Texture-based landform segmentation of LiDAR imagery [An article from: International Journal of Applied Earth Observations and Geoinformation]

This digital document is a journal article from International Journal of Applied Earth Observations and Geoinformation, published by Elsevier in 2005. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
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.