Outlier-Robust Superpixel-Level CFAR Detector With Truncated Clutter for Single Look Complex SAR Images

The constant false alarm rate (CFAR) detectors are well studied for ship detection in synthetic aperture radar (SAR) images, which suffer performance degradation due to the capture effect from interfering outliers, such as nearby targets, sidelobes, and ghosts in multitarget 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 article, 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-to-fine 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 state-of-the-art methods based on the Sentinel-1 and Gaofen-3 SAR data validate the performance of the proposed method.

Automated summary

This article proposes a ship detection method that combines complex signal kurtosis and clutter truncation, improving detection accuracy and efficiency through optimized clutter modeling and detection processes.