Synthetic Aperture Radar (SAR) imaging relies on using focusing algorithms to transform raw measurement data into radar images. These algorithms require knowledge of SAR system parameters, such as wavelength, center slant range, fast time sampling rate, pulse repetition interval, waveform, and platform speed. However, in non-cooperative scenarios or when metadata is corrupted, these parameters are unavailable, rendering traditional algorithms ineffective. To address this challenge, this article presents a novel parameter-free method for recovering SAR images from raw data without the requirement of any SAR system parameters. Firstly, we introduce an approximated matched filtering model that leverages the shift-invariance properties of SAR echoes, enabling image formation via convolving the raw data with an unknown reference echo. Secondly, we develop a Principal Component Maximization (PCM) method that exploits the low-dimensional structure of SAR signals to estimate the reference echo. The PCM method employs a three-stage procedure: 1) segment raw data into blocks; 2) normalize the energy of each block; and 3) maximize the principal component's energy across all blocks, enabling robust estimation of the reference echo under non-stationary clutter. Experimental results on various SAR datasets demonstrate that our method can effectively recover SAR images from raw data without any system parameters. To facilitate reproducibility, the Matlab program is available at https://github.com/huizhangyang/pcm.

Significant efforts have been made to develop techniques for extracting useful information from remote sensing (RS) images in view of their favorable properties such as wide coverage, good timeliness and large content. Such techniques often involve scene classification, which provides an effective way to interpret RS data. However, large intra-class variances and high inter-class similarities make remote sensing scene classification (RSSC) a tough task. Recently, deep learning has been brought to bear upon RSSC. Deep models with ever-improving classification accuracy are continually being put forward. This letter shifts focus from network structure optimization to the loss function and takes a principled approach based on optimal transport (OT). OT measures the divergence between two probability distributions by computing the minimum cost of converting one distribution to the other, which can help discriminate different data clusters in high-dimensional feature spaces. Relaxation of marginal constraints during computation of OT objective can accommodate certain potentially desirable transportation plans that are otherwise inadmissible. However, the effect of OT objective is sensitive to the extent of marginal relaxation. In this work, we propose a method, named exact marginal relaxation robust optimal transport (EMRROT), to estimate the optimal threshold for marginal relaxation and investigate its application to RSSC. Experimental results show that the proposed EMRROT achieves highly competitive performances on the benchmark RSSC datasets.

Deep learning methods have been studied for polarimetric synthetic aperture radar (PolSAR) land-cover classification. However, these methods often lack interpretability. This article proposes a module based on the hidden Markov model (HMM). It retains the overall structure of the neural network while correcting some easily detectable errors made by the neural network using a statistical model. This module is added after the training of the neural network is completed, and it does not require complex training once the neural network model is determined. Furthermore, the classification accuracy is improved when this module is added to various neural networks. Experiments are conducted using the Hainan dataset acquired by the Aerial Remote Sensing System of the Chinese Academy of Sciences. The experimental results demonstrate the superiority of the proposed method.

Radar transmits active agile waveformplays a significant role in anti-jamming. However, efficient coherent integration of target's agile echo in a coherent processing interval (CPI) usually poses a severe challenge. This letter proposes an improved sparsity adaptive matching pursue (ISAMP) to address this issue. Firstly, the agile echo signal model of random interpulse frequency and PRT joint agile (RI-FPrtJA) waveform is derived; The sparse reconstruction model of RI-FPrtJA echo signal is then mathematically deduced based on compress sensing theory; Lastly, the ISAMP is proposed to accurately accomplish sparse reconstruction based on the regularized grids mismatch correction and adaptive searching step extension. The simulation results demonstrate that the ISAMP method can achieve better coherent integration in terms of mismatch sidelode levels suppression, sparse reconstruction capacity, and computational cost, compared to some current existing methods.

The rapid advancement of Transformer-based large language models (LLMs) is presenting significant challenges for their deployment, primarily due to their enormous parameter sizes and intermediate results, which create a bottleneck in memory capacity for effective inference. Compared to traditional DRAM, Non-Volatile Memory (NVM) technologies such as Resistive Random-Access Memory (RRAM) and Phase-Change Memory (PCM) offer higher integration density, making them promising alternatives. However, before NVM can be widely adopted, its reliability issues, particularly manufacturing defects and endurance faults, must be addressed. In response to the limited memory capacity and reliability challenges of deploying LLMs in NVM, we introduce a novel low-overhead weight-level map, namedWolf.Wolfnot only integrates the addresses of faulty weights to support efficient fault tolerance but also includes the addresses of outlier weights in LLMs. This allows for tensor-wise segmented quantization of both outliers and regular weights, enabling lower-bitwidth quantization. TheWolfframework uses a Bloom Filter-based map to efficiently manage outliers and faults. By employing shared hashes for outliers and faults and specific hashes for faults,Wolfsignificantly reduces the area overhead. Building onWolf, we propose a novel fault tolerance method that resolves the observed issue of clustering critical incorrect outliers and fully leverages the inherent resilience of LLMs to improve fault tolerance capabilities. As a result,Wolfachieves segment-wise INT4 quantization with enhanced accuracy. Moreover,Wolfcan adeptly handle Bit Error Rates as high as$1 {\boldsymbol{\times}} 10^{-2}$without compromising accuracy, in stark contrast to the state-of-the-art approach where accuracy declines by more than 20%.

Remote sensing scene classification categorizes imaged areas by ground contents to provide semantic information for Land Use and Land Cover monitoring. The remote sensing images exhibit diverse scene targets, textures, shapes, and spatial arrangements under varying solar illumination, therefore needing high-level feature analysis for effective scene characterization. Existing methods, including the widely used conventional neural networks (CNNs), often fall into local minima during the optimization process due to the non-convexity of the loss function over the parameter space. Furthermore, existing methods have two limitations in feature extraction: single-scale features are not adaptive to the spatial variation, and simply concatenating multi-scale features neglects the geometric relationships among resolutions. Moreover, the flattening operation commonly used in network architectures tends to destroy the inherent topological structure of images, leading to a loss of spatial information in the scene representation. To address these issues, in this study, a Chaos-based multi-scale tensor pyramid network (CMTPN) is proposed. Firstly, a 3D discrete chaotic system is proposed based on both the logistic map and the tent map to achieve a more uniform distribution and a broader full chaotic map range. The map is then embedded into the neural network structure with the aim of obtaining an optimization result. Secondly, a multi-scale tensor pyramid network is proposed. This network extracts local geometric details and global contextual information through parallel branches, in which the Tensor Contraction Layer is utilized to combine features from different scales by using attention mechanisms to adaptively integrate high- and low-resolution information, and meanwhile the Parallel Tensor Regression Layer breaks down the multi-scale features using the Tucker decomposition for the final classifications. Effectiveness of the proposed algorithm is demonstrated through extensive experiments conducted on three remote sensing benchmark datasets: NWPU-RESISC45, AID, and OPTIMAL-31, showing that the proposed CMTPN not only achieves superior classification accuracy but also maintains competitive computational efficiency, robustly outperforming state-of-the-art methods.

This work introduces DiSpeckle, a diffusion-based SAR despeckling method in which a physics-inspired stochastic differential equation (SDE) governs the bidirectional transition process between speckled and clean images. The key insight is that the forward diffusion process closely parallels the physical formation of speckle noise—both involve the accumulation of microscopic random perturbations that progressively distort the underlying signal. Building on this analogy, the forward diffusion process is designed using an SDE that emulates the speckle formation mechanism, namely the coherent aggregation of unresolved backscattered signals. Despeckling is then achieved by inverting this process through the corresponding reverse SDE. Unlike previous diffusion-based methods that rely on transforming speckle into an approximately Gaussian distribution, this physics-inspired formulation offers a mathematically rigorous and physically consistent pathway from speckled to clean imagery. In addition, DiSpeckle provides a flexible framework that seamlessly integrates off-the-shelf Gaussian denoisers. Experiments demonstrate that DiSpeckle—even when using off-the-shelf Gaussian denoisers—matches or outperforms state-of-the-art despeckling methods. With fine-tuned denoisers, it achieves superior performance while requiring only one-third of the training data. The code will be made publicly available on GitHub upon publication.

This article proposes a novel eigenvalue-based detector, called Lambda-1 detector, for adaptive and robust interference detection in single-look-complex (SLC) synthetic aperture radar (SAR) images. The proposed method leverages the increased eigenvalues caused by interference in SAR image blocks, where the interference is expected to have a small set of eigenvalues, particularly with a dominating one. Specifically, the method segments the image into multiple blocks, computes the eigenvalues of each block’s covariance matrix, and compares the largest eigenvalue$\lambda _{1}$with a threshold to determine the presence of interference under the criteria of constant false alarm rate (CFAR), thereby enabling adaptive interference detection against varying levels of interference-to-signal ratios (ISRs). The largest eigenvalue is characterized by the order-2 Tracy-Widom distribution (no closed-form expression) under the assumption of the image’s homogeneity, and the threshold is adaptively determined based on a scaled and shifted Gamma distribution that fits this distribution with a closed-form expression. The method is robust by first modeling and then correcting the impacts of upsampling and windowing of SAR image data on the fit distribution’s parameters, and by incorporating outlier removal preprocessing. Experimental results validate the effectiveness of the proposed method in successfully detecting both strong and weak interferences in various SAR images, including Sentinel-1 and Gaofen-3. The detection performance is quantitatively evaluated using false alarm rate$P_{\mathrm { fa}}$and detection rate$P_{d}$. In summary, the proposed Lambda-1 detector effectively identifies interference artifacts in focused SAR images and holds promise for improving the quality of SAR imagery by incorporating adaptive interference removal.

This paper investigates the effect of current-wave interaction on the ocean surface current (OSC) retrieval under the DCA framework using an improved Doppler radar imaging model (IDopRIM). Both numerical simulation data and a real SAR observation image in scenarios involving ocean internal waves (IW) are utilized for analyses. Experimental results suggest that the contribution of current-wave interaction to the sea surface Doppler velocity cannot be ignored for the IW ocean surface with rapidly varying currents. Under certain conditions, the relative errors of the OSC retrieval due to neglecting such a contribution could be larger than 30%.

Polarimetric High-Resolution Range Profile (HRRP) holds significant potential in Radar Automatic Target Recognition (RATR) due to its ability to provide detailed polarimetric and spatial information. In recent years, deep learning methods have been extensively applied in RATR based on polarimetric HRRP. However, these methods often focus solely on local or temporal features, thereby not fully utilizing the spatial information. Additionally, valuable polarimetric information is frequently overlooked. Moreover, most of these methods do not distinguish between target and noise areas, resulting in a lack of emphasis on crucial range cells. This paper introduces a method for polarimetric HRRP recognition based on the Vision Transformer (ViT), which effectively extracts both local and temporal features. Our approach incorporates a polarimetric preprocessing step in which manual features and Convolutional Neural Networks (CNNs) are combined to enhance the extraction of polarimetric features. To direct the network’s focus toward significant range cells, we design a novel attention loss. Experimental results demonstrate that our proposed method improves recognition accuracy and maintains robustness in noisy environments.

The active jamming suppression of Synthetic Aperture Radar (SAR) images remains a severe challenge. The jamming SAR images simulation dataset is firstly built by open SSDD dataset and random shift-frequency jamming type; The formula of existing diffusion model is then modified using the low-rank based block space filter (BSF) theory; Lastly, jamming SAR images are as the inputs to effectively train our proposed model to generate the jamming suppressed images. Experimental results qualitatively and quantitatively demonstrate the effectiveness of the proposed method.

In this paper, we propose an edge attention superpixel segmentation method for polarimetric synthetic aperture radar (PolSAR) images. Image edges are specially considered during the segmentation process by imposing an edge-attention-based distance. The edge-attention-based distance is defined on an edge map and it ensure that one superpixel is not divided into several parts by image edges, which helps to generate superpixels with better boundary adherence. Experimental results on AirSAR and Radarsat-2 images demonstrate the superiority of the proposed method.

A sea-land segmentation method in polarimetric SAR images is proposed based on thresholding using polarimetric likelihood ratio in this article. Firstly, the volume scattering power is extracted based on three-component decomposition. Then, a typical sampling region of the sea is extracted based on the mean and variance of the region to determine the range interval of the threshold. Finally, the optimal threshold such that the polarimetric likelihood ratio between the sea and land region reaches the maximum is searched in the interval. Experiments of different sensors of data in San Francisco including AirSAR, RADARSAT-2, TerraSAR-X and Gaofen-3 show that both the precision and recall rates of the proposed method are higher than 90%, significantly surpassing those of the comparison methods.

SAR despeckling is a crucial preprocessing step for various SAR applications. This work introduces SpeckleDiff2D, a complex-valued speckle reduction diffusion network. Our approach emphasizes the design of a forward noise-accumulation process that mimics the generation mechanism of speckle noise. The proposed despeckling algorithm is trained exclusively on synthesized speckled images generated by optical dataset and is validated on both simulated and real SAR images. Experimental results demonstrate the method’s efficiency and resilience to domain shifts, positioning it as a promising solution to the persistent challenge of data insufficiency in SAR despeckling design.

In this paper, the characterization of different target scattering vector models for the physical scattering mechanism of a target is compared, focusing on the differences between our proposed target scattering vector model and other classical scattering vector models. The comparison will be in two parts, depending on the type of data in use. For the analysis of the physical scattering mechanism of single-look data, the α/β scattering vector model, the con-diagonalization scattering vector model developed by Touzi, and our proposed αBscattering vector model are compared for the effect of characterizing the physical scattering mechanism of the target, respectively. For the analysis of the physical scattering mechanism of multi-look data, the effects of H/α decomposition and our proposed ∆αB/αBdecomposition on the classification of the target areas are compared, and the results are combined with the Wishart iterative classifier to optimize the classification results further. The experiments are performed on RADARSAR-2 (C-band) and ALOS-2 (L-band) data from San Francisco, USA. The results show that for single-look data, the αBscattering vector model is superior in overall performance. For multi-look data, the H/α and ∆αB/αBdecompositions tend to misclassify volume and double scattering, respectively, and the ∆αB/αBmay have better results than the H/α decomposition for the application of man-made targets dominated by double scattering.

