Local Correntropy Matrix Representation for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

Local Correntropy Matrix Representation for Hyperspectral Image Classification

Published:2022 Issue: Volume:60 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zhang Xinyu¹^ORCID,Wei Yantao¹^ORCID,Cao Weijia²^ORCID,Yao Huang¹,Peng Jiangtao³,Zhou Yicong⁴^ORCID

Affiliation:

1. Hubei Research Center for Educational Informationization, Faulty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China

2. Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China

3. Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan, Hubei, China

4. Faculty of Science and Technology, University of Macau, Macau, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province

Self-Determined Research Funds of Central China Normal University (CCNU) From the Colleges’ Basic Research and Operation of Ministry of Education

National Key Research and Development Program 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/ielx7/36/9633014/09740677.pdf?arnumber=9740677

Reference62 articles.

1. Neighboring Region Dropout for Hyperspectral Image Classification

2. Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

3. Local Linear Spatial–Spectral Probabilistic Distribution for Hyperspectral Image Classification

4. Dimensionality Reduction of Hyperspectral Imagery Based on Spatial–Spectral Manifold Learning

5. Multiscale Superpixel Kernel-Based Low-Rank Representation for Hyperspectral Image Classification

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