Unsupervised classification of polarimetric SAR images via SV[formula omitted]MM with extended variational inference

Abstract

This paper addresses the unsupervised classification problem for multilook polarimetric synthetic aperture radar (PolSAR) images via proposing a new spatially variant Gp0 mixture model based on the extended variational inference (SVGp0MM-EVI). Specifically, an SVGp0MM is constructed by associating data points with their own mixing coefficient vectors rather than sharing the same vector, which provides the additional flexibility to incorporate the spatial context and further effectively deal with the more heterogeneous areas than Gp0MM. Then, an extended variational inference (EVI) algorithm is derived to facilitate the assignment of the conjugate prior distributions and accurately estimate the underlying parameters, in which two “help” functions are specially designed to solve the intractable variational lower bound. Meanwhile, dual spatial constraints based on the Bartlett distance and the mean template are also explored on polarimetric matrix and the hyperparameter in the posteriori distribution of mixing coefficients, respectively, thus adequately capturing the local correlation from the polarimetric matrix and the geometry perspectives. Furthermore, the learning algorithm with all the closed-form updates is developed and the cluster number could be automatically determined. Five PolSAR data sets (including two labeled data sets), which are obtained by different sensors, are used to verify the effectiveness of the proposed model. Experimental results illustrate that the proposed SVGp0MM-EVI is beneficial to classification task of PolSAR images, and achieves higher accuracy (e.g., 95.65% and 97.41% OA values) compared with some widely-used methods (i.e., H/α-Wishart, Chernoff–Wishart, Gp0MM, SVWMM and CK-HDRF methods).