为了有效地对极化SAR图像进行分类，基于目标分解和支持向量机，提出了一种极化SAR图像非监督分类法。该方法首先利用目标分解理论获得极化熵和平均散射角，并在熵 平均散射角平面对图像进行初分类，以确定类中心；然后利用Wishart分布定义的距离函数寻找训练样本，同时选择一定的极化参数组成特征矢量，并利用训练样本和特征矢量训练支持向量机；最后用训练好的分类器对极化SAR图像进行分类。通过对ESAR图像进行分类，比较了多种参数组合的分类结果，并与Wishart方法进行了比较，结果表明，该方法特征选择非常灵活，不仅结果类内离散度更小，且不需要太多的迭代次数。

This paper presents a new method for unsupervised classification of terrain types using polarimetric synthetic aperture radar data. This unsupervised classification combines the target decomposition theory and the support vector machines. The initial cluster centers are firstly determined by target decomposition advanced by Cloude and Pottier. Then the pixels near to the cluster centers are selected to train the support vector machines using Wishart distribution. The classified results are then used to define training sets for the next iteration if necessary. Finally, by the optimal separating hyperplanes and the kernel method this method obtains extraordinary classification results and neednot much iteration. And the effects of feature vectors consisted of several polarimetric parameters are discussed in detail.