The difficulty of coastline detection in polarimetric SAR images is that the coast zone includes both the sea noised by the sidelobe echo of strong scattering targets and intertidal zones with varying water content. Methods based solely on the statistical distribution of quad-polarization coherent matrix or single-channel scattering intensity would result in incorrect segmentation. In this paper, a coastline detection method in polarimetric SAR images based on three-region Markov random field (MRF) segmentation with embedded components obtained by Freeman decomposition is proposed. Firstly, the powers of Freeman three-component decomposition of the land cover are obtained. Then, the 3 × 3 scattering coherent matrix is decomposed into two parts of volume scattering power and non-volume scattering 2×2 coherent matrix based on the reflective symmetry of natural land covers. A joint probability distribution of the two parts is constructed and embedded into the three-region MRF model to segment the image into sea, strong scattering, and other regions. The coastline is finally extracted by determining the boundary of the segmented sea region. To improve the accuracy and robustness of the initial segmentation, a threshold segmentation method by sampling typical regions is proposed to segment the double-bounce and volume scattering powers. Experimental results using RADARSAT-2 data from regions in Dalian and Boao of China, and Singapore demonstrate that the proposed method accurately detects coastlines in different scenes, with an average offset of less than 2 pixels. The performance is significantly superior to coastline detection methods based on two-region thresholding segmentation, MRF segmentation, and level set segmentation.

A bridge detection method in polarimetric SAR images based on the context feature of the bridge is proposed in this article. Using the context feature that bridge crossing over water, water and land are segmented using the Markov random field (MRF) segmentation method based on the initialization segmentation of the edge energy of the volume scattering component first. The candidate bridge pixels surrounded by water are then detected by scanning the jetties in multidirection. Using the geometric feature that bridge appears as a long strip, an approximate rectangle detection criterion is proposed for the initial extraction of the regions of interest (ROIs) of bridges first. A directional entropy (DE) parameter used for the determination of the main direction and boundary of the extracted long strip region is then proposed to refine the ROIs. Using the context feature that both upper and lower ends of a bridge are large land masses and both left and right sides are large water areas, bridges are finally recognized by measuring the proportion of land pixels at both ends and water pixels at both sides. The results obtained by data of AirSAR in San Francisco of USA, RADARSAT-2 in Fujian, Boao and Lingshui of China demonstrate the effectiveness of the proposed method. Compared with the spatial-based bridge detection method, the proposed method can effectively reduce false alarms and missed detections, and the intersection over union (IoU) index is improved by at least 5%.

To address the problem of threshold segmentation of polarimetric images in coastal zones when the intensity is not bimodal under complex coastal environments, a thresholding method based on three-component decomposition and likelihood ratio is proposed in this article. First, the double-bounce and volume scattering powers are extracted by three-component decomposition. Then, a three-step thresholding diagram is carried out in the two scattering powers. The threshold base point is determined by sampling a typical window regions with the minimum or maximum average power and variance first. The threshold interval is then determined by adding or subtracting a fluctuation range to the base point. The threshold point with the maximum likelihood ratio is then searched within the interval. The final two-region or three-region segmentations of coastal images are carried out by segmenting the volume scattering power to obtain the low scattering region and segmenting the double-bounce scattering power to obtain the strong scattering region. The experimental results of RADARSAT-2 Singapore, AirSAR San Francisco, as well as TerraSAR-X Singapore data, demonstrate that the proposed method outperforms the traditional thresholding method. It effectively reduces the wrong segmentation in complex coastal scenes, showcasing an average segmentation precision exceeding 85% and an average intersection over union rate closing to 0.75. Code is available at https://github.com/polSAR-lab/polSAR-thresholding.

Using polarimetric synthetic aperture radar (PolSAR) imagery for ship detection is a critical research area in marine surveillance. Currently, the mainstream methods primarily fall into two categories: superpixel approaches and neighborhood matrix methods. These methods aim to utilize both the polarimetric and spatial information of the neighborhood pixel patch for detection. However, existing methods may not fully exploit the potential of neighborhood information. This letter formulates the ship detection problem as a binary classification task and introduces an innovative ship detection algorithm based on kernelized support tensor machine (K-STM). By employing neighborhood polarimetric tensors as the feature representation of the pixel patch, we can implicitly incorporate all polarimetric and spatial information within different dimensions of the tensor. With the help of the tensor kernel function, K-STM can effectively extract feature information embedded in the neighborhood polarimetric tensors across different dimensions. Two PolSAR datasets acquired from Radarsat-2 are used for experimental validation. The proposed K-STM method achieves the highest figure of merit (FoM) of 0.898 and 0.975 for two datasets. It demonstrates that the proposed method can achieve better performance on ship detection.

This study proposed an improved Doppler radar imaging model (IDopRIM) to address the overestimation of the original DopRIM and produce better accuracy for investigating the current-wave interaction effect on ocean surface current (OSC) retrieval. The IDopRIM’s performance, with the root-mean-square (rms) errors mostly under 0.3 m/s, is validated against the empirical model and actual measurements. It shows a notable improvement over the original model in which the maximum deviation could be up to about 1.5 m/s at moderate-to-high wind speeds and upwind direction for HH polarization. Furthermore, this study uses both numerical simulations and real synthetic aperture radar (SAR) imagery to assess the impact of current-wave interaction on OSC retrieval, especially in scenarios involving ocean internal waves (IWs), using the Doppler centroid anomaly (DCA) method incorporated with the proposed IDopRIM. The results underscore the significance of incorporating current-wave interactions in OSC retrieval for IW conditions, revealing that neglecting these interactions can result in relative errors of over 30% in certain cases.

Local climate zone (LCZ) classification plays a critical role in urban environment research and has attracted extensive attention from many researchers. However, the potential of deep learning-based approaches is not yet fully explored in this field, even though neural networks continue to push the frontier for various applications. In this paper, we propose a novel multimodal multiscale Transformer network for LCZ classification by introducing multiscale patch embedding and multimodal fusion learning in Transformer architecture. The proposed multiscale patch embedding effectively captures hierarchical interrelationships of image contextual neighborhoods, and automatically learns discriminative features. And the proposed multimodal fusion learning enables the network to naturally fuse multispectral and synthetic aperture radar (SAR) data under the guidance of attention mechanism. To further improve classification accuracy, we impose semi-supervised learning to mine unlabeled image data information. Both labeled and pseudo-labeled data jointly drive our network updates. Experiments conducted on the So2Sat LCZ42, CHN15-LCZ and SouthKorea6-LCZ benchmark datasets demonstrate that our proposed approach outperforms other existing methods significantly and achieves state-of-the-art performance. In the generated LCZ maps, urban and natural classes are well distinguished, the urban structure with waters or mountains is well preserved. Finally, we also discuss the impact of the sample receptive field and sample heterogeneity on LCZ classification performance, which provides a new idea for future studies of LCZ classification.

Target recognition plays a crucial role in the intelligent interpretation of synthetic aperture radar (SAR) images. However, polarimetric information holding great potential in target recognition has not been fully studied. In this paper, we propose a novel method for target recognition in polarimetric SAR (PolSAR) images by using convolutional neural networks (CNNs) to optimize discrete polarimetric correlation pattern. Discrete polarimetric correlation pattern transfers PolSAR images from image domain to rotation domain, and achieves a high-dimensional representation of the target. We then formulate an optimization problem, which is the basis for target recognition, to unfold the low-dimensional embeddings from the raw representations. The optimization problem aims to achieve intra-class compactness and inter-class separation of the target embeddings. Interestingly, we employ CNN as a powerful tool to solve it. By combining the rotation domain features with the neural network, we obtain a discriminative representation that reflects the target’s polarimetric scattering mechanism, and finally realize high-performance target recognition. Experiments performed on both simulated and real datasets demonstrate that the proposed method outperforms reference methods significantly in almost all metrics. It is worth mentioning that even on low-resolution images, the proposed method can still achieve high-precision recognition performance. In addition, through feature visualization, we gain deeper insights into the network behavior. Finally, feature separability issue is also discussed, further confirming that the optimized features do have the characteristics of intra-class compactness and inter-class separation.

Neural network (NN)-based methods have been the mainstream in target recognition. However, the weak interpretability of NNs harbors decision-making risks, which restrict their application in practical scenarios. In this article, we propose to interpret the NN pattern with pruning for target recognition in polarimetric synthetic aperture radar (PolSAR) images. We first generate the initial features of the target by calculating a discrete polarimetric correlation pattern in the rotation domain. In contrast to earlier approaches involving manual extraction of empirical representations, we cast initial feature refining as an optimization problem and employ trainable convolutional layers to address it. Interestingly, the weights of the learnable layers exhibit certain patterns with respect to the rotation angle. To get insights into the NN weights, we use network pruning to highlight the main components of the network weights. In this way, the key polarimetric feature elements can be distinguished, leading to a deeper understanding of the role of polarization information in target recognition. Extensive experiments on Pol-MSTAR and GOTCHA demonstrate that the proposed method not only outperforms existing reference methods in recognition metrics but also greatly provides network interpretability of polarimetric scattering. The correlations between co- and cross-polarization are quite crucial for SAR target recognition.

Due to spectrum sharing spaceborne synthetic aperture radar (SAR) often experiences signal interference emitted by ground radio systems. Interference removal methods for SAR images are important measures to address this problem. Among these methods, block subspace filtering (BSF) has the advantage of removing various types of interference signals directly in single look complex (SLC) images. However, it assumes that the observation scene does not contain strong point scatterers, otherwise, BSF will have severe performance decline in terms of losing strong point scatterer intensity and causing horizontal or vertical black lines. This paper proposes a Robust version of BSF (RBSF), which can successfully overcome the above performance decline, thereby significantly improving the robustness of the algorithm. Specifically, RBSF uses a constant false alarm rate detector to detect and mask out strong scattering pixels from the SLC image. Then, BSF reconstructs the interference components from the SLC image with strong pixels being masked out, and finally subtracts them from the original SLC image. Moreover, we find that interference will reduce, to some extent, the image contrast and entropy. Based on this finding, we design an adaptive RBSF method which selects the subspace dimension parameter adaptively by means of optimizing the image contrast and entropy. Extensive experiments demonstrate that the RBSF algorithm achieves significant performance improvement over the original BSF algorithm.

Synthetic aperture radar (SAR) systems employ active imaging modes to collect observation data. However, restrictions such as power constraints often necessitate SAR systems to operate with a limited operational duty cycle, resulting in considerable idle time per orbit. This article introduces two innovative observation modes for SAR systems, aiming to effectively utilize this idle time for localizing terrestrial emitters, such as radar and communication systems, in the azimuth-range plane of SAR observation geometry. The proposed modes leverage passive observation capability of SAR systems and work in low power during time slots not dedicated to active imaging. The implementation first involves establishing observation geometries and positioning planes for SAR systems moving along linear and circular trajectories. Next, two passive observation modes are designed, utilizing two azimuth receive beams to gather observations from distinct angles along linear and circular trajectories, respectively. Subsequently, parametric models of beam center crossing times (BCCTs) are then developed, with the emitter coordinate as model parameters. Analytical and numerical positioning methods are proposed by inversing the model parameters. Additionally, Cramer–Rao lower bounds (CRBLs) are derived for the emitter coordinate under the proposed observation modes, and it is demonstrated that the proposed estimators achieve these theoretical bounds. Finally, several simulation experiments are conducted to analyze the performance of the proposed modes and methods.

This paper studies and summarizes a feature extraction strategy from the multiscale monogenic space for target recognition in synthetic aperture radar (SAR) images. We propose applying the regional Euclidean (L2) norm to extract the features of the monogenic signal. It can obtain the maximum eigenvalue in each region and reduce information loss. We performed several groups of experiments on dimensionality reduction, they show that the regional L2 norm extraction method has the best classification results. MSTAR, OpenSARship, and FUSAR-ship datasets are the commonly used verification datasets for SAR target recognition. The experiments on these datasets have demonstrated the effectiveness of the monogenic signal extracted with regional L2 norm for SAR target recognition. In combination with K-Nearest Neighbor (KNN), sparse representation classifier (SRC), or support vector machines (SVM), the proposed method achieves great results compared with some advanced algorithms.

It is difficult to extract accurate geometric features for bridge detection in SAR images. A bridge detection method in polarimetric SAR images based on context feature of the bridge is proposed in this paper. Using the context feature that bridge crossing over water, water and land are segmented using the Markov random field segmentation method by extracting the surface, double-bounce, and volume scattering powers of the terrain first. The candidate bridge pixels are then detected by the proposed multi-direction jetty scanning method. Using the context feature that bridge appears as a long strip and both ends are large land regions, an approximate rectangle detection method is proposed to extract the regions of interest (ROIs) of bridges first. The false alarms generated by dykes and dams are then discriminated by the context features. The bridge detection results using AirSAR in San Francisco, RADARSAT-2 in Fujian and TerraSAR-X in Singapore demonstrate the effectiveness of the proposed method. The detection rate is high and the false alarm rate is low.

Multi-band polarimetric synthetic aperture radar (PolSAR) has significant advantage in information extraction. However, the demanding acquisition requirement greatly prohibits its development. Typically, compared to low-frequency band PolSAR data, high-frequency band suffers more severe data insufficiency. In this paper, the authors proposed to resolve this issue by simulating Ka-band PolSAR images from X-band images. For this purpose, a conditional Generative Adversial Network (cGAN) based X-to-Ka band PolSAR image transfer network has been proposed. Adaptations in terms of preprocessing and loss function are made to the original cGAN so that it can be better adapted to PolSAR image processing. The proposed method is verified using the X- and Ka-band dataset acquired in Hainan, China by the Aerial Remote Sensing System of the Chinese Academy of Sciences. Experimental results demonstrate the feasibility of the proposed method.

Abstract In order to improve the detection probability of weak targets, tracking radar using sum and difference beams often adopt the method of long‐time coherent integration. However, the multidimensional migration of time‐varying targets will lead to the decline of parameter estimation accuracy. To solve this problem, this article proposes a refined angle estimation method for time‐varying targets with the traditional sum and difference beam echo model, this method compensates and searches the angle parameters of the targets based on subarray rotation invariant and focus process. In addition, this article also studies the masking problem of highly dynamic proximity group targets detection, and proposes an adaptive weighted LMS‐CLEAN based on Least Mean Square criterion, which effectively reduces the influence of masking effect on the parameter estimation accuracy of weak targets. Firstly, the proposed algorithm performs angle search and phase compensation on the pulse compression echo of sum and difference channels based on subarray rotation invariant. Secondly, focus the search matrix, reconstruct the strong target echo, and stripe it from both channels by adaptive weighting. Lastly, repeat the above steps until parameters of all targets are achieved precisely. The proposed two algorithms maintain a very low computational effort while effectively reducing the parameter estimation error, and are highly promising for engineering applications. In order to verify the effectiveness of the proposed algorithm, this article also provides some numerical experiments to compares with two existing algorithms in error performance, anti‐noise performance, and computational complexity.

