrade. In this paper, to decrease the computational complexity and improve the robustness, we propose a novel method called the simultaneous diagonalization transform (SDT). The proposed method enables an almost simplest representation of information from the covariance matrix providing a rapid detection algorithm. The simulation experiments demonstrate that polarimetric detectors based on SDT consistently outperform those based on other methods in terms of accuracy, efficiency, and sample size requirements across various complex backgrounds. Furthermore, the effectiveness, robust, and fastness of the polarimetric detector based on SDT is validated using real data collected by RadarSAT-2, GaoFen-3, and Sentinel-1A.

VideoSAR realizes continuously scene observation and expands SAR's range-azimuth two-dimensional scattering information to range-azimuth-time three-dimensional scattering information. Considering the effects of speckle noise, a novel speckle filter for VideoSAR image was proposed in this paper using the three-dimensional information of VideoSAR image. The proposed method mainly contains three steps. Firstly, a context covariance matrix that contains spatial and temporal context information is constructed for each pixel. Then, the similar samples are selected for each pixel according to the similarity test of covariance matrices. Finally, a sample averaging estimator is applied based on the similar samples. Experiments on real VideoSAR data show that the proposed algorithm achieves effective speckle filtering performance while well preserving the edge texture.

Manmade-target three-dimensional (3D) reconstruction is an attractive topic and also a challenge in radar imaging field. The factorization based 3D reconstruction algorithm using multi-view radar images provides a way without extra configuration requirements. The precondition of it is that the reconstructed points' positions are retrievable in all images, but it is hard to be satisfied in practice due to the points loss problem caused by occlusion and scattering variation during view change. The points loss problem reduces the reconstructed points and weaken the details. We propose a modi-fied manmade-target 3D reconstruction algorithm. Firstly, we calculate homography matrix after feature matching of each adjacent image pair and improve its accuracy through non-linear optimization. Then, we put forward a track generation algorithm under the guidance of the homography matrix to estimate strong points' positions in each image and a track filter to refine the estimation. Finally, we achieve the 3D coordinates through the orthographic factorization method. The measured data processing results demonstrate the validity and the reconstruction quality improvement of the proposed method.

Compact polarimetry synthetic aperture radar (compact-pol SAR) provides relatively rich polarimetric information while maintaining a wide swath, which is quite suitable for the earth observation. Urban area is important observation of remote sensing. However, the backscattering of manmade targets, including urban areas, is sensitive to the relative geometry between target orientation and the radar line of sight, which still makes urban area extraction a challenge. Polarimetric correlation pattern is a tool to mine target hidden information within scattering diversity. This study extends the polarimetric correlation pattern to compact-pol SAR for urban area extraction, which mainly contains three steps. Firstly, pseudo quad polarimetry (qual-pol) data is generated from compact-pol SAR data based on Nord's method. Then, polarimetric correlation pattern features are extended to pseudo quad-pol data. Finally, the urban area is extracted using the superpixel CFAR detection method. Experimental studies are carried out with airborne PiSAR and spaceborne ALOS-2 datasets. Compared with traditional compact-pol features, the proposed extended polarimetric correlation pattern features achieve superior performances.

Ship monitoring is an important application of synthetic aperture radar (SAR). The constant false alarm rate (CFAR) methods are commonly used for ship detection. However, CFAR detectors usually face challenges for inshore dense ship detection. Due to the significant mixture of ship candidates and sea clutters within the clutter window, the detection threshold may be overestimated leading to many missed detections. To mitigate this issue, a superpixel-level CFAR detector is proposed. The main contribution contains two aspects. First, a labeling procedure is established for pure clutter superpixels and mixture superpixels discrimination in terms of unsupervised clustering. Second, a nonlocal topology strategy is proposed to adaptively determine a sufficient number of pure clutter superpixels for detection threshold estimation. In this vein, an adaptive superpixel-level CFAR approach is constructed and validated with Radarsat-2, Sentinel-1, and AIRSARShip-1 data sets. Comparison studies demonstrate the superiority of the proposed method. Compared with a traditional CFAR detector and two recent superpixel methods, the proposed method achieves clearly better performance for inshore dense ship regions.

Man-made target recognition is of great significance for many applications within microwave remote sensing. The scattering diversity of various man-made target structures makes radar target identification a difficult task. This work aims at mitigating this issue by mining and utilization of man-made target scattering diversity in polarimetric rotation domain with the interpretation tool of polarimetric correlation pattern. The optimal polarimetric roll-invariant feature set is collected from polarimetric correlation pattern. Then, a polarimetric roll-invariant feature coding scheme is developed for man-made target structure recognition. Moreover, polarimetric radar measurement errors in terms of channel coupling and imbalance are also considered. Experimental studies with electromagnetic computation datasets including canonical structures and an unmanned aerial vehicle (UAV) target and real spaceborne polarimetric synthetic aperture radar (PolSAR) data of a ship target are carried out. Compared with the Cameron decomposition, the proposed method exhibits better recognition performance and stronger robustness, especially for oriented man-made structures.

