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Proceedings Paper

Comparison of kernel-based methods for spectral signature detection and classification of hyperspectral images
Author(s): L. Capobianco; L. Carli; A. Garzelli; F. Nencini
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Paper Abstract

The aim of this paper is to assess and compare the performance of two kernel-based classification methods based on two different approaches. On one hand the Support Vector Machine (SVM), which in the last years has shown excellent results for hard classification of hyperspectral data; on the other hand a detection method called Kernel Orthogonal Subspace Projection KOSP, proposed in a recent paper.1 To this aim, the widely used "Indian Pine" Aviris dataset is adopted, and a common "test protocol" has been considered: both methods have been tested adopting the one-vs-rest strategy, i.e. by performing the detection of each spectral signature (representing one of the N classes) and by considering the spectral signatures of the remaining N - 1 classes as background. The same dimensionality of the training set is also considered in both approaches.

Paper Details

Date Published: 29 September 2006
PDF: 12 pages
Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 63650W (29 September 2006); doi: 10.1117/12.690188
Show Author Affiliations
L. Capobianco, Univ. of Siena (Italy)
L. Carli, Univ. of Siena (Italy)
A. Garzelli, Univ. of Siena (Italy)
F. Nencini, Univ. of Siena (Italy)


Published in SPIE Proceedings Vol. 6365:
Image and Signal Processing for Remote Sensing XII
Lorenzo Bruzzone, Editor(s)

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