Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image Classification
Graph Neural Networks (GNNs) have emerged as a promising solution for few-shot hyperspectral image (HSI) classification. However, existing GNN-based approaches face critical limitations in three key aspects: 1) suboptimal graph topology construction due to fixed or heuristic-based edge definitions,...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11003994/ |
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| author | Zhaoxia Xue Zhiwei Liu Zhaohui Xue Tingqiang Song |
| author_facet | Zhaoxia Xue Zhiwei Liu Zhaohui Xue Tingqiang Song |
| author_sort | Zhaoxia Xue |
| collection | DOAJ |
| description | Graph Neural Networks (GNNs) have emerged as a promising solution for few-shot hyperspectral image (HSI) classification. However, existing GNN-based approaches face critical limitations in three key aspects: 1) suboptimal graph topology construction due to fixed or heuristic-based edge definitions, 2) inefficient propagation of discriminative node features across heterogeneous regions, and 3) inadequate fusion of spatially correlated and spectrally discriminative patterns. To overcome these challenges, we propose a Spatial-Spectral Contrastive Graph Neural Network (SSCGNN), which introduces three novel components. First, we propose a directed graph reconstruction module that transforms conventional undirected graphs into diversity-aware topology through multi-head self-attention mechanisms. Second, we design a collaborative learning framework where the spatial graph network and spectral graph network mutually enhance feature extraction. Finally, we construct a graph contrastive learning module that augments edge representation learning under limited supervision. By constructing positive/negative edge pairs through spectral-spatial similarity metrics, it regularizes the graph structure to emphasize class-discriminative connections and suppress noisy inter-node relationships. Extensive experiments on four benchmark HSI datasets—University of Pavia (PU), Salinas Valley (SA), WHU-Hi-HanChuan (WHU-HH), and WHU-Hi-LongKou (WHU-HL)—demonstrate SSCGNN’s superiority over state-of-the-art few-shot learning methods. With only 5 labeled samples per class, our model achieves overall accuracy (OA) values of: 93.36% (PU, +6.22% vs. baseline GCN), 98.37% (SA, +4.91% vs. spectral-spatial CNN), 84.68% (WHU-HH, +8.03% vs. meta-learning approaches), 95.87% (WHU-HL, +7.15% vs. graph attention networks). |
| format | Article |
| id | doaj-art-4592c327e9534b9c81bcac623b834ed1 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4592c327e9534b9c81bcac623b834ed12025-08-20T01:56:48ZengIEEEIEEE Access2169-35362025-01-0113882788829010.1109/ACCESS.2025.356987411003994Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image ClassificationZhaoxia Xue0Zhiwei Liu1Zhaohui Xue2https://orcid.org/0000-0001-6253-2967Tingqiang Song3School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, ChinaiFLYTEK Company Ltd., Hefei, ChinaCollege of Geography and Remote Sensing, Hohai University, Nanjing, ChinaSchool of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, ChinaGraph Neural Networks (GNNs) have emerged as a promising solution for few-shot hyperspectral image (HSI) classification. However, existing GNN-based approaches face critical limitations in three key aspects: 1) suboptimal graph topology construction due to fixed or heuristic-based edge definitions, 2) inefficient propagation of discriminative node features across heterogeneous regions, and 3) inadequate fusion of spatially correlated and spectrally discriminative patterns. To overcome these challenges, we propose a Spatial-Spectral Contrastive Graph Neural Network (SSCGNN), which introduces three novel components. First, we propose a directed graph reconstruction module that transforms conventional undirected graphs into diversity-aware topology through multi-head self-attention mechanisms. Second, we design a collaborative learning framework where the spatial graph network and spectral graph network mutually enhance feature extraction. Finally, we construct a graph contrastive learning module that augments edge representation learning under limited supervision. By constructing positive/negative edge pairs through spectral-spatial similarity metrics, it regularizes the graph structure to emphasize class-discriminative connections and suppress noisy inter-node relationships. Extensive experiments on four benchmark HSI datasets—University of Pavia (PU), Salinas Valley (SA), WHU-Hi-HanChuan (WHU-HH), and WHU-Hi-LongKou (WHU-HL)—demonstrate SSCGNN’s superiority over state-of-the-art few-shot learning methods. With only 5 labeled samples per class, our model achieves overall accuracy (OA) values of: 93.36% (PU, +6.22% vs. baseline GCN), 98.37% (SA, +4.91% vs. spectral-spatial CNN), 84.68% (WHU-HH, +8.03% vs. meta-learning approaches), 95.87% (WHU-HL, +7.15% vs. graph attention networks).https://ieeexplore.ieee.org/document/11003994/Hyperspectral image (HSI)classificationcontrastive learninggraph neural networks (GNNs)few-shot learning |
| spellingShingle | Zhaoxia Xue Zhiwei Liu Zhaohui Xue Tingqiang Song Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image Classification IEEE Access Hyperspectral image (HSI) classification contrastive learning graph neural networks (GNNs) few-shot learning |
| title | Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image Classification |
| title_full | Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image Classification |
| title_fullStr | Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image Classification |
| title_full_unstemmed | Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image Classification |
| title_short | Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image Classification |
| title_sort | spatial spectral contrastive graph neural network for few shot hyperspectral image classification |
| topic | Hyperspectral image (HSI) classification contrastive learning graph neural networks (GNNs) few-shot learning |
| url | https://ieeexplore.ieee.org/document/11003994/ |
| work_keys_str_mv | AT zhaoxiaxue spatialspectralcontrastivegraphneuralnetworkforfewshothyperspectralimageclassification AT zhiweiliu spatialspectralcontrastivegraphneuralnetworkforfewshothyperspectralimageclassification AT zhaohuixue spatialspectralcontrastivegraphneuralnetworkforfewshothyperspectralimageclassification AT tingqiangsong spatialspectralcontrastivegraphneuralnetworkforfewshothyperspectralimageclassification |