Compact polarimetric target detection is currently attracting increasing interest due to the generation of synthetic aperture radar (SAR) systems. Recently, the polarimetric notch filter (PNF) has been demonstrated to be effective for ship detection in both full-polarization and compact polarization-mode SAR images. In this paper, the probability density function (PDF) and the probability of false alarms (PFA) of the PNF are derived in the K-Wishart case (where the texture obeys the gamma distribution) since K distributions are suitable for many scenes including sea clutter. The PDF and PFA are also derived in closed forms for the polarimetric whitening filter (PWF) in the K-Wishart distribution case, the performance of which is close to that of the optimal polarimetric detector. The constant false alarm rate threshold for ship detection can be obtained from the PFA via the bisection method, which allows for the automatic and adaptive implementation of ship detection in complicated sea backgrounds in practical applications. The performances of the two detectors are compared via simulated data and measured data from AIRSAR in the compact polarimetric SAR case. The results show that the detectors given are accurate, while the PWF detector is generally better than the PNF detector in the K-Wishart case of the CP SAR model.

Polarimetric synthetic aperture radar (PolSAR) is an important tool for marine remote sensing. In the past two decades, subaperture decomposition is considered as an alternative method to extract ship targets, especially in complex conditions where the detection based on intensity performs not well. The introduction of polarization information can enhance the detection ability of radar. The combination of sublook techniques and polarization will lead to more precise target extraction. The first methods jointly using information of polarization and sublook is the polarimetric internal Hermitian product (Pol-IHP), which nowadays has been corrected as Pol-HIP. However, it is limited by the number of sublooks and recently developed as generalized multi-sublooks correlation. In this article, a novel sublook polarimetric covariance matrix (SPCM) is established, based on which a sublook-polarimetric whitening filter (sub-PWF) is further proposed for ship detection. The SPCM represents a given pixel using a high-dimensional covariance matrix which utilizes the spectral correlation in imaging progress and the scattering characteristics of different polarization channels. The superiority of sub-PWF based on SPCM is validated via the measured single look complex PolSAR data from RadarSAT-2 and GaoFen-3. Results show this new method can improve the target to clutter ratio and reduce the clutter fluctuation. The ROC curves in both small and large scenes proves that sub-PWF can reach ideal detection performance.

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.

The utilization of polarimetric synthetic aperture radar (PolSAR) enables the preservation of more comprehensive target scattering characteristics compared to conventional SAR. Despite the significant advancements in deep learning (DL) technology for single polarimetric SAR ship detection, there remains a scarcity of DL research specifically focused on PolSAR ship detection. This article proposed a lightweight theory-driven network (LT-Net) for combining domain knowledge and DL techniques, which is suitable to detect small targets and reduce the computational complexity. The LT-Net incorporates convolutional block attention module, and employs differential downsampling during the process of high-dimensional feature extraction. The integration of domain knowledge lends each convolution step a distinct physical significance. Due to the limited availability of public datasets, this article presents a public fully polarized ship detection (FPSD) dataset for the first time, which contains 853 Pauli pseudocolor maps (in JPG format) and multilook complex data (in TIF format), with a total of 1714 ship targets from AIRSAR, UAVSAR, and RadarSAT-2. The FPSD encompasses a range of scenarios, with the majority of ship targets being small targets. Validation on FPSD shows that LT-Net exhibits significantly lower time complexity and space complexity compared to four popular target detection networks, Meanwhile, LT-Net achieves the best detection performance in the Pauli pseudocolor maps.

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

Ship wake detection using synthetic aperture radar (SAR) imagery provides a way to obtain small marine ship information, but it often becomes unavailable and unreliable during a high sea state. Polarimetric information provides a potential way to solve this problem, which can enhance the ship target as well as the ship wake features. However, three challenges still exist in ship wake detection in polarimetric SAR imagery: the unwanted influences of bright and singular points on ship wake detection, the lack of performance analysis of wake detection by new-type polarimetric enhancement methods, and the difficulty of using the assessment criteria for ship wake detection. In this paper, we try to solve the above problems. Firstly, fully polarized SAR imagery of both ship turbulent and Kelvin wake is simulated based on the two-scale composite model, and the Polarimetric Whitening Filter (PWF) and Polarimetric Detection Optimization Filter (PDOF) are applied to the simulated fully polarized SAR imagery to enhance the ship wake features. Secondly, since the bright and singular points resulting from the ship echoes and the polarimetric enhancement methods may lead to misdetections, a logarithm process and z-score normalization pre-processing has been applied to the images. Then, a new assessment criterion for wake detection performance has been formulated, and the probability of missing detections (PMDs) and the probability of false alarms (PFAs) have been defined for two different requirements. And a Radon transform-based ship wake detection method for both ship turbulence and Kelvin wake has been carried out in horizontal–horizontal (HH), vertical–vertical (VV), horizontal–vertical (HV), PWF and PDOF SAR imagery. Finally, an analysis of the wake detection performance has been carried out. The PWF and PDOF can improve the wake detection performance by an average of nearly 50 percent compared with the HH and VV.