The radon Fourier transform (RFT)-based coherent integration is an important target detection method. However, the computational cost of parameter searching and blind-speed sidelobe (BSSL) remain the main challenges in actual RFT applications. In this letter, we propose a Whale optimization algorithm-based RFT (WOA-RFT) to accelerate the parameter searching process and improve BSSL suppression performance. First, the discrete RFT signal model is derived; WOA-RFT is then proposed to speed up the RFT and suppress BSSL. The simulation results demonstrate that our proposed method has a better performance in terms of parameter estimation accuracy, BSSL suppression capability, and computational cost, compared to some existing methods.

The dense, complex and variable electromagnetic environment poses a serious challenge to radar signal sorting (RSS) in modern electronic reconnaissance systems. In order to improve RSS performance, this letter proposes a semi-supervised learning framework-based RSS method via a residual graph convolutional network (ResGCN-RSS) to effectively improve the generalization ability of the signal sorting models in small data scenarios. Firstly, the graph structure construction of intercepted radar signals is performed via K-nearest neighbor algorithm. Then, the three-layer ResGCN is designed to adaptively improve the features learning. Finally, RSS can be effectively and efficiently implemented through an end-to-end ResGCN with small labeled graph data of interleaved radar signals. The simulation experimental results show that our proposed method can achieve better average accuracy with little computational cost increasing when the labeled data is very small, compared to some existing methods.

With the change in the spatial angle, the cross-polarization isolation (CPI) of polarimetric phased-array radar (PPAR) changes as well, destroying the estimation of the target polarization scattering matrix (PSM). To correct the bias in PPAR, this article comprehensively designs the transmitting antenna, receiving antenna, and signal waveform and proposes a bias correction method based on system optimization. First, for the transmitting antenna of PPAR, the second-order cone program (SOCP) model is proposed to optimize the weighting coefficient. With the SOCP-based beamforming method, not only beam pattern in any spatial angle can be achieved, but also arbitrary polarization state can be precisely configured. Then, for the wideband receiving signal with a certain beamwidth, an angle estimation method based on eigenvalue decomposition is proposed in this article, which can effectively cure the challenges introduced by the beamwidth and signal bandwidth. Subsequently, for the signal waveform, the phase code is designed to measure all the elements of PSM in simultaneous transmission and simultaneous reception (STSR) mode, which could eliminate the biases of the moving speed and the second-order cross-polarization error. Finally, this article compares with other methods based on differential reflectivity, and experiments show that the factors such as the spatial angle, array structure, antenna beamwidth, signal bandwidth, motion speed, and signal-to-noise ratio (SNR) have the least influence on the present method in this article.

Speckle reduction is a longstanding topic for polarimetric synthetic aperture radar (PolSAR) images. In this paper, we propose a novel end-to-end PolSAR image despeckling framework for the first time, which predicts the weight matrices of neighboring pixels instead of the target pixel itself nor the nor the noise, to achieve image despeckling. It hardly relies on any assumptions on the speckle noise distribution. Within this framework, residual in residual scaling network (RIRSN) is developed by combining the advantages of residual connections and residual scaling. To reduce network redundancy further, a dynamic version of RIRSN (DRIRSN) is also proposed by adjusting the network structure dynamically based on noise level and image content. Specifically, in DRIRSN, we introduce a lightweight network called picture2vector to estimate noise level, and a well-designed loss function to estimate image information level and measure image denoising quality simultaneously. The proposed picture2vector and loss function guide DRIRSN to focus on image areas with rich content and information, enhancing the adaptability of the network. DRIRSN inherits the properties of RIRSN for adaptively selecting and weighting the pixels of the neighborhood, and dynamically adjusts the network structure according to the estimated noise level and image content. We compare the proposed networks with reference methods on both simulated images and real images. Experimental results demonstrate that the proposed networks can effectively reduce speckle noise with low time consumption and, meanwhile, better preserve the details and the repetitive structures such as textures and edges, and the polarimetric scattering characteristics, compared with the other methods.

Data insufficiency poses a significant challenge in Ka-band Polarimetric Synthetic Aperture Radar (PolSAR) applications. Traditional PolSAR simulation approaches fail to conquer this issue due to the intricate modeling and computational complexities induced by high-frequency. In this paper, the authors propose to mitigate this issue through neural style transfer. An X2Ka translation network is proposed to transfer X-band PolSAR images to Ka-band. Leveraging the well-verified generative network Pix2Pix, the authors adapt it to accommodate the specific discrepancies between PolSAR and optical data. Experiments are conducted on X- and Ka-band PolSAR images acquired by an Airborne PolSAR system from the Chinese Academy of Sciences. Both qualitative and quantitative evaluation results demonstrate the effectiveness of the proposed network.

Signals from ground-based emitters frequently cause interference to synthetic aperture radar (SAR). A typical class of such interference signals is the transmitted pulses of ground-based radar systems due to the spectrum sharing between the Earth exploration-satellite service (active) and radiolocation in International Telecommunication Union radio regulations. In this paper, we study the localization model and method of ground-based pulse emitters using SAR as the observation platform. Specifically, we first establish a nonlinear parametric observation model of pulse time of arrival (PTOA) based on SAR observation geometry, where the model parameters include the emitter position in SAR range-azimuth plane. Then, we approximate the PTOA observation model by a second-order polynomial, and estimate the azimuth and range positions of the emitter from the polynomial coefficients. Finally, we perform numerical experiments to test the accuracy of the proposed PTOA localization method. The results show that our method can achieve meter-level azimuth accuracy and kilometer-level range accuracy. Moreover, we study the Cramér-Rao lower bound (CRLB) of the emitter location, and by comparison, we show that the root mean square error of the proposed method is only about 1.5 times coarser than that of CRLB, demonstrating that our method achieves near-optimal localization accuracy.

This paper numerically investigates the spatial ergodicity of radar scattering from randomly rough ocean surface. Based on the accurate full-wave multi-level steepest decent-sparse matrix canonical grid (MLSD-SMCG) method and Monte Carlo simulation, we simulate the L-band normalized radar scattering coefficients from one-dimensional (I-D) rough ocean surfaces with different radar illumination sizes. According to the simulation results, it is found that: For the scattering angle less than 85°, the normalized bistatic radar scattering coefficient from ocean surface with radar illumination size no less than $16\lambda$ has good spatial ergodicity. Also, the emissivities from ocean surfaces with sizes exceeding $64\lambda$ manifest spatial ergodicity if the measurement accuracy of emissivity is less than the order of magnitude of 10 −4 .

In this study, the eigenvector-based polarimetric feature extraction methods are analyzed based on the target dichotomy decomposition. The eigenvector-based scattering characterization methods, including the α / ß scattering model, Touzi's scattering model, and a recently developed scattering vector parameterization model, are applied to the single scattering matrix decomposed by the stable Huynen decomposition, i.e., Yang's decomposition. Experiments are performed on RADARSAT-2 data acquired over an oil spill area, where several scattering behaviors are presented such as backscatter from ships and the metallic target, ocean surface, oil slicks, and the multiple reflections between ships and ocean surface. Results show that the dichotomy decomposition-based scattering vector parameters are effective in characterizing the dominant scattering mechanism of targets.

Change detection is an important topic for the use of polarimetric synthetic aperture radar (PolSAR) images. In this study, we investigate the change detection method based on regions. First we segment PolSAR images with improved simple linear iterative clustering (SLIC), then take the averaged coherency matrix as the following change detection unit. The performances of three classical test statistics based on regions and pixels are compared, respectively. Three data sets collected over Langqi Island, Fuzhou, China, by RADARSAT-2 are used for evaluation. Experiments show the region-based method can stably improve the performance of change detection and reduce the false alarms than those of the pixel-based method. Further, the optimization of polarimetric contrast enhancement (OPCE) test statistic is not easily affected by spackle noise and provides stable detection results.

In this letter, we present a novel ship detection method for polarimetric synthetic aperture radar (PolSAR) images. Generalized polarization relative entropy (GPRE) is proposed to measure the differences between the target and clutter in scattering mechanism, randomness, and intensity. Since it is difficult to derive a theoretical closed-form of the GPRE, we employ the kernel density estimation to model the distribution of the GPRE in ocean regions. Then, a constant false alarm rate (CFAR) ship detection method is proposed based on the estimated distribution. Experiments performed on both synthetic and real scene images demonstrate the effectiveness of the proposed method.

This letter proposes a novel convolutional neural network (CNN) method for dual-polarized synthetic aperture radar (SAR) ship grained classification. The network employs hybrid channel feature loss that jointly utilizes the information contained in the polarized channels (VV and VH). It is demonstrated that, by adopting the proposed CNN framework and the novel loss function, the classification performance can be efficiently improved. First, instead of the prevalently used threefold or fourfold division (container ship, oil tanker, bulk carrier, and so on), the proposed method can further divide vessels into eight accurate categories. Second, this method can not only effectively classify targets into eight categories but also its accuracy in terms of fewer category classifications surpasses existing methods. Third, the method can achieve good performance on a small training data set. Experiments conducted on the OpenSARShip data sets indicate that the proposed classification method achieves state-of-the-art results.

In order to detect ships from the imagery of polarimetric synthetic aperture radar (PolSAR), a neighborhood polarimetric covariance matrix (for simplicity, we call it [$N$] hereinafter) was recently constructed. However, its calculation process is time-consuming and the backscattering heterogeneity near ship edges is also not well considered. For curing these shortcomings, we here propose two novel superpixel-based neighborhood polarimetric covariance matrices. In brief, the first matrix denoted by [SN] uses the simple linear iterative clustering (SLIC) to yield superpixels, whereas in the second matrix denoted by [GN], the gradient operator Sobel is adopted to obtain superpixels. Based on these two different kinds of superpixels, then, two different feature vectors$v_{\text {SN}}$and$v_{\text {GN}}$are separately built to compute [SN] and [GN]. Experiments performed on the real PolSAR datasets show that, compared to [$N$], [SN] and [GN] can improve the performance of the polarimetric whitening filter (PWF) more significantly and the time consumptions of calculating [SN] and [GN] are both much less.

Ship target detection using radar is an important application in military and civilian fields. For the polarization estimation of scattering waves in the underdetermined case, i.e., the number of scattering waves from ships is larger than the number of sensors, this paper proposes two estimation methods with different measurement models. 1) For the single-vector-sensor model, this paper proposes thepolarization-invariantESPRIT-based method. This method can estimate the polarization of signals containing target echo, interference, and noise, which can cure the problem that the accuracy of existing method is poor under low interference signal ratio. 2) For the multi-vector-sensor model, this paper proposes an improved ESPRIT method based on thespatial-invariant and time-invariantsimultaneously, which can increase the degree of freedom without increasing hardware cost. As for another problem of multi-vector-sensor, i.e., almost all existing methods assume that the number of scattering waves is known, this paper introducesthe polarization spectrumfor the first time, which can estimate the polarization parameters when the number of scattering waves is unknown. Finally, we analyze the two proposed ESPRIT-based methods comparing with some existing methods through Monte Carlo simulation, which results demonstrate the efficience of the proposed methods.

Ship detection via synthetic aperture radar (SAR) has been demonstrated to be very useful as polarimetric information helps discriminate between targets and sea clutter. Among the available polarimetric detectors, optimal polarimetric detection (OPD) theoretically provides the best detection performance under the assumption that the fully developed speckle hypothesis stands. This study proposes a polarimetric detection optimization filter (PDOF). The target clutter ratio (TCR) over the speckle variation was maximized using a matrix transform to derive the PDOF. The objective function based on a matrix transform instead of a vector transform is optimized to obtain synthetic effects by combining a polarimetric whitening filter (PWF) and a polarimetric matched filter (PMF). Subspace form of the PDOF (SPDOF) is also proposed, which gives performance comparable to the PDOF. Assuming a Wishart distribution, the exact and approximate expressions of the closed-form probability density function (PDF) of the PDOF are derived. The probability of false alarm (PFA) was derived in a closed-form expression, which allows obtaining the PDOF threshold analytically. Moreover, the gamma model is extended to a generalized gamma distribution ($\text{G}\Gamma \text{D}$) to adapt complicated resolutions and sea states. Experiments with simulated and real data validate the correctness and effectiveness of the results. The PDOF detector achieves the best performance in most virtual and real-world environments, especially in cases where the target statistics and clutter are not Wishart-distributed.

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.

Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing and has important applications in the detection of ships. Although many polarimetric detectors have been proposed, they are not well combined. Recently, a polarimetric detection optimization filter (PDOF) was proposed, which performs well in most environments. In this study, a novel subspace form of the PDOF [strict PDOF (SPDOF)] was further developed based on the Cauchy inequality and matrix decomposition theories, enhancing detection performance. Furthermore, a simple method to determine the optimal dimension of the subspace detector based on the trace ratio form was proposed by calculating the area under the receiver operating characteristic (ROC) curve, reaching the best detection performance among the subspaces of the detector. Moreover, to combine different subspace detectors, a modified linear discriminant analysis was proposed and developed for the diagonal loading detector (DLD) based on polarimetric subspaces. The experimental results demonstrate the superiority of these joint polarimetric subspace detectors. Most importantly, DLD solves for previous limitations due to the complex clutter background and achieves a performance comparable to that of the Wishart (Gaussian) distribution, particularly in the low target-to-clutter ratio (TCR) case.

Constant false alarm rate (CFAR) detector is a class of widely used methods for target detection in radar images. Classical CFAR detectors perform target detection on a pixel-by-pixel basis using certain sliding windows for estimating clutter statistics, which run fast for small images. However, as the image size gets large, the time cost of these detectors will increase significantly since the time complexity with respect toN×N-pixel image isO(N2). In practice, radar images, such as those in synthetic aperture radar (SAR), usually have very large numbers of pixels (which can be on the order of 10000 × 10000), making the classical CFAR detectors very time-consuming when applied to these images. In this paper, we present graphics processing unit (GPU)-oriented Designs for speeding up CFAR detectors, including smallest/greatest-of CFAR and order-statistic CFAR. The proposed designs implement CFAR detectors via tensor operations, including tensor convolution, shift, and boolean operation, which can be fast operated by GPU. Experiment results show that the proposed GPU-oriented CFAR detectors running on a high-performance Nvidia RTX 3090 GPU can be thousands of times faster than the classical CFAR detectors, and realize real-time target detection in large-size radar images. Examples using SAR and range-Doppler images are provided as illustrative applications of the proposed GPU CFAR detectors to target detection in radar images.