Man-made targets with various orientations usually exhibit scattering diversity which makes polarimetric radar target recognition difficult. The recently developed polarimetric rotation domain techniques provide solutions to understand and utilize target scattering diversity. Meanwhile, the Null-Pol theory is the important component of target optimal polarization in radar polarimetry. This work first extends the Null-Pol theory into the polarimetric rotation domain and discloses the different scattering responses from canonical man-made structures. In this vein, the Null-Pol response pattern interpretation tool is established and a set of Null-Pol features are extracted and characterized. Then, a man-made structure identification approach is proposed based on selected features derived from the Null-Pol response pattern. Comparison studies are carried out with simulated datasets of canonical structures considering polarimetric measurement errors, simulated, and measured data of unmanned aerial vehicles (UAVs). Compared with the Cameron decomposition, the proposed method achieves more accurate and stable performance.

Polarimetric radar can acquire complete polarization information and is widely used in many applications. However, target orientation relative to the radar line of sight usually exhibits significant influences on the scattering mechanisms. Recently, such target scattering diversity has been successfully characterized and utilized with the polarimetric rotation domain interpretation techniques. In radar polarimetry, target scattering responses are affected by both polarization orientation angle and polarization ellipticity angle. In this vein, this work aims at exploring and utilizing the complete target scattering diversity by extending polarimetric rotation domain techniques to the polarization ellipticity angle dimension. The main idea is to develop a three-dimension polarimetric correlation pattern (3-D PCP) interpretation tool, which can visualize and exhibit targets’ polarimetric rotation domain properties in terms of both the polarimetric orientation and ellipticity angles. Then, a set of global, local, and mutual polarimetric features are proposed to characterize the responses of a 3-D PCP interpretation tool. Especially, the curvatures in differential geometry are first introduced to describe the properties of the 3-D surface. The performance of these new polarimetric features is investigated with spaceborne polarimetric synthetic aperture radar (PolSAR) data. Experimental results demonstrate the advantage of the proposed 3-D PCP features in enhancing the target clutter ratio (TCR), especially for the weak ship area. Based on this, a non-local superpixel-level contrast measure (NSLCM) method for ship detection is proposed. Pure sea samples can be determined adaptively for salient map construction. Comparison results demonstrate better detection performance for both the inshore dense ship area and weak ship area.

Ship detection is an important task in civil or military applications and we can use polarimetric synthetic aperture radar (PolSAR). Many polarimetric detectors were proposed and achieved good performances in particular environments, such as optimal polarimetric detector (OPD), polarimetric whitening filter (PWF), polarimetric notch filter (PNF), polarimetric detection optimization filter (PDOF) and diagonal loading detector (DLD) etc. Up to know, the analytical links among different polarimetric detectors have not been found. In this work, the above polarimetric detectors are unified in mathematical forms and a general framework of polarimetric detectors based on quadratic optimization is presented. The mathematical forms are summarized as a trace of two matrices’ product. One is a detection transformation matrix and the other is the polarimetric covariance matrix of the pixel to be detected. We find that all these polarimetric detectors can be regarded as the optimization of such detection matrix, which is the key point of the general framework, and the difficulty turns to be a linear inseparable problem. Pocket Perceptron Linear Algorithm (PPLA) is used to solve the linear inseparable problem. In the case of low resolution, target detection is almost an indivisible problem, and multilayer perceptron (MLP) cannot provide better detection results than PPLA. In the case of high resolution, target detection becomes a nonlinear separable problem, and MLP is gradually superior to PPLA. Additionally, the optimal weights of the recent DLD are obtained to compare with other detectors in the general framework and the DLD is developed to a more general case (GDLD). The experiments validate the general framework of polarimetric detectors. Different detectors in the general framework are utilized and compared in both simulated and measured PolSAR data. The results show the optimal solution in the general framework can always reach the best performance, and the GDLD is the closest one to the optimal detector of the general framework.

Model-based decompositions are powerful tools for scattering mechanism interpretation of polarimetric synthetic aperture radar (PolSAR) data. By incorporating their refined physical scattering models and utilizing the excellent nonlinear data fitting capability of neural networks, a target-to-mechanism mapping network is proposed. Inputting the polarimetric features defined in the normalized polarimetric feature space, the proposed network outputs the normalized powers of four scattering components. Experimental studies demonstrate that the trained network on Pi-SAR X-band PolSAR data shows good interpretation performance and generality on the cross-observation perspective Pi-SAR X-band PolSAR data and the cross-frequency Radardat-2 C-band PolSAR data. In addition, the proposed approach has a fast interpretation speed.

The interpretation and recognition of man-made target is an important application of polarimetric radar. The scattering diversity of radar target contains rich information. The polarization interpretation theory in the rotation domain has been proposed to mine and characterize target hidden information. Recently, this interpretation technique has received plenty of attention and achieved successful applications. Based on the study, this work further utilizes the polarimetric correlation pattern interpretation tool for man-made target characterization and recognition. Its potential and advantages are verified by using canonical structures, the Slicy model with electromagnetic computation data and ship targets with polarimetric synthetic aperture radar (PolSAR) data. The experimental studies show that polarimetric correlation pattern has great potential for man-made targets recognition.

