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A Novel Hyperspectral Unmixing Method with K-Means and VAE Based Network Structure

Published: 15 March 2023 Publication History

Abstract

Since the existing blind hyperspectral unmixing methods based on deep learning (DL) are sensitive to spectral variability and noise, a network architecture based on the Variational AutoEncoder is proposed. The proposed algorithm first clusters the input HSI data and transports the clustered data into the VAE network to overcome the impact of spectral variability. At the same time, the hidden variable layer of VAE is used for pixel recovery, so that the network has a good ability to resist noise. Simulation data and real data experiments show that compared with the state-of-the-art DL algorithms, the proposed algorithm has a better unmixing performance.

References

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Shaoquan Zhang; Guorong Zhang; Fan Li; Chengzhi Deng; Shengqian Wang; Antonio Plaza; Jun Li;. Spectral-Spatial Hyperspectral Unmixing Using Nonnegative Matrix Factorization. IEEE Transactions on Geoscience and Remote Sensing, 2022.
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Cited By

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  • (2024)Conditional Variational Autoencoders with Fuzzy InferenceProceedings of the Eighth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’24), Volume 210.1007/978-3-031-77411-9_9(91-103)Online publication date: 20-Dec-2024
  • (2023)KMNET for Hyperspectral Unmixing2023 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)10.1109/InGARSS59135.2023.10490373(1-4)Online publication date: 10-Dec-2023

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cover image ACM Other conferences
EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering
October 2022
1999 pages
ISBN:9781450397148
DOI:10.1145/3573428
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 March 2023

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View all
  • (2024)Conditional Variational Autoencoders with Fuzzy InferenceProceedings of the Eighth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’24), Volume 210.1007/978-3-031-77411-9_9(91-103)Online publication date: 20-Dec-2024
  • (2023)KMNET for Hyperspectral Unmixing2023 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)10.1109/InGARSS59135.2023.10490373(1-4)Online publication date: 10-Dec-2023

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