Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework
<|reference_start|>Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework: 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.<|reference_end|>
@article{li2024hyperspectral,
    title={Hyperspectral Unmixing Under Endmember Variability: A Variational
  Inference Framework},
    author={Yuening Li, Xiao Fu, Junbin Liu, and Wing-Kin Ma},
    journal={arXiv preprint arXiv:2407.14899},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14899},
    primaryClass={cs.LG cs.CV}
}