The scattering mechanism interpretation of oriented manmade targets especially those with large orientation angles is a challenging task. Recently, to fit the cross-polarization and off-diagonal terms, physically meaningful double-bounce and odd-bounce scattering models have been developed by modeling their independent orientation angles. Nevertheless, parameters inversion of these models is a nonlinear optimization procedure. The main purpose of this work is to convert the nonlinear model inversion procedure to a linear solution while inheriting the generalized models. By disclosing the latent relationship between the polarization orientation angle and double-bounce orientation angle, a double-bounce orientation angle inversion method is developed. Then a refined double-bounce scattering model is proposed and a fast general model-based decomposition is established. The comparison experiments are performed on PiSAR data. Compared with the state of art approaches, the proposed decomposition method achieved improved decomposition performance from both visual and quantitative investigation for oriented built-up areas.

Speckle filtering of polarimetric synthetic aperture radar (PolSAR) data is a necessary preprocessing step for many subsequent applications. The performances of speckle reduction and details preservation are primarily determined by the scheme of similar pixel selection. This letter mainly contributes to establish a novel approach for adaptive and efficient selection of similar pixels. The core idea is to introduce the clustering concept to collect similar samples during the similarity test of polarimetric matrices which is functioned to be a distance measure. Adaptive clustering based on fast finding of density peaks of the data elements is adapted and an automatic determination strategy of cluster numbers is developed. Then the proposed speckle filter is established based on the distance measure and the adaptive clustering. Both unmanned aerial vehicle SAR (UAVSAR) and Radarsat-2 data sets are used for experimental studies. The demonstrations of similar pixel selection for several typical scattering patterns are carried out. The similar samples have been adaptively and precisely identified by the proposed scheme. Furthermore, the comparisons of speckle filtering performances with several advanced speckle filters clearly demonstrate the efficiency and superiority of the proposed method.

For polarimetric synthetic aperture radar (PolSAR) data, the correlation of two polarization channels is sensitive to the target orientation relative to the sensor's illumination direction. In this letter, the concept of polarimetric correlation pattern is proposed to explore this scattering diversity. The core idea is to extend the polarimetric correlation from a fixed angle to the rotation domain along the radar line of sight. A set of new polarimetric features are derived, and three features with high target-to-clutter ratio (TCR) are selected for PolSAR ship detection. The proposed ship detection method mainly contains three steps: the features selection, thresholding procedure, and morphological filtering. Experimental studies with Radarsat-2 and GaoFen-3 data validate the advantage of the proposed approach, especially for inshore dense ship discrimination.

Ship detection is one of important applications for synthetic aperture radar (SAR). Speckle effects usually make SAR image understanding difficult and speckle reduction becomes a necessary pre-processing step for majority SAR applications. This work examines different speckle reduction methods on SAR ship detection performances. It is found out that the influences of different speckle filters are significant which can be positive or negative. However, how to select a suitable combination of speckle filters and ship detectors is lack of theoretical basis and is also data-orientated. To overcome this limitation, a speckle-free SAR ship detection approach is proposed. A similar pixel number (SPN) indicator which can effectively identify salient target is derived, during the similar pixel selection procedure with the context covariance matrix (CCM) similarity test. The underlying principle lies in that ship and sea clutter candidates show different properties of homogeneity within a moving window and the SPN indicator can clearly reflect their differences. The sensitivity and efficiency of the SPN indicator is examined and demonstrated. Then, a speckle-free SAR ship detection approach is established based on the SPN indicator. The detection flowchart is also given. Experimental and comparison studies are carried out with three kinds of spaceborne SAR datasets in terms of different polarizations. The proposed method achieves the best SAR ship detection performances with the highest figures of merits (FoM) of 97.14%, 90.32% and 93.75% for the used Radarsat-2, GaoFen-3 and Sentinel-1 datasets, accordingly.

Speckle reduction and target detection are usually two independent and successive procedures in SAR information processing systems. The separated implementation scheme makes the determination of a suitable combination of speckle filter and target detector a challenging task in practice. This work attempts to propose a novel integrated approach for both SAR speckle reduction and target detection. Firstly, a new representation in terms of the context scattering vector and context covariance matrix is established for information augmentation and mining for SAR data. Then, similar pixels within a large moving window are selected using matrix similarity test and the similar pixel number (SPN) is recorded. The speckle reduction can be conducted with the determined similar samples and salient targets (e.g. manmade targets) can be detected with the SPN index simultaneously. Finally, the speckle reduction and ship detection integrated processing procedure is established. Experimental studies are carried out with space-borne SAR datasets. The proposed integrated framework shows superiority in both speckle reduction and target detection performances.

