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Proceedings Paper

Geomorphological feature extraction from a digital elevation model through fuzzy knowledge-based classification
Author(s): Demetre P. Argialas; Angelos Tzotsos
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Paper Abstract

The objective of this research was the investigation of advanced image analysis methods for geomorphological mapping. Methods employed included multiresolution segmentation of the Digital Elevation Model (DEM) GTOPO30 and fuzzy knowledge based classification of the segmented DEM into three geomorphological classes: mountain ranges, piedmonts and basins. The study area was a segment of the Basin and Range Physiographic Province in Nevada, USA. The implementation was made in eCognition. In particular, the segmentation of GTOPO30 resulted into primitive objects. The knowledge-based classification of the primitive objects based on their elevation and shape parameters, resulted in the extraction of the geomorphological features. The resulted boundaries in comparison to those by previous studies were found satisfactory. It is concluded that geomorphological feature extraction can be carried out through fuzzy knowledge based classification as implemented in eCognition.

Paper Details

Date Published: 14 March 2003
PDF: 12 pages
Proc. SPIE 4886, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II, (14 March 2003); doi: 10.1117/12.463279
Show Author Affiliations
Demetre P. Argialas, National Technical Univ. of Athens (United States)
Angelos Tzotsos, National Technical Univ. of Athens (United States)

Published in SPIE Proceedings Vol. 4886:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II
Manfred Ehlers, Editor(s)

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