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Proceedings Paper

Optimum band selection evaluation for landslide studies in temperate environments
Author(s): Mohammad Firuz Ramli; David Petley; William Murphy; Rob Inkpen
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Paper Abstract

The problems associated with the selection of the optimum band combination have been recognised since the early days of the Landsat MSS. Although this instrument consists of only four bands, it was soon realised that the number of bands would increase as the technology advanced. Obviously, to generate and analyse every combination of bands is potentially time-consuming. Various statistical methods of choosing the band combination containing the most information for environmental and geological studies such as optimum index factor, maximum variance-covariance determinant, and principal component analysis have been introduced. In order to evaluate the applicability of these methods to landslide studies, the eleven band Airborne Thematic Mapper (ATM) imagery of the inland slopes of Stonebarrow Hill in West Dorset, England was used. These slopes are extensively mantled with relict landslide features. The best band combination results obtained from these methods are evaluated against the visually checked imagery, where all possible bands are generated and classified in terms of texture and colour. The ability to express the texture and colour in the composite imagery that might be related to the landslide features are crucial in landslide studies.The results showed that all these statistical methods are not suitable to be used in landslides study. However, from early visual classification results showed that two combinations of three bands from three different wavelengths produced the best composite image.

Paper Details

Date Published: 14 March 2003
PDF: 9 pages
Proc. SPIE 4886, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II, (14 March 2003); doi: 10.1117/12.462324
Show Author Affiliations
Mohammad Firuz Ramli, Univ. Putra Malaysia (Malaysia)
David Petley, Univ. of Durham (United Kingdom)
William Murphy, Univ. of Leeds (United Kingdom)
Rob Inkpen, Univ. of Portsmouth (United Kingdom)

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

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