Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection-Reference-Cited by-同舟云学术

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Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection

Published:2023 Issue: Volume:11 Page:129709-129721
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ning Shangfeng¹,Wang Wenhong¹^ORCID

Affiliation:

1. College of Computer Science, Liaocheng University, Liaocheng, China

Funder

Discipline with Strong Characteristics of Liaocheng University–Intelligent Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10287968.pdf?arnumber=10287968

Reference46 articles.

1. Progressive Band Selection of Spectral Unmixing for Hyperspectral Imagery

2. Band Selection of Hyperspectral Images Using Attention-Based Autoencoders

3. A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification

4. Correlation-Guided Ensemble Clustering for Hyperspectral Band Selection

5. Novel Gumbel-Softmax Trick Enabled Concrete Autoencoder With Entropy Constraints for Unsupervised Hyperspectral Band Selection

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