LAND-COVER CLASSIFICATION OF SAR IMAGES BY COMBINING LOW-LEVEL FEATURES AND CATEGORY CONTEXT

摘要

A novel land-cover classification framework for HR SAR images which combines low-level features and category context is presented in this paper. We use patch-based features for low-level information extraction, including average intensity, texture within a patch and the super texture we proposed to model the texture similarity of neighboring patches. To represent the local category context of SAR images, we propose the label layout filter. This work resolves local ambiguities of low-level features from a category context perspective. The framework demonstrates good performance in both accuracy and visual appearance for HR SAR scene interpretation.