基于去取向理论的全极化SAR图像模糊非监督聚类

由于复杂散射体的随机取向导致其回波具有一定的波动性，利用目标分解理论对全极化SAR图像进行分类时，分类结果会出现一定程度的错分现象。该文提出了一种新的非监督分类算法，该算法首先根据去取向理论，将目标向量旋转到最小交叉极化方向；然后，采用u/v/H参数描述散射机制，以模糊隶属函数代替参数平面的硬阈值划分；最后，以多元复Wishart分布描述相干矩阵，基于Bayes极大似然分类准则进行分类。以中国广东淡水附近的L波段NASA/JPL SIR-C全极化SAR图像作为实验数据进行了仿真试验，并进一步对聚类中心的迁移进行了讨论。试验和讨论结果表明:同基于H/和类k-mean的算法比较，该文的聚类算法对聚类效果有明显改善，类别对应的散射机制也更为准确，分类结果有利于地表类型的自动识别。

Unsupervised Classification of Polarimetric SAR Image Using Deorientation Theory and Complex Wishart Distribution

Scatter targets of complex terrain surfaces with random orientation product random fluctuating echoes. This leads to a confused classification by directly using target decomposition on full polarimetric SAR (PolSAR) image. To solve this problem, a new unsupervised classification method is proposed in this paper. Firstly, the target vector is transformed to the state with minimization of cross-polarization (min-x-pol); then the parameters u/v/H are used to characterized scattering mechanism, and the fuzzy membership is adopted instead of "hard" division of parameter plan; finally, characterizing the coherency matrix as multivariable complex Wishart distribution, the polarimetric SAR image is classified based on Bayes maximum likelihood criteria. Experiment is performed on a L-band NASA/JPL SIR-C polarimetric SAR image over Danshui town, Guangdong, P.R. China. Furthermore, the movements of the clustering centers are discussed. Compared with the k-mean like method based on , the results show that the proposed method provides a significant performance improvement in classification result and the associated scattering mechanism of class is more accurate. The classification result is beneficial for automatic recognition of terrain type.