Robust Collaborative Nonnegative Matrix Factorization For Hyperspectral Unmixing (R-CoNMF)

TL;DR

This paper presents R-CoNMF, a robust extension of the collaborative nonnegative matrix factorization algorithm, improving hyperspectral unmixing by incorporating volume regularization and a convergent optimization method.

Contribution

R-CoNMF introduces volume regularization and a new PAO algorithm, enhancing robustness and convergence guarantees over prior CoNMF methods.

Findings

Effective endmember estimation with unknown number of endmembers

Improved accuracy in hyperspectral unmixing tasks

Convergence to a critical point is guaranteed

Abstract

The recently introduced collaborative nonnegative matrix factorization (CoNMF) algorithm was conceived to simultaneously estimate the number of endmembers, the mixing matrix, and the fractional abundances from hyperspectral linear mixtures. This paper introduces R-CoNMF, which is a robust version of CoNMF. The robustness has been added by a) including a volume regularizer which penalizes the distance to a mixing matrix inferred by a pure pixel algorithm; and by b) introducing a new proximal alternating optimization (PAO) algorithm for which convergence to a critical point is guaranteed. Our experimental results indicate that R-CoNMF provides effective estimates both when the number of endmembers are unknown and when they are known.