Estimating the Intrinsic Dimension of Hyperspectral Images Using a Noise-Wh itened Eigengap Approach

Abderrahim Halimi, Paul Honeine, Malika Kharouf, Cédric Richard, Jean-Yves Tourneret

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Linear mixture models are commonly used to represent a hyperspectral data cube as linear combinations of endmember spectra. However, determining the number of endmembers for images embedded in noise is a crucial task. This paper proposes a fully automatic approach for estimating the number of endmembers in hyperspectral images. The estimation is based on recent results of random matrix theory related to the so-called spiked population model. More precisely, we study the gap between successive eigenvalues of the sample covariance matrix constructed from high-dimensional noisy samples. The resulting estimation strategy is fully automatic and robust to correlated noise owing to the consideration of a noise-whitening step. This strategy is validated on both synthetic and real images. The experimental results are very promising and show the accuracy of this algorithm with respect to state-of-the-art algorithms.
Original languageEnglish
Pages (from-to)3811-3821
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume54
Issue number7
Early online date3 Mar 2016
DOIs
Publication statusPublished - Jul 2016

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