SSTGCNs: Spectral-Spatial-Temporal Long-Range Dependencies Joint Feature Extraction With Graph Convolutional Networks for Adaptive Change Detection

Hyperspectral image change detection plays an important role in ground observation tasks, making full use of the rich spectral and spatial information in the bitemporal hyperspectral to identify subtle changes in the surface. Currently, most methods are extracting spatial-spectral features, ignoring...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 18; S. 28257 - 28268
Hauptverfasser:	Chang, Zhanyuan, Wei, Yuwen, Lian, Jie, Jin, Mingxiao, Wang, Dong, Li, Xuyang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Adaptation models Adaptively fused features Artificial neural networks Change detection Computational modeling Correlation Data mining Data models Datasets Distance Feature extraction Hyperspectral imaging long-range dependencies Modules Osteoarthritis Pixels Redundancy Spatial data Spatial dependencies spectral–spatial–temporal features Temporal variations Transformers
ISSN:	1939-1404, 2151-1535
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Abstract	Hyperspectral image change detection plays an important role in ground observation tasks, making full use of the rich spectral and spatial information in the bitemporal hyperspectral to identify subtle changes in the surface. Currently, most methods are extracting spatial-spectral features, ignoring the interaction between bitemporal images. In addition, there is a long-distance dependence between the spectrum and pixels of hyperspectral images. How to capture the dependence between long-distance bands and pixels is a problem that needs to be solved at present. To solve the above problems, this article proposes spectral-spatial-temporal long-range dependencies joint feature extraction with graph convolutional networks. First, the network uses spectral-spatial-temporal feature extraction module to capture spectral-spatial-temporal features and increase the interactive features of bitime phase images. Second, a long-distance dependence capture module is designed to capture the long-distance dependence between bands and pixels through the long-range spectral-spatial dependence module. Finally, through redundancy suppression and adaptive feature fusion, redundant features are removed and high-information features are fused to improve the model's feature expression ability and generalization ability. To verify the effectiveness of the model, we conducted experiments on three public Hyperspectral Change Detection datasets. The experimental results showed that the overall accuracy (OA) and Kappa coefficient (Kappa) indicators of our model on the USA data were improved by 0.55% and 1.44%, respectively, compared with the latest methods. On the China dataset, the proposed method compared the latest AIWSEN, OA, and Kappa to obtain suboptimal results, while the precision (P), recall (R), and F1-scores achieved the optimal results, which were 2.59%, 0.89%, and 1.14% higher than the state-of-the-art method, respectively. The OA, P, R, and F1 indicators on the River dataset achieve optimal results: 0.36%, 1.32%, 0.5%, and 0.91% improvements, respectively, over the state-of-the-art methods. Explain that our change detection method works better for spectral data containing more information.
AbstractList	Hyperspectral image change detection plays an important role in ground observation tasks, making full use of the rich spectral and spatial information in the bitemporal hyperspectral to identify subtle changes in the surface. Currently, most methods are extracting spatial–spectral features, ignoring the interaction between bitemporal images. In addition, there is a long-distance dependence between the spectrum and pixels of hyperspectral images. How to capture the dependence between long-distance bands and pixels is a problem that needs to be solved at present. To solve the above problems, this article proposes spectral–spatial–temporal long-range dependencies joint feature extraction with graph convolutional networks. First, the network uses spectral–spatial–temporal feature extraction module to capture spectral–spatial–temporal features and increase the interactive features of bitime phase images. Second, a long-distance dependence capture module is designed to capture the long-distance dependence between bands and pixels through the long-range spectral–spatial dependence module. Finally, through redundancy suppression and adaptive feature fusion, redundant features are removed and high-information features are fused to improve the model’s feature expression ability and generalization ability. To verify the effectiveness of the model, we conducted experiments on three public Hyperspectral Change Detection datasets. The experimental results showed that the overall accuracy (OA) and Kappa coefficient (Kappa) indicators of our model on the USA data were improved by 0.55% and 1.44%, respectively, compared with the latest methods. On the China dataset, the proposed method compared the latest AIWSEN, OA, and Kappa to obtain suboptimal results, while the precision (P), recall (R), and F1-scores achieved the optimal results, which were 2.59%, 0.89%, and 1.14% higher than the state-of-the-art method, respectively. The OA, P, R, and F1 indicators on the River dataset achieve optimal results: 0.36%, 1.32%, 0.5%, and 0.91% improvements, respectively, over the state-of-the-art methods. Explain that our change detection method works better for spectral data containing more information.
Author	Lian, Jie Li, Xuyang Wang, Dong Chang, Zhanyuan Wei, Yuwen Jin, Mingxiao
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SubjectTerms	Accuracy Adaptation models Adaptively fused features Artificial neural networks Change detection Computational modeling Correlation Data mining Data models Datasets Distance Feature extraction Hyperspectral imaging long-range dependencies Modules Osteoarthritis Pixels Redundancy Spatial data Spatial dependencies spectral–spatial–temporal features Temporal variations Transformers
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Title	SSTGCNs: Spectral-Spatial-Temporal Long-Range Dependencies Joint Feature Extraction With Graph Convolutional Networks for Adaptive Change Detection
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