Radio spectrum bands allocated to spaceborne synthetic aperture radar (SAR) imagery are shared by multiple missions. In practical radio spectrum environments, these bands are also used by some ground radars, e.g., C-band weather radar. Due to this fact, radio frequency interference (RFI) may occur for a spaceborne SAR when its received signals contain the transmitted waveforms from another SAR or radar operating at the same frequency band. This particular class of RFI is usually linear-frequency-modulation (LFM) signals, which can cause bright radiometric artifacts in focused SAR images. Most existing signal processing approaches designed for addressing this problem belong to the class of preprocessing methods, which removes RFI in level-0 raw radar data before SAR focusing. In this article, we propose a postprocessing kernel—2-D SPECtral ANalysis (2-D SPECAN) filter, for removing the class of LFM RFI in level-1 SLC images. The filtering consists of three main steps: Step 1: focus LFM RFI artifacts in SLC images as point-like responses in the spectral domain via 2-D SPECAN; Step 2: perform 2-D notch filtering in the spectral domain to remove the most contribution of the RFI responses; and Step 3: transform the filtered spectrum back into the SLC image domain using the inverse operation of the 2-D SPECAN. For computation efficiency, we design a simplified processing flow and adopt a blockwise processing strategy. Experiments with several Sentinel-1 SLC images demonstrate that severe RFI artifacts in SLC images can be removed significantly by the proposed method.

Radio signals emitted by various sources, such as ground radars and broadcast/communication devices, can unintentionally cause radio frequency interference (RFI) to spaceborne synthetic aperture radar (SAR), degrading SAR image qualities to various degrees. Most existing methods tackle this problem by applying specially designed preprocessing steps to RFI-polluted level-0 SAR data before SAR focusing. However, such preprocessing is not widely used in spaceborne SAR, as there exist radiometric artifacts due to various RFI sources in the level-1 single-look complex (SLC) image products in many spaceborne SAR data, e.g., Sentinel-1 open data archives. To address this problem, in this article, we first propose a generic subspace model for characterizing a variety of RFI types, which reveals a low-dimensional structure of RFI subspace. Based on the proposed model, we next design a block subspace filter (BSF) for removing RFI artifacts in SLC SAR images directly. Experiments with ERS-2, ENVISAT/ASAR, Sentinel-1, and Gaofen-3 data are presented, and quantitative assessments based on numerical simulations are provided, which demonstrates the promising performance and application potentials of the proposed method. BSF is simple yet efficient and does not require performing preprocessing on level-0 raw data, which is helpful for users to obtain clean SAR images. MATLAB/Octave code implementation of BSF is available athttps://github.com/huizhangyang/BSF.

Superpixel segmentation approaches for polarimetric synthetic aperture radar (SAR) images have only been studied in recent years. Simple linear iterative clustering (SLIC) is a simple and efficient superpixel segmentation method, first proposed for optical images. It basically includes three implementation steps, i.e., initialization, local$k$-means clustering, and postprocessing. The challenge of applying SLIC to polarimetric SAR images lies in constructing the effective spatial and feature similarity and proposing the efficient segmentation procedure. In this study, to address both issues, we modify the SLIC clustering function to adapt the characteristics of polarimetric statistical measures. A new initialization method is proposed, which exploits the image gradient information to produce robust cluster centers. Furthermore, in an effort to give a comprehensive comparison and provide a fair assessment of the feature similarities for polarimetric SAR imagery, four classic statistical distances, among which two were not studied along with the SLIC previously, are embedded in the modified clustering function. The proposed method is validated by comparing with state-of-the-art SLIC-based algorithms and also the Ncut and TurboPixel algorithms. Experiments on extensive polarimetric SAR data sets show that the proposed method can significantly improve the segmentation results, with producing better boundary adherence and compact as well as uniform superpixels. We also obtain distinct conclusions that are different from the existing studies when investigating the performances of the statistical measures.

Pseudo quad polarimetric (quad-pol) image reconstruction from the hybrid dual-pol (or compact polarimetric (CP)) synthetic aperture radar (SAR) imagery is an important technique for radar polarimetric applications. There are three key aspects concerned in the literature for the reconstruction methods, i.e., the scattering symmetric assumption, the reconstruction model, and the solving approach of the unknowns. Since CP measurements depend on the CP mode configurations, different reconstruction procedures were designed when the transmit wave varies, which means the reconstruction procedures were not unified. In this study, we propose a unified reconstruction framework for the general CP mode based on one of our previous work. Both the iterative and least squares (LS) error solving approaches are extended. Validation is carried out on polarimetric data sets from RADARSAT-2 (C-band) to compare the performances of the reconstruction models and CP modes.

To solve the problem of dense vehicle target detection in polarimetric synthetic aperture radar (PolSAR) images from urban areas under complex scenarios, this paper proposes a target detection method that combines the superpixel segmentation and the Wishart classifier. Firstly, the buildings are detected based on the different polarimetric scattering characteristics of ground objects. Then, the morphological information of the target is obtained by the local Wishart classifier and the superpixel segmentation. After that, the center points of the target are obtained by the global Wishart classifier. Finally, the region growing procedure is used to fuse the information obtained by above-mentioned classifiers to complete the target detection task.

The wind direction asymmetries of the fully polarimetric ocean emissivity at L-band are studied using a semi-theoretical approach. The model is tuned and validated against Aquarius and SMAP observations, which incorporates the two-scale polarimetric ocean emission model, an improved directional wave spectrum, the AG13 foam emissivity model and a novel foam coverage model. Comparisons of model simulations to satellite data show that the overall accuracy of model could reach 0.2 K. The average standard deviations of the differences of polarimetric brightness temperature between observations and model are less than about 0.16 K for wind speeds up to 15 m/s. The negative and positive upwind-crosswind (UC) asymmetries of L-band ocean emission and the transition of the phase signatures in terms of wind speed are well illustrated for all polarizations by the proposed model. The directional variations of L-band brightness temperature for the third Stokes parameter show less sensitivity to wind speed with the increase of observation angles.

The effects of ocean spectrum truncation on the sea clutter distribution properties in numerical simulations are studied using a recently developed full-wave method, i.e., the multilevel steepest decent - sparse matrix canonical grid (MLSD-SMCG) method and the KHCC03 spectrum. Two types of ocean surface profiles are generated for Monte Carlo simulations based on the full and truncated spectra at the wind speed of 10 m/s. The surface profiles generated by the truncated spectrum have lengths about 1/6 of those using full spectrum. 1000 realizations are conducted for each type of profiles. The simulations are illustrated at L-band (1.4 GHz) and the incidence angle is 40°. For the simulated far-field scattering fields and normalized radar cross sections (NRCS), we use the K-distribution model to fit the probability density functions (PDF) of the amplitude and backscatter of clutters. It is found that spectrum truncation has non-negligible effects on the distribution characteristics of sea clutter in the numerical simulations, particularly for the amplitude distributions. The fitted PDF indicates that the simulated sea clutter using truncated spectrum has more small values compared with that using full spectrum.

Ship detection of polarimetric SAR (PolSAR) imagery has attracted lots of attentions in recent years. Also, it is known that among the polarimetric channels$HH, HV$, and$VV, VV$is the most sensitive to sea clutter. Following this guidance, in this paper, a novel ship detector SVVS is first proposed via subtracting the term$C_{33}$from the total power detector SPAN. And then, the complect polarimetric covariance difference matrix [$CP$] is utilized to calculate SVVS, leading to the construction of another novel ship detector$\text{SVVS}_{CP}$. Finally, we investigate the statistical distribution of sea clutter with$\text{SVVS}_{CP}$and further develop an adaptive$\text{SVVS}_{CP}$-based C-FAR detector for ship detection. The experiment carried out on one real PolSAR imagery shows that, compared to SPAN, both SVVS and$\text{SVVS}_{CP}$hold better ship detection performances.

In a recent work, a neighborhood polarimetric covariance matrix [N] was proposed to detect ships from polarimetric SAR (PolSAR) imagery. However, its computational complexity is extremely high. Besides, the heterogeneity surrounding ship edges is also not well considered in [N]. To cure these draw-backs, we construct a novel superpixels-based neighborhood polarimetric covariance matrix [SN] in this paper. Specifically, the simple linear iterative clustering (SLIC) is first used to obtain superpixels. Then, the vector vmeancorresponding to the mean value of superpixel is further computed so as to characterize the neighborhood information of each pixel in superpixel. Finally, by combining the original scattering vector v and vmeantogether, the vector t12is built to calculate [SN]. The experiment tested on one L-Band ALOS PolSAR imagery shows that i) the polarimetric whitening filter derived from [SN] (i.e., PWFSN) has a better detection performance than that derived from [N] (i.e., PWFN); ii) the calculation process of [SN] takes much less time than that of [N].

In this letter, we first investigate the relationship between polarimetric covariance matrix [C] and complete polarimetric covariance difference matrix [CP], and then construct a scattering difference parameter SDP. Subsequently, a hybrid polarimetric covariance matrix [HC] is developed based on SDP for curing the disadvantage of [CP], that is the scattering difference information of small ships cannot be well contained in [CP]. By fusing the feature “1-SDP” and the power detector SPANHCderived from [HC] together, a novel ship detection method SPANSDPis finally proposed to detect ships. Experiments performed on the airborne SAR (AIRSAR) L-Band and GF-3 C-Band data verify that 1) SPANSDPcan detect small ships more accurately than other state-of-the-art methods and 2) [HC] is more effective in improving ship detectors' detection performances in comparison with [CP].

The compact polarimetric (CP) imaging mode is a special, dual-polarization mode in which only one polarization is transmitted and two orthogonal polarizations are simultaneously used to measure the returns. The transmitting polarization can be an arbitrary elliptical wave, and therefore, theoretically, there are numerous possibilities of hybrid dual-pol modes. Since the scattering process is a function of the incident field and scattered field, when we use polarization to measure the backscattering process, radar measurement is polarization-dependent. This leads to difficulties in developing a unified interpretation for target scattering characterization in compact polarimetry, since the scattering models should be modified to take the fact of polarization dependency into account. In this letter, in the framework of a previously proposed CP formalism method, we develop a general model-based target decomposition technique that is applicable to the general CP mode. First, a general link between the fully polarimetric (FP) and the general CP scattering matrix elements is established. Second, the FP models corresponding to a single symmetric scattering mechanism and azimuthally symmetric volume scattering are mapped to the general CP case. Assuming that the 2-D backscatter is composed of a single scattering component and a volume scattering component, the decomposition is finally implemented by solving a quadratic equation. The C-band Shuttle Imaging Radar with Payload C/ X-synthetic aperture radar (SIR C/X-SAR) data are used in the experiment for demonstration.

The effects of sea surface temperature (SST) on ocean radar backscattering are investigated under both the neutral and nonneutral atmospheric conditions for the applications of wind retrieval. The impact factors are parameterized as functions of SST. The SST effects on the variations in ocean scattering and wind retrieval are evaluated using an analytic model which combines the KHCC03 spectrum and the second-order small slope approximation (SSA-II) model. Under the neutral condition, we present the following new insights at three commonly used bands: 1) the seawater permittivity accounts for a dominant effect of SST at the L-band. The SST effects induce a wind underestimation of 0.3 m/s over cold seawater and a wind overestimation of 0.24 m/s over warm seawater at the L-band and a wind speed of 8 m/s. The seawater viscosity plays a significant role in the SST effects on ocean scattering at the C- and Ku-bands, while its variation induced by SST has insignificant effects on L-band scattering; 2) for the C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds; and 3) both the dielectric and dynamic factors play significant roles in the SST effects on ocean scattering at the Ku-band. Under the nonneutral condition, the simulation results show that the air–sea interaction governs the SST effects on ocean scattering and wind velocity variations. Other than the air–sea interaction, the wind retrieval errors induced by other SST-related factors are negligible at the L- and C-bands.

Electromagnetic scattering and emission from 1-D rough surfaces with multiple elevations are studied using full-wave simulations. Both the root-mean-square (rms) heights and the surface length are large compared to the wavelength. A novel multilevel steepest decent-sparse matrix canonical grid (MLSD-SMCG) method is proposed to address limitations in the original SMCG. The uniform Nystrom method and neighborhood impedance boundary condition (NIBC) are also incorporated in solving the dual surface integral equations (SIEs) of the method of moments (MoM). Simulation results are illustrated at L-band for soil and ocean surfaces. The surface rms heights and lengths are up to 1.43 and 243.8 m corresponding to 6 and 1024 wavelengths at 1.26 GHz, respectively. For ocean surfaces, the wind speeds up to 20 m/s are considered, and the entire spectrum is included to capture all relevant surface length scales. Numerical results indicate the proposed approach is computationally efficient and accurate. Energy conservation checks in simulations are at $10^{-4}$ for ocean scattering and emission. Also, the effects of wind-driven roughness on ocean emissivity are further investigated using the proposed approach in terms of wind speed and observation angle for both polarizations.

The detection of small ships in polarimetric synthetic aperture radar (PolSAR) images is still a topic for further investigation. Recently, patch detection techniques, such as superpixel-level detection, have stimulated wide interest because they can use the information contained in similarities among neighboring pixels. In this article, we propose a novel neighborhood polarimetric covariance matrix (NPCM) to detect the small ships in PolSAR images, leading to a significant improvement in the separability between ship targets and sea clutter. The NPCM utilizes the spatial correlation between neighborhood pixels and maps the representation for a given pixel into a high-dimensional covariance matrix by embedding spatial and polarization information. Using the NPCM formalism, we apply a standard whitening filter, similar to the polarimetric whitening filter (PWF). We show how the inclusion of neighborhood information improves the performance compared with the traditional polarimetric covariance matrix. However, this is at the expense of a higher computation cost. The theory is validated via the simulated and measured data under different sea states and using different radar platforms.

We employ the second-order small slope approximation (SSA-II) and the method of moment (MoM) to investigate the roughness scale effect on ocean radar scattering in both three- (3-D) and two-dimensional (2-D) cases. Various roughness scales of the ocean surface are represented by truncating the Kudryavtsev wave spectrum. Criteria of full spectrum truncation are proposed for the numerical simulations of ocean scattering. Numerical results are illustrated in fully bistatic configuration at L- and C- bands. It is found that short waves with wavenumber larger than 316 rad/m have little effects on ocean scattering. The large-scale waves put more effects on scattering in the forward directions, particularly for large incidence angles. For numerical simulations of ocean scattering with incidence angle less than 60°, using small surface profiles with size about 1/6 of those accounting for full spectrum yields result with errors less than 2dB.

