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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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing Vol. 18; pp. 28257 - 28268
Main Authors: Chang, Zhanyuan, Wei, Yuwen, Lian, Jie, Jin, Mingxiao, Wang, Dong, Li, Xuyang
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2025.3626434