An adaptive-trimming-depth-based constant false alarm rate (ATD-CFAR) ship detector of heterogeneous environment in SAR imagery is proposed in this letter. Traditional CFAR detectors generally use all samples in the background window for parameter estimation. However, in the heterogeneous regions, these detectors will overestimate the parameters used for statistical modelling due to the interference of high-intensity interference pixels such as adjacent ships, ghosts, breakwaters and azimuth ambiguity, which leads to the missing detection of ship targets. To solve this problem, we design an adaptive-depth-based method for clutter trimming in the local reference window, so the interference pixels can be effectively removed, while the real sea clutter samples can be retained to the greatest extent. After that, the maximum likelihood estimator is used for parameter estimation, where the estimation accuracy is greatly elevated, and statistical model of the sea clutter is precisely established.

本文提出了一种基于自适应修剪深度的SAR图像异构环境恒虚警率（ATD-CFAR）舰船检测器。传统的CFAR检测器通常使用背景窗口中的所有样本进行参数估计。但是，在异类区域中，由于高强度干扰像素（例如相邻的船只，幽灵，防波堤和方位模糊度）的干扰，这些检测器会高估用于统计建模的参数，从而导致缺少对船只目标的检测。为解决这一问题，我们设计了一种基于自适应深度的局部参考窗口内杂波修剪方法，可以有效去除干扰像素，同时最大程度地保留真实的海杂波样本。之后，