SAR Image Labeling with Hierarchical Markov Aspect Models

Scene segmentation and semantic labeling are im- portant problems in SAR image interpretation. This paper pro- poses an efficient SAR imagery labeling method based on aspec t model which can be learnt from keywords-labeled training data directly. Furthermore, a novel hierarchical Markov aspect model (HMAM) is presented by building aspect model on quadtree. HMAM outperform both aspect model and hierarchical MRFs due to their complementary as aspect model use global relevance estimates while quadtree can further explore image context and multi-scale cues. The experimental results on TerraSAR-X dataset show that our labeling method is effective and effici ent, and demonstrate that HMAM improve labeling performance significantly with only a modest increase in learning and inf erence complexity than aspect model. Index Terms—Synthetic Aperture radar(SAR),Image labeling, Hierarchical markov aspect model,Quadtree.