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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