Texture-based landform segmentation of LiDAR Imagery

A. Lucieer, A. Stein

Research output: Contribution to journalArticleAcademicpeer-review

44 Citations (Scopus)


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
Original languageEnglish
Pages (from-to)261-270
JournalInternational Journal of applied Earth Observation and Geoinformation
Publication statusPublished - 2005


  • classification
  • objects

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