Improving Autoencoders Performance for Hyperspectral Unmixing Using Clustering-Reference-Cited by-同舟云学术

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Improving Autoencoders Performance for Hyperspectral Unmixing Using Clustering

Published:2022 Issue: Volume: Page:102-121
ISSN:1865-0929
Container-title:Recent Challenges in Intelligent Information and Database Systems
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Author:

Grabowski Bartosz^ORCID,Głomb Przemysław^ORCID,Książek Kamil^ORCID,Buza Krisztián^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-8234-7_9

Reference22 articles.

1. Bhatt, J.S., Joshi, M.V.: Deep learning in hyperspectral unmixing: a review. In: IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 2189–2192 (2020). https://doi.org/10.1109/IGARSS39084.2020.9324546

2. Bioucas-Dias, J.M.: A variable splitting augmented Lagrangian approach to linear spectral unmixing. In: 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pp. 1–4 (2009)

3. Bioucas-Dias, J.M., et al.: Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 5(2), 354–379 (2012). https://doi.org/10.1109/JSTARS.2012.2194696

4. Chapelle, O., Schlkopf, B., Zien, A.: Semi-supervised learning. In: IEEE Transactions on Neural Networks, vol. 20 (2006)

5. Dobigeon, N., Moussaoui, S., Coulon, M., Tourneret, J.Y., Hero, A.O.: Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery. IEEE Trans. Signal Process. 57(11), 4355–4368 (2009). https://doi.org/10.1109/TSP.2009.2025797

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. KMNET for Hyperspectral Unmixing;2023 IEEE India Geoscience and Remote Sensing Symposium (InGARSS);2023-12-10

2. IVIU-Net: Implicit Variable Iterative Unrolling Network for Hyperspectral Sparse Unmixing;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2023

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