Accurate calculations of electromagnetic scattering and emission from ocean surfaces with large sizes and root-mean-square (RMS) heights are still a challenge for the fullwave numerical methods. The widely used sparse matrix canonical grid (SMCG) method is only efficient for small and moderate roughness surfaces. This is because of the limitation of approximations in the far-field expressions using Taylor series expansion. In this paper, a novel multilevel steepest decent-sparse matrix canonical grid (MLSD-SMCG) method is proposed to address this issue. The proposed approach is implemented with the method of moment (MoM) in solving the dual surface integral equations (SIEs). Simulation results are illustrated at L-band for ocean surfaces with wind speed up to 20 m/s. The entire spectra are involved to capture all scales of waves in simulations. Thus the surface root-mean-square (RMS) heights and lengths are up to 3.82 and 1024 wavelengths of 1.26 GHz, respectively. Numerical results indicate the proposed approach is computationally efficient and accurate. Energies in simulations are conserved to the order of 10-4for ocean scattering and emission at various wind speeds.

The effects of sea surface temperature (SST) on ocean radar backscattering and wind retrieval are investigated using the second-order small slope approximation (SSA-II) model. Impact factors are parameterized and built into an SST-enhanced KHCC03 spectrum. By employing the Monin-Obukhov similarity theory (MOST), the air-sea interaction is considered in the analyses. Under the neutral condition, the SST effects on wind retrieval cannot be neglected at L-band and the seawater permittivity and air density are the dominant factors. For C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds. At Ku-band, the SST-related factors besides the air density and seawater viscosity can also significantly affect the ocean backscattering.

Classification is an important topic in synthetic aperture radar (SAR) image processing and interpretation. Markov Random Field (MRF) has been widely used for capturing the spatial-contextual information of the image. In this study, we first introduce two ways to construct the Wishart mixture model, and combine these models with MRF. Then, we use these models to implement pixel-based classification using a real PolSAR image. Finally, we analyze the experimental results and evaluate the robustness and applicability of the model mixture strategies.

Compact polarimetric interferometric synthetic aperture radar (CPolInSAR) has been widely used due to its low complexity and costs. But in the field of land cover classification, there are very few studies on CPolInSAR images. In this paper, a novel classification method is proposed to investigate the performance of CPolInSAR images for land cover classification. Specifically, the m - δ decomposition and the optimal polarimetric coherence coefficient are first employed to extract the features of CPolInSAR images, and then support vector machine (SVM) is utilized for classification. The experimental results show that 1) the optimal polarimetric coherence coefficient can be used to achieve higher accuracy especially in wood land and built-up areas; 2) CPolInSAR has a greater potential for land cover classification than PolSAR and compact PolSAR.

The scattering process is a function of the incident field and the scattered field. When we use polarization to measure the backscattered process, radar measurement is polarization dependent. This dependency causes difficulties in developing unified interpretations for scattering characterization in compact polarimetry because the compact polarimetric (CP) system could be with different transmit polarizations. In this study, in the framework of a previously proposed CP formalism method, we develop a general model-based target decomposition technique applicable to the general CP mode related to an arbitrary transmit wave polarization. Firstly, a general link between the fully polarimetric (FP) and CP scattering matrix elements is established. Secondly, the FP symmetric scattering model and the volume scattering model are mapped into the general CP case. By assuming that the 2-dimentional backscatter is composed of a single scattering mechanism and a volume scattering component, finally the decomposition can be implemented by solving a quadratic equation. We apply this method for oil spill detection and discrimination. C-band SIR-C/X-SAR data are used for validation to show the effectiveness of the general CP mode for ocean surface characterization.

In this paper, a new scattering difference parameter named as SDP is first constructed to characterize the relationship between polarimetric covariance matrix [C] and complete polarimetric covariance difference matrix [CP]. Then, by integrating ”1-SDP” and the power maximization synthesis detector (PMS) derived from [CP], a novel ship detection method OmSPcpis further developed. In order to demonstrate the performance of the proposed method, one AIRSAR L-Band Polarimetric SAR dataset with 22 ships is exploited. The experimental results show that, compared to other methods, OmSPcpcan hold a better ship detection accuracy.

In this letter, we propose a novel ship detection method in high-resolution synthetic aperture radar (SAR) images. In the proposed method, the superpixel is utilized as the basic processing cell. We propose a superpixel-level Fisher vector to characterize the difference between the target and clutter superpixel due to the strong discriminating ability of the Fisher vector. Besides feature extraction, the threshold determination is another key step in the detection scheme. We determine the threshold in a semisupervised way, which combines the advantages of both supervised and unsupervised methods. Based on the threshold, the target superpixels are discriminated and we obtain the final detection result. Compared with existing methods, the proposed method shows robust performance under low signal-to-clutter ratio (SCR) conditions. Experiments performed on both synthetic data and a TerraSAR-X image demonstrate the effectiveness of the proposed method.

The roughness scale effects on ocean radar scattering are studied using the second-order small slope approximation (SSA-II) and the method of moments (MoM). The KHCC03 spectrum is employed to represent 2-D and 1-D sea surfaces in the above two scattering methods, respectively. Criteria of full spectrum truncation are proposed for the numerical simulations of ocean scattering. Numerical results are illustrated in fully bistatic configuration at L- and C-bands. It is found that scattering at higher frequency is relatively more sensitive to the small-scale roughness but less sensitive to the large-scale roughness. At L- and C-bands, short waves with wavenumber larger than 316 rad/m have little effect on ocean scattering. The large-scale waves put more impacts on scattering in the forward directions, especially for large incidence angles. Other than the specular direction, the effects of large-scale roughness on ocean scattering are in general smaller at VV-pol than HH-pol. The bistatic scattering at cross polarizations is less sensitive to the roughness scale as compared to the copolarizations. For numerical simulations of ocean scattering with incidence angle less than 60°, using small surface profiles with size about 1/6 of those accounting for full spectrum yields results with errors less than 2 dB. Results also indicate that the incoherent parts dominate the scattered power from ocean surfaces with large-scale roughness.

Compared with optical images, polarimetric synthetic aperture radar (PolSAR) images usually maintain lower resolution. Ship targets in PolSAR images have fewer pixels than those in optical images. Therefore, architectures such as Faster R-CNN and its variations that focus more on target pixels may fail for PolSAR images. Due to limited data and the simple geometric structures of small ship targets, the R-CNN framework with a large neural network as its backbone easily overfits the training set. In this article, we propose a novel lightweight patch-to-pixel convolutional neural network (P2P-CNN) for ship detection via PolSAR images. P2P-CNN focuses on both the target and its surroundings. A patch of proper size that contains both a target and its surroundings is used as the input of the neural network to determine whether the pixel in the center of the patch belongs to the target. To utilize contextual semantic information at all scales, all feature maps from the top down are combined to improve the final result. Instead of conventional convolution, dilated convolutions are used in the proposed neural network to exponentially expand receptive fields without adding any model parameters. The proposed approach and comparative methods are tested and compared on PolSAR images in various environments under varying image resolution, target size, sea condition, sensor type, etc. The experimental results demonstrate that the proposed approach outperforms all the compared methods.

Constant false alarm rate (CFAR) algorithms using a local training window are widely used for ship detection with synthetic aperture radar (SAR) imagery. However, when the density of the targets is high, such as in busy shipping lines and crowded harbors, the background statistics may be contaminated by the presence of nearby targets in the training window. Recently, a robust CFAR detector based on truncated statistics (TS) was proposed. However, the truncation of data in the format of polarimetric covariance matrices is much more complicated with respect to the truncation of intensity (single polarization) data. In this article, a polarimetric whitening filter TS CFAR (PWF-TS-CFAR) is proposed to estimate the background parameters accurately in the contaminated sea clutter for PolSAR imagery. The CFAR detector uses a polarimetric whitening filter (PWF) to turn the multidimensional problem to a 1-D case. It uses truncation to exclude possible statistically interfering outliers and uses TS to model the remaining background samples. The algorithm does not require prior knowledge of the interfering targets, and it is performed iteratively and adaptively to derive better estimates of the polarimetric covariance matrix (although this is computationally expensive). The PWF-TS-CFAR detector provides accurate background clutter modeling, a stable false alarm property, and improves the detection performance in high-target-density situations. RadarSat2 data are used to verify our derivations, and the results are in line with the theory.

Polarimetric whitening filter (PWF) can be used to filter polarimetric synthetic aperture radar (PolSAR) images to improve the contrast between ships and sea clutter background. For this reason, the output of the filter can be used to detect ships. This paper deals with the setting of the threshold over PolSAR images filtered by the PWF. Two parameter-constant false alarm rate (2P-CFAR) is a common detection method used on whitened polarimetric images. It assumes that the probability density function (PDF) of the filtered image intensity is characterized by a log-normal distribution. However, this assumption does not always hold. In this paper, we propose a systemic analytical framework for CFAR algorithms based on PWF or multi-look PWF (MPWF). The framework covers the entire log-cumulants space in terms of the textural distributions in the product model, including the constant, gamma, inverse gamma, Fisher, beta, inverse beta, and generalized gamma distributions ($\text{G}\Gamma $Ds). We derive the analytical forms of the PDF for each of the textural distributions and the probability of false alarm (PFA). Finally, the threshold is derived by fixing the false alarm rate (FAR). Experimental results using both the simulated and real data demonstrate that the derived expressions and CFAR algorithms are valid and robust.

In this article, we investigate the scattering components of ships and find that the surface scattering may be the primary scattering for some ships, especially small ships. Meanwhile, the drawbacks of the complete polarimetric covariance difference matrix [CP] are also pointed out in theory. Based on these analyses, two new methods are then constructed to detect the ships. More specifically, the first one RsP is constructed by directly combining the similarity parameter of surface scattering Rs and the power-maximization synthesis (PMS) detector. The second one RsDVH is designed by taking advantage of four different features (i.e., Rs, double-bounce scattering, volume scattering, and helix scattering), which are all derived from the joint polarimetric information that is developed by combing the information of the polarimetric covariance matrix [C] and [CP]. Subsequently, the generalized Gamma distribution (GΓD) is found suitable for characterizing the RsDVH values of the sea clutter. At last, an adaptive constant false-alarm-rate (CFAR) detector developed from RsDVH is proposed for ship detection. To verify the effectiveness of RsP and RsDVH, four polarization synthetic aperture radar (PolSAR) imageries are tested, including one L-band UAVSAR imagery with 19 ships, two L-band AIRSAR imageries with 22 and 53 ships, respectively, and one C-band GF-3 imagery with ten ships. The experimental results show that: 1) the surface scattering is beneficial to detecting ships, especially the ships with prominent surface scatterings; 2) compared with other state-of-the-art methods, RsDVH can more effectively enhance the target-to-clutter ratio (TCR) values of small ships in the case of rough sea surface; and 3) the joint polarimetric information that is put forward and exploited for the first time in this article has a greater potential to help ship detectors improve their detection performances than the traditional polarimetric information included in [C].

In this paper, we propose a novel ship detection method for polarimetric synthetic aperture radar (PolSAR) images via graph-based sparse manifold ranking. The main framework comprises a coarse-to-fine scheme. We employ the image intensity for prescreening and introduce graph-based sparse manifold ranking (GSMR) for discrimination. The distance between the ship and clutter in sparse code domain is explored. And the image elements in candidate region are ranked based on the prescreening priors and the new distance in graph labeling framework. The final detection result is produced with the ranking procedure. Experiments performed on two RADARSAT-2 images demonstrate the effectiveness and superiority of the proposed method.

Ship detection is one important task in radar remote sensing. Moreover, Polarimetry shows a valuable contribution to discriminate between targets and clutter. The performance of most polarimetric detectors depends on two important factors: target clutter ratio (TCR) and speckles (or standard deviation to mean ratio of clutter background). The polarimetric matched filter (PMF) is just to maximize the TCR, while the polarimetric whitening filter (PWF) only takes the speckle reduction into consideration. In this paper, the optimal polarimetric detection filter (OPDF) is put forward, which considers maximizing the ratio of TCR to speckle. The approximate expression of the probability density function (PDF) of the OPDF is derived in closed form, so are the probability of false alarm (PFA) and the probability of detection (PD) in Wishart distribution assumption. The threshold of the OPDF detection can be easily obtained in closed form or via the bisection method. Experiments via simulated data validate the correctness of our results. The OPDF detector gives the best performance in most environments, especially in low PFA case and in the case where the statistics of targets is not the ideal Wishart distribution.

Compact polarimetry (CP) is a dual-polarization hybrid imaging mode, which only provides partially polarized backscatter characteristics of scatterers. The compact polarimetric synthetic aperture radar (SAR) measures different fully polarimetric scattering elements when the illumination wave parameter varies. The backscattered wave is fully characterized by two channel elements, in which the channel ratio is important in identifying physical scattering mechanisms. In this study, we propose a new formalism method for general compact polarimetric data, such that the compact measurements are presented according to a same standard for describing target physical properties. Then based on this formalism, a channel ratio-based scattering characterization method is proposed, which is mathematically equal to the fully polarimetric ΔαB/αBmethod but without any scattering assumption. The formalism provides a natural way to characterize the target scattering behaviors. Experiments show that based on this formalism, the proposed method is convenient and efficient for compact target scattering interpretation.

Sea surface and ship have different backscattering mechanisms, in which surface scattering is predominant for sea surface in the low sea state case. Based on this fact, many ship detectors have been developed by suppressing the surface scattering resulted from sea surface. Actually, small ship may also have strong surface scattering sometimes. In such a case, the methods of avoiding using surface scattering features may easily miss the detection of small ships. To verify this point, in this paper, we first analyze the shortcomings of An's method which is based on surface scattering similarity and the power maximization synthesis detector (PMS), and then improve it for detecting small ships more effectively. In order to demonstrate the performance of the proposed method, AIRSAR L-Band Polarimetric SAR dataset is exploited. Comparing to other methods, the new method shows a better ship detection performance.

This paper proposes a phase-derived velocity measurement (PDVM) method for high-speed targets based on wideband direct sampling linear frequency modulated radar. First, a high-speed target echo model considering intrapulse Doppler modulation is developed. Then, a PDVM model considering acceleration is established. The key to realizing PDVM is resolving phase ambiguity. Under low signal-to-noise ratio (SNR) conditions, a joint processing method combining acceleration information and multiframe data to solve phase ambiguity is proposed, which can significantly reduce the SNR requirement for PDVM. In this paper, the small-amplitude micromotion measurement capability of the proposed method is verified by simulation. Moreover, the measured data of a Ku-band ground-based radar are used to verify the applicability of the PDVM method under low SNR conditions and its feasibility to be applied to complex multi-scattering point targets. Both the simulation and experimental results show that the proposed method is suitable for high-speed targets with radial motion, including acceleration and jerk, and that the PDVM precision can reach the order of magnitude of centimeters per second or millimeters per second.

