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Advisor(s)
Abstract(s)
Given an hyperspectral image, the determination of the number of endmembers and the subspace where they live without any prior knowledge is crucial to the success of hyperspectral image analysis.
This paper introduces a new minimum mean squared error based approach to infer the signal subspace in hyperspectral imagery. The method, termed hyperspectral signal identification by minimum error (HySime), is eigendecomposition based and it does not depend on any tuning parameters. It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.
Description
Keywords
Hyperspectral signal Model
Citation
NASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - Hyperspectral signal subspace estimation. IGARSS: 2007 IEEE International Geoscience and Remote Sensing Symposium, Vols 1-12: Sensing And Understanding Our Planet. ISSN 2153-6996. Vol. 1-12. 3225-3228, 2007
Publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.