So far, the problem of unmixing large or multitemporal hyperspectral data sets 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. In this paper, we propose to study the interest of a partially asynchronous distributed unmixing procedure based on a recently proposed asynchronous algorithm.

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... nder standard assumptions, the proposed algorithm inherits its convergence properties from recent contributions in nonconvex 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 paper can be implemented to address various matrix factorization problems. Index Terms-Hyperspectral (HS) unmixing, nonconvex optimization, partially asynchronous distributed estimation. Pierre-Antoine Thouvenin (S'15-M'17) received the State Engineering degree in electrical engineering from ENSEEIHT, Toulouse, France, in 2014, and the M.Sc. and Ph.D. degrees in signal processing from the National Polytechnic Institute of Toulouse, University of Toulouse, Toulouse, in 2014 and 2017, respectively. Since 2017, he has been a Post-Doctoral Research Associate with the School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, Scotland. His research interests include statistical modeling, optimization techniques, and HS unmixing. Nicolas Dobigeon (S'05-M'08-SM'13) received the State Engineering degree in electrical engineering from ENSEEIHT, Toulouse, France, in 2004, and the M.Sc. degree in signal processing, the Ph.D. and Habilitation à Diriger des Recherches degrees in signal processing from the National