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Texture-based landform segmentation of LiDAR imagery


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

DOI: doi:10.1016/j.jag.2004.10.008


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 multiscale 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. (c) 2004 Elsevier B.V. All rights reserved.

Item Details

Item Type:Refereed Article
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Application software packages
UTAS Author:Lucieer, A (Professor Arko Lucieer)
ID Code:33374
Year Published:2005
Web of Science® Times Cited:41
Deposited By:Geography and Environmental Studies
Deposited On:2005-07-13
Last Modified:2011-11-17

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