Rotational parameter estimation is fundamental to the focusing of imaging algorithm and inverse synthetic aperture radar (ISAR) image size calibration. Previous research has indicated that scattering points (SPs) in different imaging time windows locate at various associated positions in range-Doppler (RD) planes. This paper exploits the relation to estimate the rotation parameters (RPs) and translation error parameters for medium processing time length and nonuniform rotation situation. The iterative closet point (ICP) method inspires an alternating iterative algorithm consisting of the SP assignment and parameter optimization. In the parameter optimization step, multiple SP set pairs are utilized to construct the objective function that represents the degree of mismatch between point set pairs. A compatible fast SP extracting method is also presented to reduce the computational complexity. The experimental results show that the proposed algorithm can provide fast, accurate, and robust estimation for parameters in nonuniform rotation case.

Compact polarimetry is a dual-polarization hybrid imaging mode and provides partially backscatter characteristics of scatterers. The compact polarimetric (CP) synthetic aperture radar (SAR) measures the combinations of fully polarimetric (FP) scattering coefficients, which is dependent on the transmit-wave polarization state. The backscattered wave is fully described by a 2-D complex scattering vector, in which the dual-channel polarization ratio represents the vector nature for describing target polarimetric properties. However, due to the dependence of CP measurements on the transmit wave, the complex channel ratio is explained differently for the same scatterers under different observation modes, e.g., the channel ratio ρ = ± j under the left circular mode and ρ = ±1 under the linear π/4 mode for the canonical trihedral and dihedral scatterers. The explanation diversity is inconvenient for the use of CP data, resulting in nonunified target decomposition algorithms. In this study, first, we propose a new formalism method for the general CP SAR mode, such that all CP mode observables are described based on the same standard for target scattering characterization. This provides potential for developing unified CP decomposition algorithms. Then, based on this formalism, a polarization ratio-based scattering characterization method is proposed, which is mathematically equal to the FP ΛαB/αBmethod but without any scattering assumption. Theoretical analyses and discussions are provided. Experiments show that the proposed method is efficient for compact polarimetric scattering interpretation.

Superpixel segmentation has been commonly used in various image processing tasks such as object detection and image classification. In this paper, we propose a novel superpixel segmentation method based on a new distance function and superpixel seed updating strategy for polarimetric synthetic aperture radar (PolSAR) images. We initialize superpixel seeds with an expected number of superpixels. Then, we iteratively cluster the pixels based on the distance function and update the superpixel seeds based on the updating strategy. When the termination condition is reaches, we stop the iteration and obtain the superpixels. The experimental results based on RADARSAT-2 data demonstrate that our method is effective and achieves a better tradeoff between boundary adherence and compactness.

On 10 August 2016 China launched the GF-3, its first C-band polarimetric synthetic aperture radar (SAR) satellite, which was put into operation at the end of January, 2017. The GF-3 polarimetric SAR has many advantages such as high resolution and multi-polarization imaging capabilities. Polarimetric SAR can fully characterize the backscatter property of targets, and thus it is of great interest to explore the physical scattering mechanisms of terrain types, which is very important in interpreting polarimetric SAR imagery and for its further usages in Earth observation. Both Radarsat-2 and Gaofen-3 satellites operate in C-band and are designed for similar missions. In this study, we compare the performances of fully polarimetric Gaofen-3 and Radarsat-2 data in representation of terrain types. By using several classic polarimetric features and the iterative Wishart-distribution based classifier, Gaofen-3 and Radarsat-2 data are compared in the aspects of polarimetric property preservation and capability of terrain classification.

We presented two target decomposition methods for polarimetric interferometric SAR (PolInSAR) images parallel to the eigen-decomposition and scattering power decomposition for polarimetric SAR (PolSAR) images. For both cases, we showed the importance of PolInSAR coherence estimation, and in particular, the newly proposed best normal matrix approximation algorithm in successful decomposition of PolInSAR data. Preliminary results were demonstrated using the ESAR data.

The optimal receiving technique of target backscattered signal in radar polarimetry has been successfully applied in many applications. In this study, we further develop the change detector which was proposed based on the optimization of polarimetric contrast of radar received powers. The change detector used for multi-temporal polarimetric synthetic aperture radar (SAR) analysis was proposed with the purpose to seek a pair of optimal polarization states such that fluctuation between the observables collected at different times can be minimized. In this study, the change detector is simplified. Simulated polarimetric data is used to verify the simplification. Real polarimetric SAR data sets are used for demonstration and the performance is compared with the likelihood ratio test (LRT) method implemented in PolSARpro.

A new speckle reduction algorithm is presented in this paper to improve the visualization of synthetic aperture radar (SAR) images. The algorithm contains two steps. First, we propose a speckle reduction filter. This filter use different smoothing effect according to scene of sub-window. So it can maintain the details while noise suppression. Second, we propose a method to enhance the details. We select a neighborhood of pixel and divide it into homogeneous and heterogeneous. Then we use different reconstruction strategy to enhance the texture. After these steps, the speckle noise of SAR images has been decreased and the texture details have been enhanced at the same time. Experiments validate the algorithm with good performance.

The application of the spaceborne stripmap range sweep synthetic aperture radar (SS-RSSAR) is limited by its fixed azimuth beam pointing. In this paper a more advanced spaceborne azimuth-range sweep synthetic aperture radar (ARSSAR) is proposed to illuminate the region of interest (ROI) by simultaneously sweeping the beam in range and in azimuth. In this case, more balances of the azimuth resolution and the azimuth swath can be flexibly realized. The basic data acquisition problem for the spaceborne ARSSAR is analyzed from two aspects: firstly, an optimized beam illumination strategy is achieved by minimizing the width of the echoes from the ROI-strip; secondly, a continuous varying pulse interval (CVPI) method is suggested to avoid the transmission blockage and maximize the allowable width of the ROI-strip. The presented approach is evaluated by the simulations.

Among high-squint high-resolution (HSHR) synthetic aperture radar imaging algorithms, nonlinear chirp scaling algorithm (NLCSA) and its extensions, such as extended NLCSA (ENLCSA), have a common drawback in that they all neglect the spatial variations of linear range migration (LRM) and Doppler centroid, and thus, only targets in a specific central slant range plane can be strictly focused. In this letter, we show that by using a new NLCSA, targets can be focused in the ground plane under HSHR conditions. Based on a more accurate 2-D spectrum, the new NLCSA outperforms the ENLCSA by introducing a new range–Doppler domain interpolation to correct residual range migration and a new perturbation function to remove the dependence of Doppler phase on azimuth. The coefficients of the new perturbation function are numerically calculated and then smoothed by polynomial fitting. Though the outputs of the numerical calculation are somewhat unstable at the current stage, it has been demonstrated to perform better than the algorithms neglecting the spatial variations of LRM and Doppler centroid, such as the ENLCSA, by point target simulations.

This letter theoretically extends and applies the overlapped subaperture algorithm (OSA) for the challenging spotlight synthetic aperture radar (SAR) imaging in a quasi-forward-looking (QFL) geometry. A parameter-adjusting framework, in which the radar parameters relate closely to the geometry, is employed to mitigate the spatial signal coupling. By innovatively fitting the phase error induced by the planar wavefront assumption directly to the wavenumbers, both the linear and quadratic phase errors, which determine the geometrical distortion and the defocusing effects, respectively, can be accurately expressed and then corrected. When compared with the parameter-adjusting polar format algorithm proposed by the authors in previous work, the OSA is superior in contributing to an expanded imaging swath. The validity of the OSA methodology is demonstrated by the simulation of a Ka-band SAR working in the QFL diving mode with an extreme 85° squint angle.

This paper proposes a coastline detection method for polarimetric synthetic aperture radar (SAR) images based on region-based and edge-based active contour models. It can be used to detect coastline accurately and fast. In this method, the region-based and edge-based active contour models are effectively combined by an important property of the likelihood ratio edge detector in polarimetric SAR images, which is proved by theory. Using low-resolution image obtained by multilook processing, we detect accurate and continued coarse coastlines by a region-based level set method. The property of the likelihood ratio edge detector of polarimetric SAR images along the coastline region is then analyzed. The coarse detection result is finally refined using a fast snake active contour model based on the edge property. Polarimetric SAR data acquired by RADARSAT-2 over a Singapore region and TerraSAR-X over a Berkeley region are both used to test the proposed algorithm. The experimental results show that the coastline is fast and accurately detected in different initial scales. The proposed method greatly reduces the data processing time compared with the coastline detection method based on a single scale.

The spaceborne stripmap range sweep synthetic aperture radar (SS-RSSAR) is a new concept spaceborne SAR system that images the region of interest (ROI) with ROI-orientated strips, which, unlike the traditional spaceborne SAR, are allowed to be not parallel with the satellite orbit. The SS-RSSAR imaging is a challenging problem because echoes of a wide region have strong spatial varieties, especially in high-squint geometries, and are hard to be focused by a single swath. The traditional imaging algorithms could solve this problem by cost-ineffectively dividing an ROI into many subswaths for separate processing. In this paper, a new wide nonlinear chirp scaling (W-NLCS) algorithm is proposed to efficiently image the SS-RSSAR data in a single swath. Comparing with the traditional nonlinear chirp scaling algorithm, the W-NLCS algorithm is superior in three major aspects: the nonlinear bulk range migration compensation (RMC), the interpolation-based residual RMC, and the modified azimuth frequency perturbation. Specifically, the interpolation for the residual RMC, the most significant step in achieving the wide-swath imaging performance, is made innovatively in the time domain. The derivation of the W-NLCS algorithm, as well as the performance analyses of the W-NLCS algorithm in aspects of the azimuth resolution, accuracy, and complexity, are all provided. The presented approach is evaluated by the point target simulations.

In this paper, a method is proposed for ship detection in poalrimetric SAR imagery. Polarimetric SAR image is decomposed into a sparse ship component and a sea clutter component. Then hierarchical priors of the latent variables associated with the components are introduced, and variational Bayesian inference is used to estimate the expectations of the latent variables. The proposed method is an automatic iterative process without any sliding window. Moreover, polarimetric SAR images are directly used, and the possible information loss in scalar polarimetric feature representation can be avoided. Using RADARSAT-2 data, we demonstrate the good performance of the proposed method.

Change detection is an important issue in many applications. In this study, we propose a new change detector for multi-temporal polarimetric synthetic aperture radar (SAR) images. The new detector is based on the optimization of polarimetric contrast minimization. The optimized solution of the contrast model is also given. Experiments are performed on two RADARSAT-2 data sets and results show agreement with land changes.

In this letter, we propose the extended polarimetric whitening filter (EPWF) for target detection in polarimetric synthetic aperture radar (PolSAR) images. We adopt finite mixture models fitted by the expectation-maximization algorithm for modeling PolSAR clutter. Specifically for single-look and multilook PolSAR data, the Gaussian mixture model (GMM) and Wishart mixture model (WMM) are applied, respectively. At the core of the EPWF, we present the closed-form solution to the optimal weighting matrix that minimizes the speckle of filtered clutter. We also derive the null hypothesis distribution of the EPWF detector to determine the detection threshold given a false alarm rate. The effectiveness of the EPWF is evaluated on both simulated and real PolSAR data. Experimental results demonstrate that the GMM and WMM are suitable for PolSAR clutter with channel-dependent texture. Compared with alternative detection statistics, the speckle level of the EPWF is lower in terms of the standard-deviation-to-mean ratio, which leads to a preferable target detection performance of the EPWF detector.

This letter presents a multiscale edge detection method for multilook polarimetric synthetic aperture radar (PolSAR) images based on the nonsubsampled contourlet transform (NSCT). The NSCT can provide flexible multiscale and directional decomposition. In the multiscale decomposition, the coefficients of the nonsubsampled pyramid in the NSCT are calculated via maximizing the polarimetric contrast between the adjacent subband levels, instead of using the difference of the adjacent subbands as used in the additive noise model. By this way, we make the NSCT applicable to PolSAR data and multiband data. Then, the edges are detected in the NSCT domain based on a fusion of the directional subband coefficients at different scales. Experimental results with both simulated and real PolSAR data show that the present approach is robust to noise and the extracted edges are complete and continuous.

In this letter, a constant false-alarm rate line detector for polarimetric synthetic aperture radar (Pol-SAR) images is presented based on Wilks' test statistic, which can be used to test the equality of two covariance matrices following the complex Wishart distribution. Due to the two-tailed nature, Wilks' test statistic can detect both bright and dark features. The probability distribution of the proposed detector is derived and verified using simulated Pol-SAR images. Experimental results on both simulated and measured Pol-SAR data show the effectiveness of the proposed detector and its superiority over the traditional detector based on the Wishart likelihood-ratio test statistic.

In this letter, we employ the sparse Bayesian multitask learning to realize joint sparsity-enforcing polarimetric inverse scattering. The prior assumption about the data model is redesigned to avoid information sharing across unrelated tasks. Based on this assumption, we provide the formulas for Bayesian inference as well as the algorithm flowchart, which still has the linear complexity. Experimental results demonstrate that polarimetric inverse scattering with the proposed method can effectively extract the characteristics of canonical scatterers.

This letter proposes a harbor detection method by extracting specific paralleled contours of harbors in polarimetric synthetic aperture radar (SAR) images. First, land and sea are partitioned by the region-based level set segmentation algorithm which is applied to the extraction of the volume scattering component. Then, parallel curves are modeled by a series of parallel line segments. They are detected by finding out and connecting paralleled line segments given certain criteria. The detected parallel curves are merged according to a distance measurement so that the harbor contour is finally detected. RADARSAT-2 polarimetric SAR data covering an area in Singapore are used to test the harbor detection method. Experimental results show that the method improves sea-land segmentation accuracy and extracts all parallel curves along the coast correctly. The method enables an accurate detection of harbors.

In this letter, we propose a novel ship detection method in synthetic aperture radar (SAR) imagery via variational Bayesian inference. First, we establish the ship detection probabilistic model which decomposes the SAR image as the sum of a sparse component associated with ships and a sea clutter component. Then, we introduce hierarchical priors of the latent variables in the model and use variational Bayesian inference to estimate the posterior distributions of the latent variables. The proposed method is an automatic iterative process without any sliding window. Experimental results accomplished over synthetic data and a RADARSAT-2 SAR image demonstrate that the proposed method can achieve state-of-the-art ship detection performance.

In this paper, we propose an improved attributed scattering model to mathematically unify the scattering models of several canonical primitives. These primitives include not only point- and line-segment-scatterers, such as trihedral, cylinder, dihedral, and rectangular plane, but also arc scatterers, such as sphere and top-hat. The estimation of the model parameters can be posed as an ill-posed linear inverse problem. To overcome the ill-posedness, we employ the incremental sparse Bayesian learning method to realize the sparsity-driven continuous parameter estimation. Inverse scattering experiments demonstrate that the proposed methodology not only provides desirable sparse representations of the target scattering response but is also able to capture richer geometrical information than the existing models.

