结合自适应脉压与排列熵特征的陆海分割方法

A Method of Land-Sea Segmentation Combining Adaptive Pulse Compression with Permutation Entropy Features

摘要:

本文针对陆海交接处强陆地杂波距离旁瓣影响近岸海上目标检测能力的问题，提出了一种基于自适应脉冲压缩与排列熵特征提取相结合的陆海分割方法。该方法首先采用两级相位补偿的自适应脉冲压缩技术，抑制近岸强地物杂波距离旁瓣对陆海分割的影响；随后利用每个距离方位分辨单元中的观测数据计算该单元的排列熵，该特征显著增强了地物回波与海面回波的对比度；最后利用大津法图像分割和形态学处理等数字图像 处理技术，提取出陆海分界线。实测数据处理结果表明，自适应脉冲压缩与排列熵特征提取相结合，能够有效提升陆海回波的对比度，保证了陆海分界线提取的准确性。

Abstract:

This paper proposes a land-sea segmentation method based on the combination of adaptive pulse compression and permutation entropy feature extraction, aimed at addressing the issue of strong land clutter sidelobes near coastlines affecting the detection capability of offshore targets. The method initially employs a two-stage phase compensation adaptive pulse compression technique to suppress the impact of strong nearshore land clutter sidelobes on land-sea segmentation. Subsequently, the permutation entropy of each distance and azimuth resolution cell is calculated using the observational data within the cell, significantly enhancing the contrast between land echo and sea surface echo. Finally, digital image processing techniques such as Otsu"s method for image segmentation and morphological processing are utilized to extract the land-sea boundary line. The processing results of actual measured data indicate that the combination of adaptive pulse compression and permutation entropy feature extraction effectively enhances the contrast between land and sea echoes, ensuring the accuracy of land-sea boundary line extraction.