Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing: A summary of current methods

Spectral unmixing is central when analyzing hyperspectral data. To accomplish this task, physics-based methods have become popular because, with their explicit mixing models, they can provide a clear interpretation. Nevertheless, because of their limited modeling capabilities, especially when analyzing real scenes with unknown complex physical properties, these methods may not be accurate. On the other hand, data-driven methods using deep learning in particular have developed rapidly in recent years, thanks to their superior capability in modeling complex nonlinear systems. Simply transferring these methods as black boxes to perform unmixing may lead to low interpretability and poor generalization ability. To bring together the best of two worlds, recent research efforts have focused on combining the advantages of both physics-based models and data-driven methods. In this article, we present an overview of recent advances on this topic from various perspectives, including deep neural network (DNN) design, prior capturing, and loss selection. We summarize these methods within a common optimization framework and discuss ways of enhancing our understanding of these methods. The related source codes are made publicly available at http://github.com/xiuheng-wang/awesome-hyperspectral-image-unmixing.