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Proceedings Paper

Independent component analysis to hyperspectral image classification
Author(s): Qian Du
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Paper Abstract

Independent component analysis (ICA) is a popular approach to blind source separation. In this paper, we investigate its application to hyperspectral image classification. In particular, the performance of Joint Approximate Diagonalization of Eigenmatrices (JADE) algorithm is studied. The major advantage of using ICA is its capability of classifying objects with unkown spectral signatures in an unkown image scene, i.e., unsupervised classification. However, ICA suffers from computational expensiveness, which limits its application to high dimensional data analysis. In order to make it applicable to hyperspectral image classification, a data preprocessing procedure is employed to select the most important bands based on the band image quality. The number of bands ought to be selected is predetermined by an estimation method. The preliminary results from experiments demonstrate the potential of ICA in conjunction with band selection to unsupervised hyperspectral image classification.

Paper Details

Date Published: 15 October 2004
PDF: 8 pages
Proc. SPIE 5546, Imaging Spectrometry X, (15 October 2004); doi: 10.1117/12.557129
Show Author Affiliations
Qian Du, Texas A&M Univ. (United States)


Published in SPIE Proceedings Vol. 5546:
Imaging Spectrometry X
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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