Partially Asynchronous Distributed Unmixing of Hyperspectral Images

TL;DR

This paper introduces a partially asynchronous distributed unmixing algorithm for hyperspectral images, improving efficiency and convergence in large-scale remote sensing data analysis.

Contribution

It presents a novel asynchronous unmixing method based on recent non-convex optimization algorithms, with proven convergence and applicability to various matrix factorization problems.

Findings

The asynchronous method converges under standard assumptions.

It outperforms synchronous approaches on synthetic and real data.

The approach is flexible for different matrix factorization tasks.

Abstract

So far, the problem of unmixing large or multitemporal hyperspectral datasets has been specifically addressed in the remote sensing literature only by a few dedicated strategies. Among them, some attempts have been made within a distributed estimation framework, in particular relying on the alternating direction method of multipliers (ADMM). In this paper, we propose to study the interest of a partially asynchronous distributed unmixing procedure based on a recently proposed asynchronous algorithm. Under standard assumptions, the proposed algorithm inherits its convergence properties from recent contributions in non-convex optimization, while allowing the problem of interest to be efficiently addressed. Comparisons with a distributed synchronous counterpart of the proposed unmixing procedure allow its interest to be assessed on synthetic and real data. Besides, thanks to its genericity and flexibility, the procedure investigated in this work can be implemented to address various matrix factorization problems.