A Framework for Automatic Building Detection from Low-Contrast VHR Satellite Imagery
Authors: 
Junjun Li, Jiannong CaoAuthors Info & Claims
Junjun Li, Jiannong CaoAuthors Info & ClaimsPages 52 - 56
Abstract
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.
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Published In
December 2019
270 pages
ISBN:9781450376822
DOI:10.1145/3376067
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- Shanghai Jiao Tong University: Shanghai Jiao Tong University
- Xidian University
- TU: Tianjin University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 February 2020
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ICVIP 2019
ICVIP 2019: 2019 the 3rd International Conference on Video and Image Processing
December 20 - 23, 2019
Shanghai, China
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