Distributed Unmixing of Hyperspectral Data With Sparsity Constraint

TL;DR

This paper introduces a distributed optimization algorithm for hyperspectral spectral unmixing that incorporates sparsity constraints, improving accuracy over existing methods by leveraging a network of single-node clusters and diffusion LMS strategy.

Contribution

A novel distributed unmixing algorithm using diffusion LMS with sparsity constraints for hyperspectral data, enhancing unmixing accuracy and robustness.

Findings

Improved AAD and SAD metrics by about 6% and 27% respectively.

Demonstrated advantages over other spectral unmixing methods.

Effective in high SNR conditions (25dB).

Abstract

Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which was added to NMF is sparsity constraint that was regularized by L 1/2 norm. In this paper, a new algorithm based on distributed optimization has been used for spectral unmixing. In the proposed algorithm, a network including single-node clusters has been employed. Each pixel in hyperspectral images considered as a node in this network. The distributed unmixing with sparsity constraint has been optimized with diffusion LMS strategy, and then the update equations for fractional abundance and signature matrices are obtained. Simulation results based on defined performance metrics, illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods. The results show that the AAD and SAD of the proposed approach are improved respectively about 6 and 27 percent toward distributed unmixing in SNR=25dB.