Estimating the Intrinsic Dimension of Hyperspectral Images Using an Eigen-Gap Approach

Abstract

Linear mixture models are commonly used to represent hyperspectral datacube as a linear combinations of endmember spectra. However, determining of 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 unsupervised and robust to correlated noise. 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.

21 pages, 4 figures and 4 tables

References in corpus (2)

• Blind and fully constrained unmixing of hyperspectral images
• Estimating the Intrinsic Dimension of Hyperspectral Images Using an Eigen-Gap Approach

Cited by in corpus (6)

• Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
• Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity or Mismodelling Effects
• Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability
• Estimating the Intrinsic Dimension of Hyperspectral Images Using an Eigen-Gap Approach
• Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images
• Spectral unmixing for exoplanet direct detection in hyperspectral data