Target classification is a main application of polarimetric synthetic aperture radar (PolSAR). With the PolSAR images of the same region observed on various dates, multitemporal classification plays an important role in crop discrimination and growth monitoring. However, due to the crop growth change, classification methods with good generalization are imperative. Recently, advanced deep learning techniques have achieved breakthroughs in optical image processing. Among them, convolutional neural networks (CNNs) can greatly improve accuracies in classification tasks. Because their multilayer architectures can extract the abstract feature with strong robustness to characterize the kinds' difference. In this vein, the multilayer architectures may be able to lead to the higher accuracies, as well as the better generalization in multitemporal PolSAR classification. So, this work aims to investigate multitemporal PolSAR classification using five CNNs of AlexNet, VGG16, Inception, ResNet50, and MobileNet. In detail, four roll-invariant features and two hidden features in the rotation domain are selected as inputs. Comparison experiment based on four temporal UAVSAR data with seven land covers validates the efficiency of these CNNs. For the train-used temporal, Inception can achieve the higher overall accuracies. While, for the train-not-used temporal, ResNet50 is with the better generalization.

The recognition of partially occluded targets is a difficult problem in the field of synthetic aperture radar (SAR) target recognition. To eliminate the effect of occlusion, the intuitive idea is to determine the exact location and the size of the occluded area. However, this is very difficult, even impossible in practice. In order to avoid this difficulty and to improve the recognition performance for the partially occluded target, a fusion strategy of the sparse representation (SR) model based on randomly erased images is proposed to recognize the partially occluded target. The proposed method randomly erases some areas many times in both the test samples and the training samples. The erased training samples in each erasure are used to sparsely represent the corresponding erased test sample. Finally, all the SR results are fused to recognize the test sample. The proposed method utilizes random erasure to eliminate the possible occluded region. In addition, this method uses the fusion strategy to overcome under-erasing of the occluded region and erroneous erasure of the unoccluded region. The key parameter of the proposed method is the erasure ratio only. Although the erasure is random, the recognition performance of the method is relatively stable. Therefore, the method can eliminate the influence of occlusion without determining the details of occlusion. The experimental results show that the proposed method is significantly better than the state-of-the-art methods in the case of occlusion. Additionally, the recognition performance of the proposed method is similar to some comparison methods in the case of no occlusion.

Speckle filtering of synthetic aperture radar (SAR) image is a necessary pre-processing for many subsequent applications. The challenge lies in how to adaptively select a sufficient number of similar pixels for an unbiased estimator generation. A novel SAR speckle filter is proposed and the core idea contains two aspects. Firstly, a context covariance matrix representation is developed within a local neighborhood to characterize the contexture information. Then, the Wishart statistic test is extended to examine the similarity of context covariance matrices. The extended similarity test indicator derived from context covariance matrices is verified to be sensitive for similar pixel localization. Thereafter, a sample averaging estimator is adopted based on the similar samples determined by the context covariance matrices similarity test (the proposed method is named as the CCM+SimiTest). Furthermore, a fast similarity test computation scheme is established which can handle large images smoothly even with a normal laptop. Intensive experimental studies with Radarsat-2, MiniSAR and ALOS-2 datasets are carried out. Comparisons with several state-of-the-art methods from both subjective and objective viewpoints demonstrate the superiority of the proposed method.

Roll-invariant polarimetric features which are independent of target orientation angle along the radar line of sight are popularly adopted in many radar application fields. During the development history of radar polarimetry, a number of roll-invariant polarimetric features have been reported in terms of different polarimetric matrix formulations, different target decomposition approaches and different scattering mechanism interpretation tools. Currently, there is lack of a comprehensive summary of these valuable roll-invariant polarimetric features. Also, their inner-relationships need to be further disclosed. Finally, deep investigations of their application potentials are essentially necessary. This work is dedicated to these aforementioned issues and a survey of roll-invariant polarimetric features is carried out.

Ship detection is an important application in polarimetric synthetic aperture radar (PolSAR) image. A novel saliency detector for PolSAR ship detection has been proposed in this work, which considers ship targets as salient candidates from sea clutter in low and medium sea conditions. Firstly, the similarity test based on polarimetric covariance matrix is applied on each pixel within its neighborhood. Then, saliency feature named Similar Pixel Number (SPN) is generated by calculating the similar samples within its neighborhood. Finally, ship targets can be obtained through proper thresholding procedure and morphological filtering. Experimental studies with two Radarsat-2 datasets validate the advantages of the proposed method.

Deep convolutional neural network (CNN) techniques have been utilized to enhance polarimetric synthetic aperture radar (PolSAR) image classification performance. This work contributes to a current challenge that is how to adapt deep CNN classifier for PolSAR classification with limited training samples while keeping good generalization performance. A polarimetric-feature-driven deep CNN classification scheme is established with both classical roll-invariant polarimetric features and hidden polarimetric features in the rotation domain to drive the proposed deep CNN model. Comparison studies validate the efficiency and superiority of the proposal. For the benchmark AIRSAR data, the proposed method achieves the state-of-the-art classification accuracies. Meanwhile, the convergence speed from the proposed CNN approach is about 2.3 times faster than the normal CNN method. For multi-temporal UAVSAR datasets, the proposed scheme achieves comparably high classification accuracies as the normal CNN method for train-used temporal data, while for train-not-used data it obtains average 4.86% higher overall accuracy than the normal CNN method. Furthermore, the proposed strategy can also produce very promising classification accuracy with very limited training samples.

