A Deep Disentangled Approach for Interpretable Hyperspectral Unmixing

Deep learning-based frameworks have been recently applied to hyperspectral umixing due to their flexibility and powerful representation capabilities. However, such techniques either use black-box models which are not physically interpretable, or fail to address the non-idealities of the unmixing problem. In this paper, we propose a physically interpretable deep learning method for hyperspectral unmixing accounting for nonlinearity and the variability of the endmembers. The proposed method is based on a probabilistic variational deep learning framework which employs semi-supervised disentanglement learning to properly separate the abundances and endmembers. A self-supervised strategy is used to generate labeled training data, and the model is learned end-to-end using stochastic backpropagation. Experimental results on both synthetic and real datasets illustrate the performance of the proposed method compared to state-of-the-art algorithms.