Sketching Model and Higher Order Neighborhood Markov Random Field-Based SAR Image Segmentation

Abstract

The Markov random field (MRF) model has been successfully applied to synthetic aperture radar (SAR) image segmentation because of its excellent ability of capturing the local contextual information in the prior model. However, the geometric structures of the SAR image are always ignored when capturing the contextual information in the prior model. Therefore, this letter presents a new SAR image segmentation method based on the sketching model and higher order neighborhood MRF. In this approach, the sketching model is utilized to represent the geometric structures of the SAR image. Meanwhile, a higher order neighborhood is constructed to capture the complex priors. Then, according to the structure fluctuation in the higher order neighborhood, the homogeneous and heterogeneous neighborhoods are distinguished. Finally, the local energy function in the prior model is constructed in the higher order neighborhood with different characteristics. Specifically, the energy functions considering the labeling consistency and focusing on the structure preservations are designed for the homogeneous and heterogeneous neighborhoods, respectively. In this way, the ability of the prior model is improved by adding the geometric structures into the energy functions. Experiments on the real SAR images demonstrate the effectiveness of the proposed method in labeling consistency and structure preservations.