The constant false alarm rate (CFAR) detectors are well studied for ship detection in SAR images, which suffer performance degradation due to the capture effect from interfering outliers, such as nearby targets, sidelobes and ghosts in multi-target environments. To address this issue, the clutter truncation scheme is adopted to reduce the outlier contamination in clutter samples such that the accuracy of clutter modeling can be improved. However, the selection of clutter truncation depth is difficult, which often resorts to sensitivity study. In this paper, the complex signal kurtosis (CSK) is first utilized as a statistical indicator for the decision of truncation depth to guarantee that the true clutter samples are maintained. Besides, a coarse-tofine detection process is designed, including global superpixel proposal with the CSK and local identification of target pixels with the superpixel-level CFAR detector based on truncated statistics. During the local CFAR detection stage, the segmented superpixels provide convenient sample indexing for the iterative clutter truncation processing. The elevated performance achieved by the proposed method mainly benefits from the schemes of two-stage detection and automatic clutter truncation, yielding the increased detection efficiency and accuracy at the same time. Besides, false alarms caused by radio frequency interference can be reduced. In the experiment, the comparative results with stateof-the-art methods based on the Sentinel-1 and Gaofen-3 SAR data validate the performance of the proposed method.

Ship detection based on synthetic aperture radar (SAR) imagery is one of the key applications for maritime security. Compared with single-channel SAR images, polarimetric SAR (PolSAR) data contains the fully-polarized information, which better facilitates better discriminating between targets, sea clutter, and interference. Therefore, many ship detection methods based on the polarimetric scattering mechanism have been studied. To deal with the false alarms caused by the existence of ghost targets, resulting from azimuth ambiguities and interference from side lobes, a modified polarimetric notch filter (PNF) is proposed for PolSAR ship detection. In the proposed method, the third eigenvalue obtained by the eigenvalue–eigenvector decomposition of the polarimetric covariance matrix is utilized to construct a new feature vector. Then, the target power can be computed to construct the modified PNF detector. On the one hand, the detection rate of ship targets can be enhanced by target-to-clutter contrast. On the other hand, false alarms resulting from azimuth ambiguities and side lobes can be reduced to an extent. Experimental results based on three C-band AIRSAR PolSAR datasets demonstrated the capability of the proposed PNF detector to improve detection performance while reducing false alarms. To be specific, the figure of merit (FoM) of the proposed method is the highest among comparative approaches with results of 80%, 100%, and 100% for the tested datasets, respectively.

This paper proposes a multiscan joint non-coherent detection method of small targets in high-resolution sea clutter with spatial-temporally structural textures induced by large-scale swells and waves, which is composed of intrascan non-coherent integration at each spatial resolution cell followed by interscan integration on a radial velocity template. The main contributions of the paper are as follows. Firstly, a concept of non-coherent radial velocity spectrum (NCRVS) is proposed to model the nonhomogeneity of multiscan integration of sea clutter with structural textures. In the NCRVS, the non-coherent intrascan integration and the interscan integration by passing the retrospective filter (RF) bank are fully combined. Secondly, the NCRVS of sea clutter has different statistics at individual radial velocity bins and the lognormal distributions are used to fit their statistics and an outlier-robust analytical bi-percentile estimator is constructed for parameter estimation. Thirdly, based on the lognormaldistributed NCRVS model, a double-channel multiscan joint detector (DC-MJD) is proposed, where the intrascan non-coherent integration channel is used to find floating or low radial velocity targets and the whitening plus intrascan non-coherent integration channel is used to find target with high radial velocity. Simulated data and measured X-band radar data are used to verify the DC-MJD detector. In comparison with the existing three multiscan detectors, the DC-MJD detector improves the detection probability by 96% in the measured data with a test target.

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