Unsupervised Self-Correlated Learning Smoothy Enhanced Locality Preserving Graph Convolution Embedding Clustering for Hyperspectral Images-Reference-Cited by-同舟云学术

Unsupervised Self-Correlated Learning Smoothy Enhanced Locality Preserving Graph Convolution Embedding Clustering for Hyperspectral Images

Published:2022 Issue: Volume:60 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Ding Yao¹^ORCID,Zhang Zhili¹^ORCID,Zhao Xiaofeng¹^ORCID,Cai Wei¹^ORCID,Yang Nengjun¹^ORCID,Hu Haojie¹^ORCID,Huang Xianxiang²,Cao Yuan³^ORCID,Cai Weiwei⁴^ORCID

Affiliation:

1. School of Optical Engineering, Xi’an Research Institute of High Technology, Xi’an, China

2. School of Optical Engineering, Chinese Academy of Engineering (CAE), Xi’an Research Institute of High Technology, Xi’an, China

3. College of Wuhan Humanities and Social Sciences, Wuhan, China

4. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China

Funder

National Natural Science Foundation of China

National Key Basic Research Strengthen Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09869886.pdf?arnumber=9869886

Reference50 articles.

1. Superpixel Contracted Neighborhood Contrastive Subspace Clustering Network for Hyperspectral Images

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3. Deep subspace clustering networks;ji;Proc Adv Neural Inf Process Syst,2017

4. Contrastive Clustering

5. Unsupervised deep embedding for clustering analysis;xie;Proc Int Conf Mach Learn,2016

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