Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework

• URL: http://arxiv.org/abs/2407.14899v1
• Date: Sat, 20 Jul 2024 15:16:14 GMT
• Title: Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework
• Authors: Yuening Li, Xiao Fu, Junbin Liu, Wing-Kin Ma,
• Abstract summary: This work proposes a variational inference framework for hyperspectral unmixing in the presence of endmember variability (HU-EV) An EV-accounted noisy linear mixture model (LMM) is considered, and the presence of outliers is also incorporated into the model. The effectiveness of the proposed framework is demonstrated through synthetic, semi-real, and real-data experiments.
• Score: 22.114121550108344
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This work proposes a variational inference (VI) framework for hyperspectral unmixing in the presence of endmember variability (HU-EV). An EV-accounted noisy linear mixture model (LMM) is considered, and the presence of outliers is also incorporated into the model. Following the marginalized maximum likelihood (MML) principle, a VI algorithmic structure is designed for probabilistic inference for HU-EV. Specifically, a patch-wise static endmember assumption is employed to exploit spatial smoothness and to try to overcome the ill-posed nature of the HU-EV problem. The design facilitates lightweight, continuous optimization-based updates under a variety of endmember priors. Some of the priors, such as the Beta prior, were previously used under computationally heavy, sampling-based probabilistic HU-EV methods. The effectiveness of the proposed framework is demonstrated through synthetic, semi-real, and real-data experiments.

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