Building extraction using local surface normal angle transformation

Y. Li; T. Qian

doi:10.1117/12.760094

1 August 2007 Building extraction using local surface normal angle transformation

Y. Li, T. Qian

Proceedings Volume 6751, Geoinformatics 2007: Cartographic Theory and Models; 67511E (2007) https://doi.org/10.1117/12.760094
Event: Geoinformatics 2007, 2007, Nanjing, China

Abstract

Building detection and extraction from digital surface model (DSM) becomes more and more attracted when high resolution airborn radar and CCD sensor find their more applications in photogrammetric field. Because DSM contains the terrain heights, we firstly derive DTM from DSM, and then generate Normalized DSM (NDSM). The buildings are extracted from NDSM. However, since urban appearance is complex, with large buildings, small buildings, trees in mass, etc., building extraction is implemented through several stages. Local Surface Normal Angle Transform (LSNAT) is implemented to the height field. Big buildings are distinguished from other large regions generated from binary NDSM. Watershed segmentation is used to detect trees in mass together with LSNAT. Roofs of the small building are extracted based on the histogram of LSNAT. A case study is presented and analyzed in the end of this paper.

Citation Download Citation

Y. Li and T. Qian "Building extraction using local surface normal angle transformation", Proc. SPIE 6751, Geoinformatics 2007: Cartographic Theory and Models, 67511E (1 August 2007); https://doi.org/10.1117/12.760094

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KEYWORDS

Image filtering

Image segmentation

Binary data

Data acquisition

Data modeling

Digital filtering

Geographic information systems

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