Hierarchical Method of Urban Building Extraction Inspired by Human Perception
In a high-resolution satellite image, buildings can be considered as clustered objects belonging to the same category. Human perception of such objects consists of an initial identification of simple instances followed by recognition of more complicated ones by deduction. Inspired by this observatio...
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Veröffentlicht in: | Photogrammetric engineering and remote sensing 2013-12, Vol.79 (12), p.1109-1119 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | In a high-resolution satellite image, buildings can be considered as clustered objects belonging to the same category. Human perception of such objects consists of an initial identification of simple instances followed by recognition of more complicated ones by deduction. Inspired
by this observation, a hierarchical building extraction framework is proposed to simulate the process, which includes three major components. First, a total variation-based segmentation algorithm is presented to decompose the given image into object-level elements. Then, shape analysis is
applied to extract some common and easily identified rectangular buildings. Finally, the detection of buildings with complex structures is formulated as a deduction problem based on preceding extracted information in terms of maximum a posteriori (MAP) estimation, and a Bayesian based approach
is proposed to deal with it. The experimental results demonstrate that the proposed framework is capable of efficiently identifying urban buildings from high-resolution satellite images. |
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ISSN: | 0099-1112 2374-8079 |
DOI: | 10.14358/PERS.79.12.1109 |