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
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摘要: 该文首先采用H/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．Abstract: In this paper, initial assumption of SAR pixel distribution is derived from H/a classifier. Then a Maximum Likelihood (ML) method is introduced to improve the classifi-cation.. 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.
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