Statistical Segmentation of Polarimetric Sar Data

This paper introduces an approach to the classification and interpretation of SAR data using complementary polarimetric and interferometric information. Strictly polarimetric and polarimetric interferometric data are first analyzed and classified separately. An unsupervised polarimetric segmentation, based on multivariate Wishart statistics, is applied to one of the separate polarimetric datasets. Pertinent polarimetric indicators permit to classify the observed scene into three canonical scattering types. The interpretation and the segmentation of an optimized interferometric coherency set leads to the discrimination of different natural media that cannot be achieved with polarimetric data only. Each type of scattering mechanism is processed through an unsupervised statistical interferometric classification procedure. The resulting classes show an enhanced description and understanding of the observed scene.