Classifying polarimetric SAR data by combining expectation methods with spatial context

Abstract

Unsupervised classification is an essential step in the automatic analysis of SAR remote sensing data. Classification results make SAR data easier to interpret and can serve as a starting point for automated analysis techniques that apply to homogeneous regions of the observed scene. Polarimetric SAR data are particularly interesting for unsupervised classification purposes, since they contain a great amount of information, allowing robust statistical clustering of the image content on the one hand and a direct physical interpretation of the result on the other. This paper proposes a new unsupervised classification approach for polarimetric SAR data. Assuming Wishart-distributed polarimetric covariance matrices, it combines spectral clustering based on the covariance matrices themselves with spatial clustering by statistical analysis of local neighbourhoods. Instead of working with binary assignments of samples to class centres, a soft decision rule is used in which each pixel is assigned to all class centres in the spectral and spatial domains. The local neighbourhood is taken into account by altering the probabilities of class membership by a neighbourhood function, obtained from normalized compatibility coefficients, describing cluster sizes and mutual tolerance. In this way, robust and homogenous classification results can be obtained even in the presence of strong speckle noise.