A fast, weighted CRF algorithm based on a two-step superpixel generation for SAR image segmentation

Among the Markov random field methods, conditional random fields (CRF) model has shown good results in Synthetic Aperture Radar (SAR) image segmentation. CRF not only directly models the posterior distribution of label field conditioned on images but also allows the interactions between observations. In this paper, we propose a Fast, Weighted CRF (FWCRF) algorithm based on a two-step superpixel generation method. In our method, heterogeneity of intensity in SAR images due to speckle noise and backscattering is considered. The first step is a preprocessing task for suppressing speckle noise which is done by the L-0 smoothing algorithm. It is followed by Sobel edge detector to highlight heterogeneous regions and edges. Then SAR image is partitioned into homogeneous and heterogeneous regions by using fast robust fuzzy c-means clustering (FRFCM). Simultaneously, the simple linear iterative clustering (SLIC) algorithm is applied to split the image into superpixels. Next, the superpixels belonging to the same class are merged by using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. Finally, the SAR image is labelled using the FWCRF algorithm which consists of weighted pairwise potential based on adaptive features and applying FRFCM as an initial segmentation method. The proposed algorithm is evaluated in comparison with other conditional random field schemes. The results of our proposed method demonstrate accuracy improvement in segmentation.