In this paper, a transform-domain filtering method is proposed for polarimetric synthetic aperture radar (POLSAR) images via patch ordering and simultaneous sparse coding (SSC). First of all, we establish a signal-dependent additive noise model for the POLSAR covariance matrix and derive the noise variance for each element of the matrix based on the complex Wishart distribution. Next, we propose an extended patch ordering algorithm for POLSAR images by extracting sliding patches and organizing them in a regular way. Then, the ordered patches are filtered by SSC, for the purpose of which we develop a new weighted simultaneous orthogonal matching pursuit algorithm by embedding the signal-dependent noise model of the POLSAR data. Finally, the filtering result is reconstructed from the filtered patches via inverse permutation and subimage averaging. Experimental results with both simulated and real POLSAR images demonstrate that the proposed method can achieve state-of-the-art filtering performance.

One basic issue of importance in polarimetric synthetic aperture radar (SAR) imagery is the identification and separation of target scattering mechanisms. Physical scattering behaviors can be characterized by polarimetric parameters from the second-order statistical observables. The average copolarization phase difference, amplitude ratio, and target coherence are important fundamental parameters for identifying scattering mechanisms. However, the individual usages of these parameters could not describe both the scattering mechanisms and the depolarization. In this paper, a new approach is proposed for scattering characterization by exploring the information contained in these three parameters. First, by assuming reflection symmetry, a new parameter is proposed for the first time to measure the scattering randomness. Then, in combination with the scattering ratio (defined by the ratio of T22+ T33to T11), a classification plane is proposed to classify target scattering mechanisms. A validation test for this new approach is performed with three RADARSAT-2 polarimetric data sets acquired over two study areas: the San Francisco Bay area and Fuzhou, China. Results show that the new approach is very promising for distinguishing orientated targets (with respect to the radar azimuth direction) in urban areas from natural scatterers such as forests, and it also shows that the new method is robust for analyzing multitemporal polarimetric SAR data.

In order to avoid the disadvantages of CFAR detector and make full use of the polarimetric information, a novel method is proposed in this paper for detecting ships of polarimetric SAR images, based on tensor robust principle component analysis (tensor RPCA). This method is completely different from the traditional CFAR detector, and distribution model and sliding window are unnecessary. The polarimetric SAR image is firstly depicted by a tensor, then an improved version of tensor RPCA is applied to the tensor by using accelerated proximal gradient (APG) algorithm. For comparison, the polarimetric whitening filter (PWF) method is also used. Experiment results show that the proposed method has excellent performance.

Scattering mechanism identification is an important issue in polarimetric SAR imagery. In this study, we propose a new method for target scattering mechanism classification for the first time. Based on several typical scattering models, including the X-Bragg scattering model, Fresnel reflection model, and volume scattering models, a new polarimetric scattering classification diagram is proposed. It consists of two parameters, i.e., the Bragg alpha angle and a new parameter which is proposed for the first time and can be used to represent backscattering randomness. Results are demonstrated and validated on RADARSAT-2 data of the San Francisco area to illustrate its effectiveness.

In this letter, we propose an iterative synthetic aperture radar (SAR) image filtering method using the nonlocal sparse model. The original SAR image is first transformed to the logarithmic SAR image domain. Then, we use the nonlocal sparse model and the iterative regularization technique to denoise the log-intensity image. In each iteration, we update the noisy image and then estimate the noise variance. For each patch in the noisy image, we find several similar patches and stack them together in a group. This noisy group is filtered by simultaneous sparse coding. Then, all of the filtered groups are aggregated to form the denoised image. Experimental results demonstrate that the proposed method can achieve state-of-the-art SAR image despeckling performance.

Ocean surveillance is an important application in synthetic aperture radar (SAR) imagery. Polarimetric SAR (Pol-SAR) provides the multi-channel scattering information and hence is very promising for earth observation. Recently, the dual polarimetric SAR especially the hybrid dual-pol mode (i.e., compact polarimetry) has been given a growing concern. It can provide wide imaging swath coverage with reduced system complexity and thus is of great potential for maritime surveillance applications. Compact decomposition methods have been widely evaluated and applied to the land cover applications. In this study, we investigate the performances of compact decompositions for ship detection. The surveillance scenario is focused on the coastal areas, where low wind regions (LWR) and small ships with low backscattered intensity often appear in Pol-SAR imagery. The compact m-δ and m-χ decompositions are included to show the importance of the contribution of total backscattered energy in ship detection. Results are demonstrated on two RADARSAT-2 acquisitions over Tianjin port and Dalian port, China.

In this letter, a modified three-component model-based decomposition method is proposed to solve the problem of instability present in the Freeman decomposition method. We incorporate the similarity parameter and introduce a new constraint that describes the relationship between the powers of scattering components. The proposed method minimizes a continuous objective function under a set of constraints, which results in the stability of the decomposition method. Furthermore, a fast algorithm for solving the optimization problem is developed to accelerate computation. We demonstrate the stability of the proposed decomposition method by using the E-SAR polarimetric data acquired over the Oberpfaffenhofen area.

Mountainous areas in synthetic aperture radar (SAR) images suffer severe geometric distortions caused by different look directions. Consequently, ground control point (GCP) extraction hardly obtains accurate results for aircraft positioning. Based on the digital elevation model (DEM) and polarimetric SAR (PolSAR) data, we propose a method for extracting GCPs in mountainous areas by introducing the polarization orientation angle shift (POAS) to minimize geometric distortions. In this method, DEM data are used as the reference map by providing POASs at arbitrary look directions to make up for the look-direction sensitivity of POASs transformed from PolSAR data. The geometric distortions between POASs transformed from DEM and PolSAR data are effectively reduced by calculating the POASs from DEM data at the same look direction and look angles of the PolSAR data. In contrast to the SAR data, which have a large dynamic range, the values of POAS are limited to a small interval. Therefore, the illumination distortions induced by visualization can be reduced. Finally, the GCP extraction between the POAS images is conducted by bilateral filter scale-invariant feature transform. Experiments using various data at different look directions demonstrate that the proposed method obtains better-quality GCPs but less invalid keypoints than the method using only intensity images for mountainous areas.

In this letter, a new method for Kennaugh matrix decomposition is proposed, and a new norm for the Kennaugh matrix is defined. The Kennaugh matrix is decomposed into two parts: The first is a coherent target matrix, and the second is a residual matrix with minimum norm. The properties of the extracted coherent target are discussed, and an application of the extracted coherent target is implemented. In this application, an incoherent image is converted into a coherent image. Single-look sphere-diplane-helix decomposition is then performed. An experiment on Airborne SAR polarimetric data over San Francisco has been carried out, thus demonstrating the effectiveness of the application.

In this letter, we propose a novel track-to-track association (TTTA) algorithm based upon the reference topology (RET) feature, which is insensitive to sensor biases. A rigorous mathematical definition of RET is presented. The insensitivity of RET to sensor biases is analyzed theoretically. In order to construct the association cost matrix, we make use of the optimal subpattern assignment (OSPA) metric to measure the distance between two RETs. Simulation results demonstrate the advantages of the proposed algorithm.

In this paper, a general scheme for complete model-based decomposition of the polarimetric synthetic aperture radar (POLSAR) coherency matrix data is presented. We show that the POLSAR coherency matrix can be completely decomposed into three components contributed by volume scattering and two single scatterers (characterized by rank-1 matrices). Under this scheme, solving for the volume scattering power amounts to a generalized eigendecomposition problem, and the nonnegative power constraint uniquely determines the minimum eigenvalue as the volume scattering power. Furthermore, in order to discriminate the remaining components, we propose two approaches. One is based on eigendecomposition, and the other is based on model fitting, both of which are shown to properly resolve the surface and double-bounce scattering ambiguity. As a result, this paper in particular contributes to two pending needs for model-based POLSAR decomposition. First, it overcomes negative power problems, i.e., all the decomposed powers are strictly guaranteed to be nonnegative; and second, the three-component decomposition exactly accounts for every element of the observed coherency matrix, leading to a complete utilization of the fully polarimetric information.

This paper investigates the application of a level set method for the automated multiphase segmentation of multiband and polarimetric synthetic aperture radar (SAR) images. The level set formulation is used to form an energy functional that includes the image statistical information defined on active contours. In addition to the classical Wishart/Gaussian distribution for locating region boundaries, edge information is incorporated into the energy functional to improve the performance of polarimetric data segmentation. An active contour model with an edge indicator is proposed by assuming that the image boundary term follows a Gibbs prior. An empirical parameter setting criterion is developed to ensure that the components of the energy functional are in proper proportion. We then investigate the multiphase extension for energy minimization, and we use a piecewise constant model to embed the proposed active contour model. Synthetic and real multiband polarimetric SAR data are used for verification. The experiments show that our method is superior to another level set method based on the Wishart/Gaussian distribution, in which SAR edge information is not included, particularly for discriminating among low-contrast regions. Furthermore, results also show that segmentation is improved when multiband data are used in the level set framework.

In this paper, a new parameter called Bragg energy proportion is introduced for spill oil detection, based on Freeman decomposition model. Bragg energy proportion is small in spill region and it is large in sea clutter region or Oleyl Alcohol (OLA) region. So, this parameter can be used to detect oil spill from sea clutter and meanwhile it reduces the OLA false-alarm effectively. In addition, the parameter is derived by HH\VV dual-polarized SAR data. Using C band SIR-C SAR data, the authors demonstrate the effectiveness of the proposed method.

Polarization ratio and co-polarized phase information are very important for polarimetric synthetic aperture radar (SAR) image interpretation, especially in the area where a single scattering mechanism is dominant. In this study, a new method including both the parameters for scattering characterization is proposed based on the extended Bragg scattering (X-Bragg) model. The theoretical analysis, which is based on the first-order Bragg scattering and the dihedral scattering models, is consistent with the demonstration of real polarimetric SAR data. The proposed method is evaluated on the entropy/alpha plane, showing its promise for distinguishing between different target scatterings. In experiment, L-band ALOS/PALSAR fully polarimetric data over Tasmania, Australia are used for illustrating the effectiveness of this method for forest/nonforest discrimination. Results from Cloude-Pottier's decomposition theorem are also given for comparison.

The statistical behavior of the sea clutter in synthetic aperture radar (SAR) images is characterized by both the marginal distribution and the spatial correlation. However, simultaneous modeling of the joint information remains a difficult job because of the non-Gaussian clutter nature. In this paper, a semiparametric approach is proposed for addressing this problem. First, we investigate the applicability of the nonparametric kernel density estimator (KDE) for estimating the marginal distribution of the SAR clutter and show that the KDE is most applicable in the log-intensity domain. Second, we propose to estimate the underlying spatial correlation structure with a copula approach and show that the Gaussian copula is a sufficiently accurate model. Consequently, the KDE, together with the Gaussian copula, offers a full characterization of the joint probability distribution, based on which a quadratic detector of null distribution governed by the well-known chi-squared law can be conveniently designed for constant false alarm rate detection. In the experiment, results with both simulated and real SAR data demonstrate that, compared with the single-point detector using only the marginal distribution, the proposed method, which incorporates spatial correlation, significantly improves the detection performance with regard to either the receiver-operating-characteristic curve or detected target pixels. The tradeoff, however, lies in a loss of false alarm rate control resulting from increased uncertainty in estimating higher dimensional distributions.

This paper introduces a novel method for filtering polarimetric synthetic aperture radar (POLSAR) imagery data. Unlike most existing filters, the proposed one is freed from using searching windows but implements the filtering in a sequential fashion. Specifically, we propose to use the inverse Wishart distribution as the conjugate prior model for the coherency matrix (CM) data and show that the maximum a posteriori (MAP) solution leads to a very simple linear recursive formula for estimate update, through which issues such as feature preservation and bias correction can be also taken into account. In the experiment, the proposed method is tested on the POLSAR dataset acquired by the DLR (Germany Aerospace Center) F-SAR system. Results demonstrate that the filter shows inspiring performances in terms of both noise reduction and feature preservation.

Synthetic Aperture Radar (SAR) has been proved as an effective instrument for detecting vertical structures of forest canopies. However, the quality of SAR images could decline because of phase errors caused by the residual motion deviations after motion compensation or autofocus process. Then, the accuracy of forest height inversion will be affected. In this paper, the relationship between residual phase errors and the accuracy of the retrieved height is established via three-dimensional forest canopies simulation of SAR. The residual motion deviations with different amplitudes and frequencies are considered in simulation experiments. Reasonable levels of estimated phase errors to reach high accuracy of retrieved height are achieved. It is demonstrated that higher accuracy of estimated phase errors is demanded for forest height inversion based on SAR data.

In this paper, a new method is proposed for ship detection based on generalized optimization of polarimetric contrast enhancement (GOPCE). Since the scattering coefficients of a sea area vary with local incident angle, pixels at different incident angles should be processed differently. The proposed detection method takes incident angle into consideration, and can be realized in two ways: using prior knowledge or local adaptive. In the first approach, sea clutter statistics at different incident angles are calculated in advance, followed by traditional GOPCE detection. The other method performs a two-stage detection. In the first stage, the polarimetric whitening filter (PWF) is applied for coarse detection. In the second stage, a moving window is applied to estimate local statistics adaptively, achieving refined detection. Effectiveness of the proposed method is demonstrated using Radarsat-2 data, and detection results of various detectors are compared.

A new approach is proposed for ship detection, based on the generalized optimization of polarimetric contrast enhancement (OPCE). The new approach consists of two steps. For the first step, the OPCE problem will be solved for obtaining optimal polarizations. In this way, the received powers of a sea area are suppressed. For the second step, we construct a function which not only contains the power term but also contains a term associated with the scattering mechanism. Three optimal coefficients will be obtained such that the variance of the function values of a sea clutter area is minimal. In this way, ships on the sea area are detected. Using two examples, we demonstrate the effectiveness of the proposed approach for ship detection.

The un-correlation assumption between like- and cross-polarizations as well as the standard trihedral target is widely used in many calibration methods. However, it is inconvenient to deploy standard calibrators in any scene, and the utilization of un-correlation assumption should also be carefully examined. In this study, we propose a practical polarimetric calibration method which uses neither the standard trihedral nor the assumption that like- and cross-polarizations are uncorrelated. Alternatively, the statistical property of odd-bounce target is extracted to substitute the standard trihedral. By applying the proposed method on the ALOS/PALSAR uncalibrated data, it is shown that the polarization signature of the trihedral reflector agrees excellently with the theoretical value. Comparing to the standard ALOS/PALSAR calibration result, the non-reciprocal effect including the Faraday rotation has been removed by the proposed method.

