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Advisor(s)
Abstract(s)
Hyperspectral applications in remote sensing are often focused on determining the so-called spectral signatures, i.e., the reflectances of materials present in the scene (endmembers) and the corresponding abundance fractions at each pixel in a spatial area of interest. The determination of the number of endmembers in a scene without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper proposes a new mean squared error 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.
Description
Chpater in Book Proceedings with Peer Review Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Proceedings, Part II
Keywords
Pattern Recognition Image Science Hyperspectral
Citation
NASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - Signal Subspace Identification in Hyperspectral Linear Mixtures. Pattern Recognition and Image Analysis. Vol. 3523, nr. 2 (2005), p. 207-214
Publisher
Springer Berlin Heidelberg