Fusion of high-resolution InSAR data and optical imagery for building detection using Conditional Random Fields

State-of-the-art satellite SAR sensors (e.g., TerraSAR-X, CosmoSkyMed) provide imagery of one meter resolution and airborne SAR sensors achieve even higher resolutions. In those data objects in urban areas become visible in high detail. However, SAR-typical effects like layover, shadowing, and interfering backscatter of multiple objects complicate interpretability. Thus, additional information about those objects may be obtained from optical imagery. In this work we combine features of high-resolution airborne interferometric SAR (InSAR) data with features of an orthophoto in order to detect buildings. A Conditional Random Field (CRF) is set up in order to integrate context-knowledge. We show that CRFs are a suitable method for integration of both context-knowledge and multi-sensor features for building extraction.