Creating and sharing knowledge for telecommunications

Hyperspectral subspace identification

Bioucas-Dias, J. ; Nascimento, J.

IEEE Transactions on Geoscience and Remote Sensing Vol. 46, Nº 8, pp. 2435 - 2445, August, 2008.

ISSN (print): 0196-2892
ISSN (online):

Scimago Journal Ranking: 2,77 (in 2008)

Digital Object Identifier: 10.1109/TGRS.2008.918089

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Abstract
Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection,
change detection, classification, and unmixing. The identification
of this subspace enables a correct dimensionality reduction,
yielding gains in algorithm performance and complexity and in
data storage. This paper introduces a new minimum mean square
error-based approach to infer the signal subspace in hyperspectral
imagery. The method, which is termed hyperspectral signal
identification by minimum error, is eigen decomposition based,
unsupervised, and fully automatic (i.e., 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. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.