Semantic Conditional Random Field for Object Based SAR Image Segmentation

Abstract

Conditional random filed (CRF) model relaxes the conditional independence of the observed data and simultaneously captures the spatial contextual information. However, the single spatial contextual model is difficult to describe the heterogeneous structures of the synthetic aperture radar (SAR) images. This paper propose an semantic conditional random field (SCRF), which integrate the semantic space and pixel space for object-based SAR image segmentation. Specifically, the SAR image is divided into aggregated, structural and homogeneous subspaces by using the hierarchical semantic model. Then, we design gaussian kernel function, geometric kernel function and uniform kernel function to adaptively describe the spatial contextual constraints in the different subspaces. These kernel functions are incorporated into the pairwise potential of CRF model to improve the ability of the model. Afterwards, the piecewise training and Bayesian inference are proposed to achieve the object-based segmentation. Experiments on the synthetic and real SAR images demonstrate the effectiveness of the proposed method in the semantic consistency and detail preservations.