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Texture-based segmentation of LiDAR imagery

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Lucieer, A and Stein, A (2005) Texture-based segmentation of LiDAR imagery. Internationl Journal of Applied Earth Observation and Geoinformation, 6 (3-4). pp. 261-270. ISSN 0303-2434

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Abstract

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.

Item Type: Article
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
ISSN: 0303-2434
Identification Number - DOI: 10.1016/j.jag.2004.10.008
Additional Information: The definitive version is available at http://www.sciencedirect.com
Date Deposited: 24 Feb 2009 00:14
Last Modified: 18 Nov 2014 03:56
URI: http://eprints.utas.edu.au/id/eprint/8391
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