Triplet Hybrid Discriminative Fields Model Based on Bayesian Fusion for Unsupervised Nonstationary SAR Image Multiclass Segmentation

Abstract

The discriminative random fields (DRF) model is suitable for analyzing images with complex textural structures and has achieved promising results in image segmentation. However, the DRF model does not consider the nonstationarity of synthetic aperture radar (SAR) images and lacks the ability to model SAR scattering statistics in nonstationary SAR image segmentation. In this paper, we propose a triplet hybrid discriminative random fields (THDF) model based on Bayesian fusion. According to its semantic structure, the THDF model belongs to hybrid discriminative models, and it provides the following promising contributions to nonstationary SAR image segmentation while inheriting the advantages of the discriminative models: first, it takes the nonstationarity of SAR images into account from the perspective of their texton appearances, and thus regulates the local label interaction patterns and considers the distribution differences of the congeneric image features in different stationary parts; and second, for nonstationary SAR images, it performs a fusion-type treatment of the nonstationary textural features and the SAR scattering statistics based on Bayesian fusion and, thus, captures the nonstationary information from SAR data in a more complete manner. The effectiveness of the proposed model is demonstrated through applications to both synthetic images and real SAR image segmentations.