A fundamental issue of maritime applications of synthetic aperture radar (SAR) data is the development of precise statistical models for clutter pixels. Several statistical models including the GK, K+R, and ${\mathcal{G}}_{\text{AO}}$ have been demonstrated to be promising for characterizing sea clutter in SAR images. This article is devoted to investigating the improvements in clutter fitting and ship detection performances by using the recently proposed ${\mathcal{G}}_{\text{AO}}$ , compared to that using the GK and K+R. First, the solution uniqueness of parameter estimators by applying the “method of log cumulants” for the ${\mathcal{G}}_{\text{AO}}$ is mathematically proven in the first time. Then, we assess the fitting performance of different models for sea surfaces with different wind speed conditions. Next, the constant false alarm rate (CFAR) detection performance of ships based on different models is compared by the indicators of CFAR loss and detection efficiency. Experiments performed on L -band ALOS-PALSAR SAR data verify the modeling capability of the ${\mathcal{G}}_{\text{AO}}$ model for sea clutter. Moreover, several ship detection examples indicate the usefulness and potential of the ${\mathcal{G}}_{\text{AO}}$ model for CFAR detection in practical applications.

In this article, we propose a target detection algorithm with a polarization feature-driven neural network for compact polarimetric (CP) SAR data. First, we use the power–entropy (PE) decomposition theory to obtain the polarization features $h_{\mathrm {esa}}$ of the CP SAR data, then input $ h_{\mathrm {esa}}$ into the neural network RetinaNet for training, and finally perform prediction on the test dataset. We compare the proposed method in this article with the constant false alarm rate (CFAR) algorithm, and the experimental results show that the neural network-based target detection algorithm has fewer false alarms while ensuring detection accuracy.

In recent years, marine oil spills have adversely affected the marine economy and ecosystem, and the detection of marine oil slicks has attracted great attention. Combining different polarimetric features for better oil spill detection is a topic that needs to be studied in depth. Previous studies have shown that the compact polarimetric (CP) synthetic aperture radar (SAR) can be effectively applied to the detection of sea surface oil spill due to its own ability, which is conducive to the extraction of sea surface oil slick. In this paper, we apply the power–entropy (PE) decomposition theory, which decomposes the total scattered power according to the entropy contribution of each cell in the response, to CP SAR data for oil spill detection. The purpose of this study is to enhance the oil slick and the separability of the sea. As a result, an oil spill detection method based on the low-entropy radiation amplitude parameter $l_{esa}$ is proposed. We compare $l_{esa}$ with the other five popular polarimetric features and validate by quantitative evaluation that $l_{esa}$ is superior to other types of polarization feature parameters under different band data. Moreover, the random forest classification is performed on the feature map and achieves the visualization results of oil spill detection. The experimental results show that the $l_{esa}$ can combine the information of the two polarimetric characteristic parameters of entropy and total scattering power, and can clearly indicate the oil slick information under different scenarios.