A Framework for Automatic Building Detection from Low-Contrast VHR Satellite Imagery

Automatic separation of buildings from built-up area has attracted considerable interest in computer vision and digital photogrammetry field. While many efforts have been made for building extraction, none of them address the problem completely. This even a greater challenge in low-contrast very-high resolution (VHR) panchromatic satellite images. To alleviate this issue, a framework for automatic building detection approach using dominant structural feature (DSF) is proposed in this study. Firstly, in order to suppress noise while enhancing structural feature, contourlet transform based image contrast enhancement is employed followed by directional morphological filtering operation. Considering the structural characteristics of buildings which are significantly different from the other non-manmade objects. We then exploit DSF by means of windowed structure tensor analysis. Candidate building edges are generated using multi-seed classification technique in DSF space, subsequently. Finally, a series rule- and knowledge-based criterions are elaborate designed for false alarm reduction procedures.