S<sup>2</sup>RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images-Reference-Cited by-同舟云学术

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S²RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images

Published:2024-06-30 Issue: Volume:62 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
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Author:

Guan Renxiang¹,Li Zihao¹,Song Chujia²,Yu Guo³,Li Xianju¹,Feng Ruyi¹

Affiliation:

1. China University of Geosciences,School of Computer Science,Wuhan,China,430074

2. China University of Geosciences,School of Geography and Information Engineering,Changsha,China,430073

3. Wuhan University of Science and Technology,School of Management,Wuhan,China

Funder

Ministry of Education

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650629.pdf?arnumber=10650629

Reference60 articles.

1. Classifying urban land use by integrating remote sensing and social media data

2. C²-YOLO: Rotating Object Detection Network for Remote Sensing Images with Complex Backgrounds

3. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

4. SPIRIT: Spectral Awareness Interaction Network With Dynamic Template for Hyperspectral Object Tracking

5. Faithful vision-language interpretation via concept bottleneck models;Lai

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