Analysis of fully polarimetric SAR data based on the Cloude-Pottier decomposition and the complex Wishart classifier

An estimation of the number of clusters is proposed for fully polarimetric SAR data analysis, and a corresponding unsupervised segmentation algorithm is also given based on the Cloude-Pottier decomposition and the complex Wishart clustering. The Monte-Carlo Cross-Validation (MCCV) is used to estimate the optimal number of clusters to reveal the inner structure of the data. Since it is a quantitative estimation of the classification performance, the MCCV algorithm also has the potential capability to perform the unsupervised segmentation validation. The effectiveness of the MCCV estimation and the segmentation algorithm is demonstrated using ESAR data acquired.