# Hyperspectral Unmixing Based on Clustered Multitask Networks

## TL;DR

This paper introduces a novel hyperspectral unmixing algorithm that clusters images and employs a distributed optimization network, improving accuracy over existing methods.

## Contribution

It proposes a new clustering-based, distributed optimization approach for spectral unmixing in hyperspectral images, integrating fuzzy c-means and diffusion LMS strategies.

## Key findings

- Enhanced unmixing accuracy demonstrated in simulations
- Outperforms traditional NMF-based methods
- Effective clustering improves spectral signature estimation

## Abstract

Hyperspectral remote sensing is a prominent research topic in data processing. Most of the spectral unmixing algorithms are developed by adopting the linear mixing models. Nonnegative matrix factorization (NMF) and its developments are used widely for estimation of signatures and fractional abundances in the SU problem. Sparsity constraints was added to NMF, and was regularized by $ L_ {q} $ norm. In this paper, at first hyperspectral images are clustered by fuzzy c- means method, and then a new algorithm based on sparsity constrained distributed optimization is used for spectral unmixing. In the proposed algorithm, a network including clusters is employed. Each pixel in the hyperspectral images considered as a node in this network. The proposed algorithm is 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.