Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework
This is an incremental improvement for remote sensing and image analysis, addressing computational challenges in existing probabilistic methods.
The paper tackles hyperspectral unmixing with endmember variability by proposing a variational inference framework that incorporates spatial smoothness and outlier handling, demonstrating effectiveness on synthetic, semi-real, and real data.
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.