Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing

• URL: http://arxiv.org/abs/2310.02340v1
• Date: Tue, 3 Oct 2023 18:21:37 GMT
• Title: Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing
• Authors: Ricardo Augusto Borsoi, Deniz Erdo\u{g}mu\c{s}, Tales Imbiriba
• Abstract summary: We propose a new interpretable deep learning method for hyperspectral unmixing that accounts for nonlinearity and endmember variability. The model is learned end-to-end using backpropagation, and trained using a self-supervised strategy. Experimental results on synthetic and real datasets illustrate the performance of the proposed method.
• Score: 16.02193274044797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although considerable effort has been dedicated to improving the solution to the hyperspectral unmixing problem, non-idealities such as complex radiation scattering and endmember variability negatively impact the performance of most existing algorithms and can be very challenging to address. Recently, deep learning-based frameworks have been explored for hyperspectral umixing due to their flexibility and powerful representation capabilities. However, such techniques either do not address the non-idealities of the unmixing problem, or rely on black-box models which are not interpretable. In this paper, we propose a new interpretable deep learning method for hyperspectral unmixing that accounts for nonlinearity and endmember variability. The proposed method leverages a probabilistic variational deep-learning framework, where disentanglement learning is employed to properly separate the abundances and endmembers. The model is learned end-to-end using stochastic backpropagation, and trained using a self-supervised strategy which leverages benefits from semi-supervised learning techniques. Furthermore, the model is carefully designed to provide a high degree of interpretability. This includes modeling the abundances as a Dirichlet distribution, the endmembers using low-dimensional deep latent variable representations, and using two-stream neural networks composed of additive piecewise-linear/nonlinear components. Experimental results on synthetic and real datasets illustrate the performance of the proposed method compared to state-of-the-art algorithms.

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