Author(s): Bioucas-Dias, José M. ; Nascimento, Jose
Date: 2005
Persistent ID: http://hdl.handle.net/10400.21/6096
Origin: Repositório Científico da Universidade Politécnica de Lisboa
Subject(s): Dimensionality reduction; Model selection; Order selection; Hyperspectral imagery; Linear mixture; Signal subspace
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.
