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

TL;DR

This paper introduces an automatic, noise-robust method for estimating the number of endmembers in hyperspectral images using eigen-gap analysis based on random matrix theory, validated on synthetic and real data.

Contribution

It presents a novel eigen-gap based approach leveraging random matrix theory for automatic endmember estimation in noisy hyperspectral data.

Findings

Accurately estimates endmembers in noisy hyperspectral images

Outperforms existing algorithms in experimental validation

Robust to correlated noise in high-dimensional data

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.