# Clustered Multitask Nonnegative Matrix Factorization for Spectral Unmixing of Hyperspectral Data

## TL;DR

This paper introduces a novel clustered multitask nonnegative matrix factorization algorithm for spectral unmixing in hyperspectral images, utilizing fuzzy c-means clustering and diffusion LMS optimization, showing improved accuracy over existing methods.

## Contribution

The paper presents a new spectral unmixing algorithm based on clustered multitask NMF with fuzzy c-means clustering and diffusion LMS, enhancing unmixing performance.

## Key findings

- Outperforms existing methods in spectral angle distance
- Achieves lower reconstruction error
- Demonstrates effectiveness on synthetic and real datasets

## Abstract

In this paper, the new algorithm based on clustered multitask network is proposed to solve spectral unmixing problem in hyperspectral imagery. In the proposed algorithm, the clustered network is employed. Each pixel in the hyperspectral image considered as a node in this network. The nodes in the network are clustered using the fuzzy c-means clustering method. Diffusion least mean square strategy has been used to optimize the proposed cost function. To evaluate the proposed method, experiments are conducted on synthetic and real datasets. Simulation results based on spectral angle distance, abundance angle distance and reconstruction error metrics illustrate the advantage of the proposed algorithm compared with other methods.