Hyperspectral Classification Using Low Rank and Sparsity Matrices Decomposition-Reference-Cited by-同舟云学术

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Hyperspectral Classification Using Low Rank and Sparsity Matrices Decomposition

Published:2020-09-26 Issue: Volume: Page:
ISSN:
Container-title:IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Cao Hongju¹,Shang Xiaodi²,Yu Chunyan²,Song Meiping²,Chang Chein-I³

Affiliation:

1. School of Software, Dalian University of Foreign Languages,Dalian,China,116044

2. Center for Hyperspectral Imaging in Remote Sensing (CHIRS) Information and Technology College, Dalian Maritime University,Dalian,China,116026

3. University of Maryland Baltimore County,Department of Computer Science and Electrical Engineering,Baltimore,MD,USA,21250

Funder

National Nature Science Foundation of China

Nature Science Foundation of Liaoning Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9323073/9323063/09324009.pdf?arnumber=9324009

Cited by 6 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. Attention and transformer complementary fusion network for hyperspectral image spectral reconstruction;International Journal of Remote Sensing;2024-07-11

2. Hybrid network model based on 3D convolutional neural network and scalable graph convolutional network for hyperspectral image classification;IET Image Processing;2022-09-25

3. The Compression Techniques Applied on Deep Learning Model;Highlights in Science, Engineering and Technology;2022-07-26

4. A Discriminative Spectral-Spatial-Semantic Feature Network Based on Shuffle and Frequency Attention Mechanisms for Hyperspectral Image Classification;Remote Sensing;2022-06-03

5. Combined Spatial-Spectral Hyperspectral Image Classification Based on Adaptive Guided Filtering;Traitement du Signal;2022-04-30

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