Unsupervised Classification of PolInSAR Data Based on Shannon Entropy Characterization With Iterative Optimization

In this paper, we propose a modified unsupervised classification method for the analysis of polarimetric and interferometric synthetic aperture radar (PolInSAR) images using the intensity, polarimetric and interferometric contributions to the Shannon entropy characterization. In order to improve the classification accuracy where the polarimetric information is similar, the method gives intensity, polarimetric and interferometric information equal weighting to more effectively use the full range of information contained in PolInSAR data. In addition, this method uses an iterative clustering scheme which combines the expectation maximization (EM) and fast primal-dual (FastPD) optimization techniques to improve the classification quality. The first step of the method is to extract the Shannon entropy characterization from the PolInSAR data. Then, the image is initially classified respectively by the spans of the intensity, polarimetric and interferometric contributions to Shannon entropy. Finally, classification results of different contributions are merged and reduced to a specified number of clusters. An iterative clustering scheme is applied to further improve the classification results. The effectiveness of this method is demonstrated with DLR (German Aerospace Center) E-SAR PolInSAR data and CETC (China Electronics Technology Group Corporation) 38 Institute PolInSAR data.