极化分解与子孔径分析相结合的极化SAR图像分类

PolSAR Image Classification by Polarization Decomposition Associated with Subaperture Analysis

本文提出了一种极化SAR图像分类的新方法,该方法将传统极化分解与子孔径分析结合起来。首先将全分辨率极化SAR图像分解成几个子孔径图像,利用子孔径分析对两类非平稳目标进行检测,得到场景中的非平稳目标。然后对全分辨率图像的相干矩阵进行特征分解,得到熵(H)和α角两个参数,并在H-α平面上对地面目标进行分类;最后,将非平稳目标检测结果与H/α分类结果结合起来,对极化SAR图像进行更为精细的分类。仿真结果表明,本文提出的方法取得了更好的分类效果。

A PolSAR image classification method which combines traditional polarization decomposition and subaperture analysis is proposed. Firstly, a full resolution PolSAR image is decomposed into several subapertures and two kinds of nonstationary targets are detected. Then, eigenvalue decomposition is done on the coherency matrices of full resolution SAR images. The two parameters, entropy H and angle a are obtained. With these two parameters, targets can be classified based on H-a plane. Finally, combining the nonstationary detection results and the H-a classification results, the classification with higher fineness is done. Simulation results show that better classification effects are obtained with the proposed method.