Polarimetric synthetic aperture radar (PolSAR) image classification is an important application. Advanced deep learning techniques represented by deep convolutional neural network (CNN) have been utilized to enhance the classification performance. One current challenge is how to adapt deep CNN classifier for PolSAR classification with limited training samples, while keeping good generalization performance. This letter attempts to contribute to this problem. The core idea is to incorporate expert knowledge of target scattering mechanism interpretation and polarimetric feature mining to assist deep CNN classifier training and improve the final classification performance. A polarimetric-feature-driven deep CNN classification scheme is established. Both classical roll-invariant polarimetric features and hidden polarimetric features in the rotation domain are used to drive the proposed deep CNN model. Comparison studies validate the efficiency and superiority of the proposal. For the benchmark AIRSAR data, the proposed method achieves the state-of-the-art classification accuracy. Meanwhile, the convergence speed from the proposed polarimetric-feature-driven CNN approach is about 2.3 times faster than the normal CNN method. For multitemporal UAVSAR data sets, the proposed scheme achieves comparably high classification accuracy as the normal CNN method for train-used temporal data, while for train-not-used data it obtains an average of 4.86% higher overall accuracy than the normal CNN method. Furthermore, the proposed strategy can also produce very promising classification accuracy even with very limited training samples.

Polarimetric coherence, which has the potential to reveal physical properties of scatterers, is an important source for polarimetric synthetic aperture radar (PolSAR) data investigation. Target structure and orientation relative to the PolSAR illumination direction are key factors affecting the polarimetric coherence degree. The relative orientation between a sensor and a target can be adjusted using the rotating processing along the radar's line of sight. The main idea of this paper is to extend the traditional polarimetric coherence at a given rotation state (θ = 0) to the rotation domain (θ ∈ [-π, π)) along the radar's line of sight for hidden information exploration. A visualization and characterization tool named as a polarimetric coherence pattern for two arbitrary polarization channels is proposed and developed. This interpretation tool is able to view the variation of polarimetric coherence in the rotation domain containing rich orientation diversity information which is seldom considered. A set of characterization features are derived to completely describe a polarimetric coherence pattern thereafter. Experimental studies with unmanned aerial vehicle SAR (UAVSAR) PolSAR data over crop areas have validated that polarimetric coherence patterns vary in terms of polarization combinations and crop types. The proposed characterization features show good potential to differentiate polarimetric responses from different land covers. Furthermore, a classification scheme combining the selected proposed features and the commonly used roll-invariant features is developed for quantitative and application investigation. Comparison studies with both UAVSAR and Airborne SAR (AIRSAR) data clearly demonstrate the superiority of the proposed classification to the conventional classification with only roll-invariant features. The overall classification accuracies for the seven and eleven land covers of UAVSAR and AIRSAR data are, respectively, increased from 90.21% and 93.87% to 95.12% and 94.63% by the proposed classification scheme. This paper also demonstrates the importance of and potential for utilizing the complementary advantages of roll-invariant features and the proposed roll-variant features.

In synthetic aperture radar (SAR) images, scattering centers (SCs) from the same geometric structure of the man-made target usually have the same scattering type and similar coordinates. Inspired by this observation, a novel clustering-based geometrical structure retrieval (C-GSR) method is proposed to estimate the geometrical structure of targets by clustering SCs according to their types and coordinates. The C-GSR method considers each peak in a SAR image as a single SC and extracts both frequency and polarization features for classification. Then, SCs are efficiently clustered using the density-distance-based clustering algorithm. Finally, the geometrical structure corresponding to each canonical scatterer can be retrieved by computing the coordinates of SCs associated with the corresponding cluster. Experimental results have demonstrated the feasibility and accuracy of the proposed C-GSR method.

Polarimetric coherence, which has the potential to reveal physical properties of scatterers, is an important source for polarimetric SAR (PolSAR) data investigation. Target orientation relative to the PolSAR illumination direction is one key factor affecting the polarimetric coherence degree. The relative orientation between a sensor and a target can be adjusted using the rotating processing along the radar line of sight. The corresponding polarimetric coherence can be varied in the rotation domain for roll-variant scatterers. The variation of polarimetric coherence in the rotation domain contains rich hidden information which is seldom considered. This work focuses on the hidden feature exploration of polarimetric coherence in the rotation domain. A framework including a visualization tool named polarimetric coherence pattern which is able to view the polarimetric coherence properties in the rotation domain and a set of parameters to characterize the polarimetric coherence pattern has been proposed. Experimental studies validate the efficiency of the proposal.

