SAR IMAGE CLASSIFICATION BASED ON CRFS WITH OBJECT STRUCTURE PRIORS

Fine-scale classification in form of object extraction or segmentation for high resolution SAR images is a challenging task due to the existing local noises, object deformation and part missing. A novel SAR classification method based on CRFs which combines low-level features, label context and object structure priors is presented in this paper. Local label pattern is proposed in this paper to model the object structures by measuring the local label configuration on the grid layer of SAR images. We build a new CRFs model with label context and object structure priors for image classification. Besides, we adopt Mean Field approximation for efficient inference of our CRFs model. This work intends to implement an efficient classification framework by integrating high-level label context and object priors and apply it to fine-scale object extraction of SAR images. The framework demonstrates good performance in both accuracy and efficiency for object extraction or segmentation of simulated images and high resolution SAR images.