Automatic detection and mapping of urban buildings in high resolution remote sensing images

In high resolution remote sensing images, urban buildings always have characteristics of complex structures and are vulnerable to background interference. For the purpose of detecting and mapping urban buildings automatically in that circumstance, a novel method is proposed in this paper. Firstly, the Conditional Random Field (CRF) is introduced to fuse multiple kinds of features to get the areas objects existing, then we propose a Hierarchical Object Process Model (HOPM), which is used to access to the location of objects as well as accurate depictions of their outline, and finally the corner detection method is utilized to delineate the vector shapes of objects. Competitive results for multiform and complicated urban buildings demonstrate the precision and robustness of the proposed method.