The Unsupervised Classification Based On The Cloude-Pottier Decomposition For Fully Polarimetric SAR Data Of Chinese Academy Of Sciences

The land cover classification is one of the most important, applications inpolarimetry remote sensing. The main advantage of fully polarimetric SAR datais that it can use target decomposition algorithm to extract the informationof the scattering mechanisms, which are not data specific and can be used fortarget identification. Thus using fully polarimetric SAR data we can achieve anunsupervised classification without ground truth information.In this paper, an improvement for the Cloude-Pottier decomposition is givenfor analysis. We use the backscattering power information to improve the per-formance of the Cloude-Pottier decomposition and the transform algorithm isalso given to represent directly the decomposition results. Several unsupervisedclassification algorithms are also proposed to improve the classification perfor-mance step by step. Firstly, the Wishart SPAN/H/αclassification is proposed tointroduce the backscattering power information to the Wishart H/α/A classifi-cation. Then in order to include the scattering information within the parameterA, the Wishart SPAN/H/α/A classification is given to use the four parametersSPAN/H/α/A for initialization, and the Wishart test statistic is applied to re-duce the number of classes. The Wishart SPAN/H/α/A classification uses apredefined number of classes to perform the classification scheme. In fact, all theunsupervised classifications for fully polarimetric SAR data proposed nowadaysuse fixed number of clusters to classify the data. It is more reasonable to deter-mine the number of clusters directly from data analysis. According to the CrossValidation theorem in pattern recognition, We proposed the Cross-Validationlog-likelihood based on the complex Wishart distribution to estimate the opti-mal number of classes to reveal the inner structure of fully polarimetric SARdata. Using the Cross-Validation log-likelihood, a new unsupervised classifica-tion method, the Wishart SPAN/H/α/A classification with an adaptive numberof classes, is also given for interpretation. The number of classes is automati-cally optimized to avoid overfitting and underfitting for the inner structure of fully polarimetric SAR data. Moreover, since the Cross Validation algorithm is aquantitative estimation of the classification performance, it also has the potentialcapability to perform the validation of unsupervised classification.