Papers › Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing
Jie Chen, Min Zhao, Xiuheng Wang, Cédric Richard, Susanto Rahardja
Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing. Conventional physics-based models are characterized by clear interpretation. However they may not be suitable for analyzing scenes with unknown complex physical characteristics. Data-driven methods have developed rapidly in recent years, in particular deep learning methods because they possess superior capability in modeling complex and nonlinear systems. Simply transferring these methods as black-boxes to conduct unmixing may lead to low physical interpretability and generalization ability. This article reviews hyperspectral unmixing works that integrate advantages of both physics-based models and data-driven methods by means of deep neural network structures design, prior design and loss design. Most of these methods derive from a common mathematical optimization framework, and combine good interpretability with high accuracy.
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