A stable model-based three-component decomposition approach for polarimetric SAR data is proposed in this paper. Two problems in the standard Freeman decomposition: (1) the instability of the decomposition; (2) the emergence of negative powers, have been stated. The proposed approach uses the regularization method to solve the problems, by minimizing a continuous objective function consisting two terms: (1) distance between the measured coherency matrix and the reconstructed one by the decomposition method; (2) the regularization term which implicates prior information. The AIRSAR polarimetric data acquired over San Francisco are decomposited by the proposed method. The results show that the stability is improved by the proposed decomposition approach and the negative powers are also eliminated by introducing constraints to the solution domain of decomposition.

Visual interpretation of SAR images plays a critical role in remote sensing applications. For rapidly obtaining the image suitable for human observation, a new method of visualization for SAR images is proposed in this paper. The statistics of the SAR data, including the single channel data intensity and the span of the Pol-SAR data, are derived. The proposed method is a parameterized method based on the characteristic statistic of the SAR data. Using the AIRSAR data, the effectiveness of the proposed method is demonstrated by comparing to the traditional algorithm.

A modified Yamaguchi decomposition with deorientation is proposed for analyzing multilook polarimetric synthetic aperture radar (PolSAR) data. Deorientation is first applied to a coherence matrix; then, the coherence matrix is decomposed into four components by the Yamaguchi decomposition. A special kind of target is found for which the original Yamaguchi decomposition output is not appropriate. This problem is solved by applying deorientation prior to the Yamaguchi decomposition. Moreover, the deorientation procedure enhances double-bounce scattering from urban areas in the decomposition output. Comparisons of the Yamaguchi decompositions with and without deorientation are shown for PolSAR data from both airborne and spaceborne systems.

In this letter, a censoring scheme that iteratively updates the outlier/target maps for target detection in synthetic aperture radar (SAR) images is proposed. For each iteration, any pixels that are indicated by the outlier map as outliers are rejected (censored out) from the clutter estimation. The resulting detected target map is then used as the new outlier map for the next iteration. This procedure is continued until there is no change to the target map, which is then output as the final detection result. The proposed scheme is generically applicable for target detection in both single-channel and multichannel SAR images. In our experiment, in particular, we tested the proposed method on both single-channel and polarimetric SAR data, and its effectiveness was successfully demonstrated.

In this letter, an unsupervised method for estimating the equivalent number of looks (ENL) in synthetic aperture radar (SAR) images is proposed. Assuming that the multiplicative noise in the SAR images is a unit mean random variable of Gamma distribution parameterized by the ENL, a lookup table between the ENL and the variance of the logarithm of the multiplicative noise is established. Then, for a SAR image, we first apply the logarithmic transform to make the multiplicative noise become additive. After that, the variance of the additive noise in the logarithmic image is estimated; finally, the ENL is obtained according to the estimated noise variance by the pre-established lookup table. The effectiveness of the proposed method is demonstrated with both simulated and real SAR images.

A new method for estimating the rotational motion of a noncooperative target is presented by exploiting the 2-D space-variant characteristic of inverse synthetic aperture radar imaging. With a space-time analysis technique, the position variation of every extracted scattering center in both the range and cross-range directions can be utilized for rotation estimation. This method differs substantially from conventional methods that exploit high-order terms of the Doppler frequency, i.e., only the position variation in the cross-range direction. An iterative processing scheme is proposed to extract as many scattering centers as possible from the phase history of returned echoes. Experimental results with some measured airplane data demonstrate the effectiveness of the proposed method.

A pretest approach based on the complex Wishart distribution in polarimetric synthetic aperture radar (POLSAR) speckle filtering is proposed in this paper. The main principle is to select homogeneous pixels in a large-scale area in the filtering process, which is called pretesting. To preserve details and fine structures while despeckling, the homogeneous pixels are selected by comparing their 3 × 3 neighboring windows. A test statistic based on the complex Wishart distribution is used to decide the selection of homogeneous pixels. Speckle filtering is processed by summing up the homogeneous pixels with weights according to the values of their test statistics. To accelerate the pretest filter, we further propose a refined algorithm, which eliminates redundant operations without deteriorating the performance. We demonstrate the performance of the proposed algorithm by using both simulated and real airborne POLSAR data.

Recently, the use of linear features for processing remote-sensing images has shown its importance in applications. Unfortunately, traditional linear feature detection methods rely heavily on the image's local information which makes them vulnerable to the presence of noise in the image. This problem becomes particularly difficult for synthetic aperture radar (SAR) image applications where SAR images are corrupted by speckle noise. In order to overcome this problem, we propose a novel method that processes the polarimetric synthetic aperture radar (Pol-SAR) images by combining the multiscale image analysis with polarimetric information in a new fashion. A two-scale approach is adopted here. On a coarse level, the coarse regions of the linear features are extracted by a curvelet transform from a speckle noise reduced image obtained by the polarimetric whitening filter. On a fine level, we develop a fuzzy polarimetric detector to accurately locate the linear features inside the regions. The effectiveness of the proposed method is demonstrated using simulated Pol-SAR data acquired from both EMISAR and Convair-580 systems.

The polarimetric entropy (H) and the alpha angle (α) are two important parameters for analyzing polarimetric synthetic aperture radar data. However, for some special cases, the unstableness of the alpha angle is found; in addition, time consumption for extracting both the parameters will become quite tedious for very large images by pixelwise eigendecomposition. To overcome these shortcomings, a fast algorithm to calculate the polarimetric entropy and two new parameters are proposed in this paper. The first parameter has similar properties to the alpha angle but is stable; the second also behaves similar to the polarimetric entropy. More importantly, both new parameters can be derived very quickly. Like the unsupervisedHα classification, the image can also be classified based on the two new parameters. The NASA/JPL AIRSAR L-band data of San Francisco are applied in the experiment.

An improved three-component decomposition for polarimetric synthetic aperture radar (SAR) data is proposed in this paper. The reasons for the emergence of negative powers in the Freeman decomposition have been analyzed, and three corresponding improvements are included in the proposed method. First, the deorientation process is applied to the coherency matrix before it is decomposed into three scattering components. Then, the coherency matrix with the maximal polarimetric entropy, i.e., the unit matrix, is used as the new volume-scattering model instead of the original one adopted in the Freeman decomposition. A power constraint is also added to the proposed three-component decomposition. The E-SAR polarimetric data acquired over the Oberpfaffenhofen area in Germany are applied in the experiment. The results show that the pixels with negative powers are totally eliminated by the proposed decomposition, demonstrating the effectiveness of the new model.

This letter attempts to address the problem on the emergence of negative eigenvalues in the coherency matrix after the compressing of multilook polarimetric synthetic aperture radar (SAR) data. A new nine-parameter expression for the eigenvalue decomposition of the coherency matrix is introduced, and a new compression algorithm is proposed for multilook polarimetric data with this expression. By comparing it with the NASA/Jet Propulsion Laboratory's Airborne SAR compression algorithm, the authors analyze the new algorithm's compression accuracy, signal-to-noise ratio, and the ability to preserve the data's polarimetric property. For polarimetric SAR data, it is important to preserve the polarimetric property of a target. The proposed algorithm has this ability which is illustrated by the comparison of the polarimetric signatures. Finally, the effectiveness of the proposed methods is demonstrated by using the experimental SAR data.

In this letter, polarization cross-entropy is introduced based on the eigendecomposition of the polarimetric coherence matrix. Then, the new parameter is employed for ship detection. From experimental results, it is derived that the distribution of the polarization cross-entropy in ocean regions can be well approximated by a generalized exponential distribution. Then, a constant-false-alarm-rate ship-detection method is proposed based on the distribution of the polarization cross-entropy. Using experimental results, the authors demonstrate the effectiveness of the new method in ship detection.

The volume coherence is an important parameter in the two-layer model for random forest height retrieval. Though it is "invisible" from the observation, it can be estimated from the data. Considering the argument range of the coherences for all the possible polarizations, six polarizations can be obtained for each scattering cell. The volume coherence can be estimated more reasonably and the ground phase center can be determined more accurately. It benefits to improve the height retrieval accuracy by this model. The proposed method is validated by using L-band single-baseline polarimetric interferometric SAR data collected with the DLR Experimental SAR system.

A novel method is presented to detect roads in synthetic aperture radar (SAR) images. A multi-segmented poly-line model is introduced to provide a more accurate description of the road as well as to ensure the road curve's smoothness in the model level. We then solve the road detection problem using the Bayesian tracking theory, where the particle filtering algorithm is adopted to provide a simple and consistent framework. The effectiveness and robustness of the proposed method is demonstrated by experimental results.

For some special case, Huynen's decomposition cannot be used to extract a desired target from an average Kennaugh matrix. In this paper, the authors modify Huynen's method for overcoming its disadvantage, based on a simple transform of a Kennaugh matrix. Using an example, the effectiveness of the modified method is validated.

Abstract —The polarization entropy is an important parameter and it has been used for target classification, target detection and so on. For calculating the polarization entropy, we have to obtain the eigenvalues of a covariance matrix and then use logarithm. Therefore, some calculation cost is necessary to get all the entropy values of adjacent n by n windows for all pixels in a polarimetric SAR image. In this paper, the authors propose a new method to calculate the polarization entropy, based on the least square method. Using a polarimetric SAR image, we validate the effectiveness of the proposed method. Keywords-polarization; Synthetic Aperture Radar (SAR); remote sensing I. I NTRODUCTION In polarimetric radar remote sensing, the polarization entropy is one of important parameters for target classification, target detection and so on [1-6]. For calculating the polarization entropy, however, one has to obtain the eigenvalues of a covariance matrix and then use logarithm. Consequently, some computation cost is necessary when we need get all the entropy values of adjacent n by n windows of every pixel in a polarimetric SAR image. In general, there may be more than one million pixels in a SAR image. So it is important to propose a simple method to calculate the polarization entropy. This paper will solve this problem. II. T

A generalized optimization of polarimetric contrast enhancement (GOPCE) is proposed in this letter. For this problem, it is not only necessary to find the optimal polarization states such that the received power ratio of a desired target and clutter is maximal, but also necessary to find three optimal coefficients such that the ratio of two factors associated with the desired target and clutter is maximal, where both the factors consist of three parameters, i.e., the Cloude entropy and two special similarity parameters. The optimal coefficients of the GOPCE are obtained by solving an eigenvalue problem. Using an example, we demonstrate that the GOPCE can be employed for detecting roads in a forest area by using polarimetric synthetic aperture radar data.

In this paper, a new approach to speckle filtering of synthetic aperture radar (SAR) data is presented. We define a parameter space consisting of two orthogonal subspaces-the signal subspace and the noise subspace. Then, the full polarimetric information from the signal subspace is obtained after speckle filtering. In this way, edges of different kinds of targets are preserved. The effectiveness of this method is demonstrated using the National Aeronautics and Space Administration Jet Propulsion Laboratory airborne L-band polarimetric SAR data.

In this paper, the concept of cross-entropy is introduced for measuring the difference between two scatterers in polarimetric SAR image classification. The difference between two scatterers is composed of three parts, i.e., the difference of average scattering mechanism, the difference of scattering randomness and the difference of the two spans of scattering matrices. The minimum cross-entropy principle is adopted to make the classification decision. The capability of this method is demonstrated by the experimental results.

Characteristic polarization state theory is restudied for the symmetric coherent Sinclair scattering matrix case. First, the geometric relations of the characteristic polarization states on the Poincare sphere are derived. Based on these relations, simple formulas are given for all of the characteristic polarization states of this Sinclair matrix in Stokes vector form. From the formulation, it is clear that the CO-POL Nulls are fundamental characteristic polarization states for the symmetric coherent Sinclair scattering matrix case, in that the others can straightforwardly be obtained from the Stokes vectors of the CO-POL Nulls. For further study of the characteristic polarization state and the distribution of the received powers on the Poincare sphere, the authors introduce the concept of the equi-power curve. It is defined as the curve on the Poincare sphere on which the received powers in some defined channel have the same value. They deal with the characteristics of the equi-power curves for various special cases. In addition, they show how the characteristic polarization states are generated by the equi-power curves. It is demonstrated that the characteristic polarization states can usually be regarded as the points of contact of the Poincare sphere and a conicoid representing a power-related quadratic form. This leads to a new method to introduce the characteristic polarization states.

In a co- or cross-polarized channel, the polarization states of the transmitting and receiving antennas are the same or orthogonal, and the corresponding target nulls (i.e., the co-pol nulls or x-pol nulls) are defined as the polarization states of the transmitting antenna such that the received power equals zero. However, no systematic studies have been carried out to solve the problem of the corresponding target nulls if the polarization states of the transmitting and receiving antennas are independent. In this paper, the target null theory is extended to the case of two independent polarization states. For two arbitrary independent symmetric scattering matrices, it is proved that there exists only one pair of polarization states such that both of the received powers equal zero. This polarization states' pair is called the co-null of the two targets, which can easily be obtained by solving an eigenvalue problem. Based on this concept and algebraic theory, the concept of the co-null space is introduced for the symmetric scattering matrix case, and many important results are presented, e.g., the relations between the co-null and the co-pol/x-pol nulls, the properties of the co-null space, and the relation between the co-null and target decomposition. Finally, the co-null for the asymmetric scattering matrix case is studied. The concepts of the mono-co-null space and the bi-co-null space are introduced, and the relations between both spaces are presented.

This paper proposes two effective numerical methods to solve the optimal problem of contrast enhancement for the coherent and incoherent cases, respectively. For the coherent case, the objective function of the optimal problem is transformed into a bilinear form. Then a numerical method is presented by using the idea of the Sequential Unconstrained Minimization Technique (SUMT). For the incoherent case, a cross-iterative method is proposed for solving the optimal problem of contrast enhancement, based on the formula of the optimal contrast polarization state in the matched-polarized channel. Both the proposed methods are convergent and straightforward for programming. In addition, the proposed methods can be used for the bistatic radar case because in this paper, it is unnecessary to restrict the symmetry of the scattering matrix and the Kennaugh matrix. For showing the effectiveness of the proposed methods, the authors give three examples. The results of the calculation are completely identical with other papers', showing the validity of the proposed methods. In these three examples, if the receiving polarization state is independent of the transmitting's, the power ratios may be much larger than those in the cases of the co-polarized, the cross-polarized, and the matched-polarized channels, showing the importance of the considered models. In addition, the computation costs are estimated by these illustrative examples, illustrating that the proposed methods are very effective.