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Communication Dans Un Congrès Année : 2023

A deep disentangled approach for interpretable hyperspectral unmixing

Résumé

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.
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Dates et versions

hal-04135343 , version 1 (20-06-2023)

Identifiants

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Ricardo Augusto Borsoi, Tales Imbiriba, Deniz Erdoğmuş. A deep disentangled approach for interpretable hyperspectral unmixing. International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, Jun 2023, Rhodes Island, Greece. ⟨10.1109/ICASSP49357.2023.10095764⟩. ⟨hal-04135343⟩
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