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Texture-based segmentation of LiDAR imagery
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In this study, we implement and apply a region growing segmentation procedure based on texture to extract spatial landform objects from a light detection and ranging (LiDAR) digital surface model (DSM). The local binary pattern (LBP) operator, modeling texture, is integrated into a region growing segmentation algorithm to identify landform objects. We apply a multi-scale LBP operator to describe texture at different scales. The paper is illustrated with a case study that involves segmentation of coastal landform objects using a LiDAR DSM of a coastal area in the UK. Landform objects can be identified with the combination of a multi-scale texture measure and a region growing segmentation. We show that meaningful coastal landform objects can be extracted with this algorithm. Uncertainty values provide useful information on transition zones or fuzzy boundaries between objects.
|Keywords:||Multi-scale texture; Region growing; Landform objects; Local binary pattern (LBP) operator|
|Journal or Publication Title:||Internationl Journal of Applied Earth Observation and Geoinformation|
|Page Range:||pp. 261-270|
|Identification Number - DOI:||10.1016/j.jag.2004.10.008|
The definitive version is available at http://www.sciencedirect.com
|Date Deposited:||24 Feb 2009 00:14|
|Last Modified:||18 Nov 2014 03:56|
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