高分辨率SAR图像地物分类算法研究

本文给出了一种关于高分辨率、单极化Synthetic Aperture Radar(SAR)图像目标分类的完整算法.首先,利用基于改进的相关邻域模型的Radar Cross-Section(RCS)重构算法进行目标和阴影检测,通过非线性积累把图像分割为“阴影区”、“背景区”和“目标区”;接着通过形态学算子进行地貌滤波和边缘提取,应用改进的Hough变换和细线化措施进行线段的连接和编组;最后利用空间联合位置模型进行目标的分类.

An Algorithm of Extended Terrain Classification in High Resolution SAR Images

A target classification algorithm in high resolution and single polarimetric SAR image is presented in this paper. Firstly,a new RCS reconstruction filter based on the modified correlated neighborhood model is used for target and shadow detection. By nonlinear iterated process,the whole image can be classified to "background","shadow" and "targets".Secondly,a series of mor-phological operators are used to terrain filtering and edge extraction,and then through modified Hough Transform technology and the measure of slimming lines,line structures of man-made clutters in "target" class are linked and grouped.Finally,with special association model,the targets we are interested in are classified.