Synthetic aperture radar image segmentation using non‐linear diffusion‐based hierarchical triplet Markov fields model

Abstract

Triplet Markov fields (TMF) model is widely used to deal with non-stationary synthetic aperture radar (SAR) images. However, its ability to capture global information remains limited due to the non-causal property. A hierarchical TMF model is proposed in this study based on the non-linear diffusion (ND) strategy, which is denoted as ND-hierarchical TMF (HTMF). ND is adopted to generate multiscale decomposition according to local image content, and that is superior to traditional wavelet decomposition in reflecting hierarchical nature of image structure and detailed features. The auxiliary field in ND-HTMF is redefined and initialised on the finest scale to characterise edge information and that enhances the prior modelling ability for non-stationary local image features. The multiscale likelihood and multiscale causal prior energy functions are then defined respectively in bottom-up and top-down procedures to capture local and global information for performing segmentation. Segmentation experiments on simulated and real SAR images demonstrate the effectiveness of ND-HTMF in both edge characterisation accuracy and robustness against speckle noise.