双波段全极化SAR图像非监督分类方法及实验研究  

Unsupervised Classification Methods and Experimental Research of Dual-frequency Fully Polarimetric SAR Images

作　　者：刘秀清[1] 杨汝良[1] 杨震[1] 

机构地区：[1]中国科学院电子学研究所

出　　处：《电子与信息学报》2004年第11期1738-1745,共8页Journal of Electronics & Information Technology

摘　　要：该文首先采用H/α分类对像素进行了初始猜测,然后进一步采用Bayes最大似然估计(ML)分类法对像素进行重新归类,不同波段电磁波对地物散射具有不同的属性,因而我们采用双波段全极化SAR数据结合的分类方法,得到了更好的分类结果。SAR图像的相干斑会影响图像的分类准确度和精度,在进行分类处理前,对双波段全极化SAR图像相干斑进行矢量滤波处理。该文使用NASA/JPL实验室在天山地区的实测数据对这些分类算法进行了实验研究,给出了单波段以及双波段全极化SAR分类结果的伪彩色图,其中双波段全极化SAR滤波后数据具有相对最优的分类结果。In this paper, initial assumption of SAR pixel distribution is derived from H/α classifier. Then a Maximum Likelihood (ML) method is introduced to improve the classification. The backscattering properties of a natural medium, that varies with the observation frequency, dual-frequency SAR images are combined to get further improved classification. Speckle in SAR images will disturb classification accuracy. Vector filter of speckle is used to dual-frequency images before classification. Experiments are done on data got by NASA/JPL lab near Tien Mountains, and pseudo-colored classification results of both single and dual frequency POLSAR image are submitted. Results show that filtered dual-frequency fully polarimetric SAR data obtain the best classification result.

关 键 词：全极化SAR 非监督分类 相干斑 矢量滤波 

分 类 号：TN951[电子电信—信号与信息处理]