Creating and sharing knowledge for telecommunications

Hyperspectral Signal Subspace Estimation

Nascimento, J. ; Bioucas-Dias, J.

Hyperspectral Signal Subspace Estimation, Proc IEEE International Geoscience and Remote Sensing Symp.- IGARSS, Barcelona, Spain, Vol. -, pp. 3225 - 3228, July, 2007.

Digital Object Identifier: 10.1109/IGARSS.2007.4423531

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.