A quick response to a large-scale natural disaster such as earthquake and tsunami is vital to mitigate further loss. Remote sensing, especially the spaceborne sensors, provides the possibility to monitor a very large scale area in a short time and with regular revisit circle. Damage ranges and damage levels of the destructed urban areas are extremely important information for rescue planning after an event. Rapid mapping of the urban damage levels with synthetic aperture radar (SAR) is still challenging. Compared with single-polarization SAR, fully polarimetric SAR (PolSAR) has a better potential to understand the urban damage from the viewpoint of scattering mechanism investigation. In radar polarimetry, the dominant double-bounce scattering mechanism in an urban area is primarily induced by the ground-wall structures and can reflect the changes of these structures. In this sense, urban damage level in terms of destroyed ground-wall structures can be indicated by the reduction of the dominant double-bounce scattering mechanism, which is the basis of this study. This work first establishes and validates the linear relationship between the urban damage level and the proposed polarimetric damage index using polarimetric model-based decomposition. Then, efforts are focused on the development of a rapid urban damage level mapping technique which mainly includes two steps of urban area extraction and polarimetric damage level estimation. The 3.11 East Japan earthquake and tsunami inducing great-scale destruction are adopted for study using L-band multitemporal spaceborne PolSAR data. Experimental studies demonstrate that the estimated damage levels are closely consistent to the ground-truth. The final urban damage level map for the full scene is generated thereafter. Results achieved in this study further validate the necessity of exploring fully polarimetric technique for damage assessment.

Mapping of the urban damage levels with synthetic aperture radar (SAR) is still challenging. Fully polarimetric SAR (PolSAR) has the potential to identify the type of scattering mechanism changes induced by urban damage. In radar polarimetry, dominant double-bounce scattering mechanism in urban areas is primarily induced by the ground-wall structures. Thereby, within an urban patch, the reduction of the dominant double-bounce scattering mechanism reflects the urban damage level in terms of destroyed ground-wall structures, which is the basis of this study. Based on our previous study, the proposed polarimetric index is further investigated. A rapid urban damage mapping technique including mainly two steps of urban area extraction and damage level index estimation is proposed. The 3.11 East Japan Earthquake and Tsunami is adopted as the study case using multi-temporal ALOS/PALSAR PolSAR data. Experimental studies demonstrate that the estimated damage levels are closely consistent to the ground-truth.

This paper focuses on earthquake/tsunami damage investigation over urban areas by exploring the multitemporal spaceborne ALOS/PALSAR PolSAR data. The polarimetric scattering mechanism changes before- and after-tsunami, at the city block scale, have been examined using model-based decomposition and PO angle techniques. These analyzes are used to establish the relationships between the polarimetric scattering mechanism changes and damage levels. The basic scattering structures such as ground-wall dihedral structures from the built-up areas were found to be stable even over a long temporal baseline. Two polarimetric indexes have been proposed for damage level indication. Experimental results validate the efficiency of these two indicators, since the built-up areas with different damage levels can be well discriminated. These results demonstrate the importance and efficiency of full polarimetric information for natural disaster assessment.

The overestimation of volume scattering power and the scattering mechanism ambiguity are still present in model-based decompositions even with the implementation of the deorientation processing. These effects are demonstrated and investigated. One possible reason is because of the limited dynamic range of the models themselves that are not fully satisfied for the mixed scene cases. An empirical volume scattering model is proposed, using the repeat-pass polarimetric synthetic aperture radar interferometry (PolInSAR) coherence, to extend the model dynamic range to be more adaptive. PolInSAR coherence is sensitive to different types of forests and terrains. The proposed model inherits these characteristics. In addition, it considers the cross-polarization power induced by oriented man-made structures. Thereby, a model-based polarimetric decomposition scheme is developed. The efficiency of the proposed method is demonstrated using E-SAR airborne and ALOS/PALSAR spaceborne repeat-pass PolInSAR datasets. Comparative experiments are carried out and show that the proposed decomposition overcomes the scattering mechanism ambiguity between forests and oriented built-up areas, since it successfully identifies the oriented buildings as double- or odd-bounce man-made structures while keeping the volume scattering dominant for the forests. Besides, the stable decomposition performance over the oriented built-up patches with quite different orientation angles also validates the improvement of the proposed decomposition. In addition, the demonstrations with short and long temporal baselines validate the generality of the proposed method.

