Spectral Variation Alleviation by Low-Rank Matrix Approximation for Hyperspectral Image Analysis

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number7450142
Pages (from-to)796-800
Journal / PublicationIEEE Geoscience and Remote Sensing Letters
Issue number6
Publication statusPublished - 1 Jun 2016
Externally publishedYes


Spectral variation is profound in remotely sensed images due to variable imaging conditions. The wide presence of such spectral variation degrades the performance of hyperspectral analysis, such as classification and spectral unmixing. In this letter, ℓ1-based low-rank matrix approximation is proposed to alleviate spectral variation for hyperspectral image analysis. Specifically, hyperspectral image data are decomposed into a low-rank matrix and a sparse matrix, and it is assumed that intrinsic spectral features are represented by the low-rank matrix and spectral variation is accommodated by the sparse matrix. As a result, the performance of image data analysis can be improved by working on the low-rank matrix. Experiments on benchmark hyperspectral data sets demonstrate the performance of classification, and spectral unmixing can be clearly improved by the proposed approach.

Research Area(s)

  • Classification, hyperspectral imagery, low-rank matrix approximation, spectral unmixing, Spectral variation