Cited By
View all- Kumar CDar SBatchu CSingamaneni KPanguluri V(2025)Peer Collaboration in DBLP Using Graph Convolutional NetworkSN Computer Science10.1007/s42979-024-03615-56:1Online publication date: 11-Jan-2025
Data Augmented Graph Convolutional Network for Hyperspectral Image Classification
Authors: 
Chunlan Yang, Dawei XueAuthors Info & Claims
Chunlan Yang, Dawei XueAuthors Info & ClaimsArticle No.: 29, Pages 1 - 6
Abstract
Labeled hyperspectral images (HSIs) data is hard to access, which becomes a great difficulty for the classification task. Graph convolutional networks can efficiently process labeled and unlabeled data via a semi-supervised fashion. To further strengthen the model classification performance, we propose a data augmented graph convolutional network (DAGCN) method. First, we use an efficient graph convolutional network to collect and extract spectral-spatial data. Then, we utilize spatial sample random reset (SSRR) method to extend spectral-spatial data with better use of abundant spatial information. Finally, we adopt the broad learning network to strengthen the width expansion of the data. Experiments prove that DAGCN outperforms the contrast methods.
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Published In
April 2024
373 pages
ISBN:9798400716607
DOI:10.1145/3663976
Copyright © 2024 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 June 2024
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- Research
- Refereed limited
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- Education Department of Anhui Province
Conference
CVIPPR 2024
CVIPPR 2024: 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition
April 26 - 28, 2024
Xiamen, China
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Overall Acceptance Rate 14 of 38 submissions, 37%
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Cited By
View all- Kumar CDar SBatchu CSingamaneni KPanguluri V(2025)Peer Collaboration in DBLP Using Graph Convolutional NetworkSN Computer Science10.1007/s42979-024-03615-56:1Online publication date: 11-Jan-2025
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