This paper presents the development of a uniform polarimetric matrix rotation theory in the rotation domain along the radar line of sight for polarimetric synthetic aperture radar (PolSAR) data interpretation. The uniform representation of each coherency matrix element is a sinusoidal function in the rotation domain. A set of oscillation parameters, including oscillation amplitude, oscillation center, angular frequency, and initial angle, is proposed to fully characterize the scattering behavior in the rotation domain. A set of rotation angle parameters, including stationary angle, null angle, and minimization/maximization angles, is derived to indicate specific states of the rotation property. The rotation relationships between the coherency and covariance matrices with linear and circular polarization bases are established. A look-up table for these parameters is provided, and their physical meanings are interpreted. These derived parameters directly link to the Huynen parameters. Therefore, the proposed theory has the ability to achieve a desired state of one Huynen parameter by rotating the polarimetric matrix at a designated rotation angle. This theory also generalizes both the classic polarization orientation angle originally derived from the covariance matrix in a circular polarization basis and the deorientation theory developed from the minimization of the cross-polarization term. The roll-invariant terms have also been summarized. Finally, multifrequency Pi-SAR and AIRSAR PolSAR data sets are used to demonstrate the derived parameters. One oscillation amplitude parameter has been verified to be especially suitable for characterization of oriented man-made targets. Two angle parameters are sensitive to the reflection symmetry condition and crop types. Therefore, a simple unsupervised classification scheme has been developed and demonstrated. Further utilization perspectives of the proposed theory have been discussed.

Orientation angle compensation was incorporated into model-based decomposition to cure overestimation of the volume scattering contribution for interpretation of polarimetric synthetic aperture radar (PolSAR) data. The compensation is based on rotating the coherency matrix to minimize the cross-polarization term. However, this processing cannot always guarantee that the double- and odd-bounce scattering components will be rotated back to zero orientation angle and left with zero cross-polarization power. As a result, built-up patches with large orientation angles may still suffer from the scattering mechanism ambiguity. In this paper, double- and odd-bounce scattering models were generalized to fit the cross-polarization and off-diagonal terms, by separating their independent orientation angles. A general decomposition framework is proposed that utilizes all elements of a coherency matrix. The residual minimization criterion is used for model inversion. All the model parameters are simultaneously obtained using a nonlinear least squares optimization technique. The manual intervention, branch conditions, and negative power issues are avoided. The performance and advantages of this approach are demonstrated and evaluated with spaceborne L-band ALOS/PALSAR and airborne X-band Pi-SAR PolSAR data sets. Comparison studies are also carried out and demonstrate that further improved decomposition performance is achieved by the proposed method, especially in oriented built-up areas.

This paper presents the development of a uniform polarimetric matrix rotation theory in the rotation domain along the radar line of sight for polarimetric SAR (PolSAR) data interpretation. The uniform representation of each coherency matrix element is a sinusoidal function in the rotation domain. A set of oscillation parameters, including oscillation amplitude, oscillation center, angular frequency and initial angle, is proposed to fully characterize the scattering behavior in the rotation domain. A set of rotation angle parameters, including stationary angle, null angle, and minimization/maximization angles, is derived from the angular frequency and initial angle to indicate the specific states of the rotation property. A look-up table for these parameters is provided and their physical meanings are interpreted. The proposed theory generalizes both the classic polarization orientation (PO) angle originally derived from the covariance matrix in a circular polarization basis and the deorientation theory developed from the minimization of the cross-polarization term. The roll-invariant terms have also been summarized. Finally, multi-frequency AIRSAR and Pi-SAR PolSAR data sets are used to demonstrate the derived parameters.

SAR Interferometry is a common methodology for detection of Natural disaster affected area. However, we applied radar polarimetric analysis to ALOS/PALSRA data sets to investigate the possibility of detection of Tsunami affected area only from the SAR data acquired after the event. The resolution of the SAR image is 30m by 30m and cannot identify each house or buildings in the area. However, we have demonstrated that single-bonce and double-bounce scattering mechanism clearly shows the Tsunami affected area in Ishinomaki city, Japan, after the Great East Japan Earthquake and Tsunami. We could also showed that the standard deviation of the polarimetric orientation angle increases by the damage, and can be used for the measure of the damage.

Deorientation processing has been incorporated into model-based decomposition to cure the overestimation of volume scattering contribution, by rotating the coherency matrix to minimize the cross-polarization term. First, the derivation of the rotation angle is clarified for avoiding the ambiguity. Moreover, even with the implementation of deorientation processing, oriented built-up areas with large orientation angles are still misjudged as volume scattering dominant. Further to the investigation of the deorientation effect, we focus on oriented built-up patches. A parameter, named dominant polarization orientation angle (DPOA), is introduced to label each patch. The behavior of the deorientation on coherency matrix and model-based decomposition over purely oriented built-up areas with respect toDPOAis disclosed. Experimental studies from the Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) polarimetric SAR data set demonstrate that model-based decompositions with deorientation work well for oriented built-up areas when |DPOA| ≤ 22.5°. However, for large |DPOA| (e.g., |DPOA| >; 22.5°), even with the deorientation processing, for the conventional decompositions which assume that only the volume scattering contributes to the cross-polarization term, the decomposed volume scattering power may also be dominant even for purely oriented built-up areas. Thereby, misinterpretation still occurs, motivating further advancements.

