Landform classification in raster geo-images

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

6 Scopus citations

Abstract

We present an approach to perform a landform classification of raster geo-images to obtain the semantics of DEMs. We consider the following raster layers: slope, profile curvature and plan curvature, which have been built to identify the intrinsic properties of the landscape. We use a multi-valued raster to integrate these layers. The attributes of the multi-valued raster are classified to identify the landform elements. The classification approach is used to find the terrain characteristics of the water movement. Moreover, we describe the mechanisms to compute the primary attributes of digital terrain model. The method has been implemented into Geographical Information System-ArcInfo, and applied for Tamaulipas State, Mexico.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAlberto Sanfeliu, Jose Francisco Martinez-Trinidad, Jesus Ariel Carrasco-Ochoa
PublisherSpringer Verlag
Pages558-565
Number of pages8
ISBN (Print)3540235272
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3287
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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