中文摘要：

利用Neumann分解理论中体散射模型的各向异性与方向角随机程度对植被的形态特征加以区分,建立各向异性-方向角随机程度平面进行初始分类。在此基础之上,利用Wishart距离进行迭代分类。选取德国Oberpfaffenhofen地区E-SAR L波段、SIR-C/X-SAR L和C波段三种极化数据进行实验,结果表明,本文方法在植被覆盖区分类效果优于Freeman-Duren和Yamaguchi模型分类结果,能较好地区分针叶林、阔叶林,且L波段分类结果优于C波段分类结果。

英文摘要：

Apolarimetric SAR classification method is proposed in this paper. We can distinguish the morphology characteristic and do initial classification using anisotropy and orientation randomness. After that, Wishart distance is used to iteratively classify. E-SAR L band, SIR-C/X-SAR L band and SIR-C/X-SAR C band polarimetric data of Oberpfaffenhofen, Germany are employed to test the new method. The results demonstrate that the result of new method is better than the results of Freeman- Duren and Yamaguchi for vegetation area.