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Journal Articles IEEE Transactions on Geoscience and Remote Sensing Year : 2016

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

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.
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Dates and versions

hal-01325467 , version 1 (02-06-2016)

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Abderrahim Halimi, Paul Honeine, Malika Kharouf, Cédric Richard, Jean-Yves Tourneret. Estimating the Intrinsic Dimension of Hyperspectral Images Using a Noise-Whitened Eigengap Approach. IEEE Transactions on Geoscience and Remote Sensing, 2016, vol. 54 (n° 7), pp.3811-3821. ⟨10.1109/TGRS.2016.2528298⟩. ⟨hal-01325467⟩
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