This paper explores the use of full polarimetric synthetic aperture radar (PolSAR) images for tsunami damage investigation from the polarimetric viewpoint. The great tsunami induced by the earthquake of March 11th, 2011, which occurred beneath the Pacific off the northeastern coast of Japan, is adopted as the study case using the Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar multitemporal PolSAR images. The polarimetric scattering mechanism changes were quantitatively examined with model-based decomposition. It is clear that the observed reduction in the double-bounce scattering was due to a change into odd-bounce scattering, since a number of buildings were completely washed away, leaving relatively a rough surface. Polarization orientation (PO) angles in built-up areas are also investigated. After the tsunami, PO angle distributions from damaged areas spread to a wider range and fluctuated more strongly than those from the before-tsunami period. Two polarimetric indicators are proposed for damage level discrimination at the city block scale. One is the ratio of the dominant double-bounce scattering mechanism observed after-tsunami to that observed before-tsunami, which can directly reflect the amount of destroyed ground-wall structures in built-up areas. The second indicator is the standard deviation of the PO angle differences, which is used to interpret the homogeneity reduction of PO angles. Experimental results from after- and before-tsunami comparisons validate the efficiency of these indexes, since the built-up areas with different damage levels can be well discriminated. In addition, comparisons between before-tsunami pairs further confirm the stability of the two polarimetric indexes over a long temporal duration. These interesting results also demonstrate the importance of full polarimetric information for natural disaster assessment.

Orientation angle compensation has been incorporated into model-based decomposition to cure the overestimation of volume scattering contribution, by rotating the coherency matrix to minimize the cross-polarization term. However, this processing cannot always guarantee to rotate the double-and odd-bounce scattering components back to zero orientation angle cases and with zero cross-polarization power. Therefore, built-up patches with large orientation angles still suffer from the scattering mechanism ambiguity. General double- and odd-bounce scattering models are proposed to fit for the cross-polarization and off-diagonal terms, by separating their independent orientation angles. The general decomposition framework is proposed. Its efficiency and advantage is demonstrated and evaluated.

The earthquake and tsunami of March 11, 2011 killed more than 15 000 people in Eastern Japan. The importance of remote sensing in understanding the damage caused by natural disasters is quite significant, and many data sets were acquired after the events. In this paper, we demonstrate the importance and the potential of full polarimetric synthetic aperture radar (SAR) images for damage assessment. Full polarimetric SAR images acquired by the spaceborne ALOS/PALSAR system from the Japan Aerospace Exploration Agency (JAXA) on November 21, 2010 and April 8, 2011 and acquired by the airborne Pi-SAR2 system from the National Institute of Information and Communications Technology (NICT) on March 12 and 18, 2011 are used for this analysis. Model-based decomposition is applied and clearly shows the scattering mechanism changes at the seriously damaged downtown of Ishinomaki city and the flooded areas near the main stream of the Kitakami River. Polarization orientation angle is estimated to provide additional information to understand the damage effect in the built-up areas. Eigenvalue-eigenvector-based decomposition analysis is also employed to further confirm the scattering mechanism changes of the flooded areas. ALOS/PALSAR does not have fine enough resolution; however, the difference of the scattering mechanisms is sufficient to identify the damaged and flooded areas. In addition, the Pi-SAR2 data sets are used to analyze the flooded paddy fields in Natori city. The relative backscattering values are compared with the multitemporal images and the cross-polarization component (HV) is observed to be more sensitive to the flooded boundary. The automatically detected flooding maps using the cross-polarization component were found to provide relatively accurate results.

Most polarimetric synthetic aperture radar interferometry (PolInSAR) data processing procedures and their applications are based on the polarimetric complex coherence descriptor. The reliable estimation of the complex coherence requires selecting sufficient homogeneous pixels for generating an unbiased estimator. In this paper, two indicators using only polarimetric and both polarimetric and interferometric information are derived as the similarity measures for complex Wishart distributed PolInSAR covariance matrix, respectively. Using these indicators, a double similarity test scheme, which shows high sensitivity to both polarimetric and interferometric properties, is proposed for similar pixel selection. The full information utilization could characterize the homogeneous pixels more accurately. Furthermore, since the similarity test has the potential to reject the pixels with different populations, it is suitable to be applied in a large searching area (e.g., 15 × 15 window) to accept sufficient homogeneous pixels. Thereby, combining with unbiased estimator, reliable estimation is achieved. The efficiency and advantage of the proposed estimation scheme are demonstrated with the aid of simulated and real PolInSAR data sets.

A volume scattering model utilizing polarimetric SAR interferometry (PolInSAR) coherence is proposed. The new model is more adaptive and better fit for both forest and skew-oriented built-up areas. Thereby, a new model-based polarimetric decomposition scheme is developed. The advantage of the proposed method is demonstrated by E-SAR PolInSAR data. And comparison experiments show that better decomposition results are achieved by the proposed method, since all the oriented built-up areas are well discriminated as double or odd bounce structures.

In this study, we consider tree vertical profile with a higher order truncation of Fourier-Legendre series using polarization coherence tomography. An improved method based on Freeman-Durben decomposition is employed to estimate tree height and surface phase. A contrast to single-baseline scenario is performed. The primary scattering mechanisms and polarization dependence are interpreted by vertical profile reconstructions. Results are validated using simulation data