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An ADMM Based Network for Hyperspectral Unmixing Tasks

Zhou, Chao; Rodrigues, Miguel RD; (2021) An ADMM Based Network for Hyperspectral Unmixing Tasks. In: 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (pp. pp. 1870-1874). IEEE: Toronto, ON, Canada. Green open access

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Abstract

In this paper, we use algorithm unrolling approaches in order to design a new neural network structure applicable to hyperspectral unmixing challenges. In particular, building upon a constrained sparse regression formulation of the underlying unmixing problem, we unroll an ADMM solver onto a neural network architecture that can be used to deliver the abundances of different (known) endmembers given a reflectance spectrum. Our proposed network – which can be readily trained using standard supervised learning procedures – is shown to possess a richer structure consisting of various skip connections and shortcuts than other competing architectures. Moreover, our proposed network also delivers state-of-the-art unmixing performance compared to competing methods.

Type: Proceedings paper
Title: An ADMM Based Network for Hyperspectral Unmixing Tasks
Event: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Location: ELECTR NETWORK
Dates: 6 Jun 2021 - 11 Jun 2021
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICASSP39728.2021.9414555
Publisher version: https://doi.org/10.1109/ICASSP39728.2021.9414555
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Technology, Acoustics, Computer Science, Artificial Intelligence, Computer Science, Software Engineering, Engineering, Electrical & Electronic, Imaging Science & Photographic Technology, Computer Science, Engineering, HSI unmixing, Deep Neural Networks, Algorithm Unrolling, Algorithm Unfolding
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10150848
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