Alternating direction method of multipliers for linear hyperspectral unmixing

Yu-Hong Dai (), Fangfang Xu () and Liwei Zhang ()
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Yu-Hong Dai: Chinese Academy of Sciences
Fangfang Xu: Shandong University of Science and Technology
Liwei Zhang: Dalian University of Technology

Mathematical Methods of Operations Research, 2023, vol. 97, issue 3, No 1, 289-310

Abstract: Abstract Linear hyperspectral unmixing (LHU) is a class of important problems in remote sensing. It can be modelled by a linearly constrained convex optimization problem with a coupled objective function. This paper proposes an alternating direction method of multipliers (ADMM) for solving this LHU model. The special structure of the LHU model allows explicit solutions to the subproblems in the ADMM and hence the ADMM is easily implementable. The global convergence of the ADMM is established despite the existence of a coupled term in the objective function. Our numerical experiments with four data sets demonstrated that the proposed ADMM is effective for solving the LHU model.

Keywords: Linear hyperspectral unmixing; Endmembers; Alternating direction method of multipliers; Globally convergence; Coupled objective function; 65K05; 68W20; 90C30 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00186-023-00815-2

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