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Proceedings Paper

Hyperspectral classification applied to the Belgian coastline
Author(s): Pieter Kempeneers; Steve De Backer; Sam Provoost; Walter Debruyn; Paul Scheunders
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Paper Abstract

Hyperspectral image classification impose challenging requirements to a classifier. It is well known that more spectral bands can be difficult to process and introduce problems such as the Hughes phenomenon. Nevertheless, user requirements are very demanding, as expectations grow with the available number of spectral bands: subtle differences in a large number of classes must be distinguished. As multiclass classifiers become rather complex for a large number of classes, a combination of binary classification results are often used to come to a class decision. In this approach, the posterior probability is retained for each of the binary classifiers. From these, a combined posterior probability for the multiclass case is obtained. The proposed technique is applied to map the highly diverse Belgian coastline. In total, 17 vegetation types are defined. Additionally, bare soil, shadow, water and urban area are also classified. The posterior probabilities are used for unmixing. This is demonstrated for 4 classes: bare soil and 3 vegetation classes. Results are very promosing, outperforming other approaches such as linear unmixing.

Paper Details

Date Published: 18 October 2005
PDF: 9 pages
Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820F (18 October 2005); doi: 10.1117/12.627602
Show Author Affiliations
Pieter Kempeneers, Flemish Institute for Technological Research (Belgium)
Steve De Backer, Univ. of Antwerpen (Belgium)
Sam Provoost, Institute for Nature Conservation (Belgium)
Walter Debruyn, Flemish Institute for Technological Research (Belgium)
Paul Scheunders, Univ. of Antwerpen (Belgium)


Published in SPIE Proceedings Vol. 5982:
Image and Signal Processing for Remote Sensing XI
Lorenzo Bruzzone, Editor(s)

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