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Proceedings Paper

Estimation of signal subspace on hyperspectral data
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Paper Abstract

Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense. The effectiveness of the proposed method is illustrated using simulated and real hyperspectral images.

Paper Details

Date Published: 19 October 2005
PDF: 8 pages
Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820L (19 October 2005); doi: 10.1117/12.620061
Show Author Affiliations
José M. Bioucas-Dias, Instituto Superior Técnico (Portugal)
Instituto de Telecomunicações (Portugal)
José M. P. Nascimento, Instituto Superior Engenharia de Lisboa (Portugal)
Instituto de Telecomunicações (Portugal)


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

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