A Novel Dual-Alternating Direction Method of Multipliers for Spectral Unmixing

With the remarkable development of spectral unmixing, the sparse-representation-based approaches have emerged as a promising alternative. The sparse-representation-based approaches aim at finding the optimal subset of a spectral library that can optimally model each pixel of a given hyperspectral image in a semisupervised fashion. The classic sparse unmixing models are solved by the prime alternating direction method of multipliers (pADMMs). However, the computation task of pADMM is heavy and time consuming. In this letter, we design a novel dual-alternating direction method of multipliers (dADMMs) for the classic sparse unmixing models. We also present the global convergence analysis of our algorithm in some special cases. As shown in our experiments, the proposed algorithm is more effective than the state-of-the-art algorithms.

Automated summary

This paper introduces a sparse unmixing method for hyperspectral images and proposes a novel dual-alternating direction method of multipliers, showing that the proposed algorithm is more effective than existing algorithms in experiments.