CGFINet: Cross-Scale Guided High-Order Feature Interaction Change Detection Network for Remote Sensing Image

Remote sensing image change detection is a valuable technology for analyzing the earth observation data. It has significant application value in resource monitoring, disaster assessment, and urban planning. However, current change detection methods have not fully explored the interrelationships betw...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.14614-14629
Main Authors: Han, Qichao, Zhi, Xiyang, Hu, Jianming, Huang, Yuanxin, Chen, Wenbin, Jiang, Shikai, Gong, Jinnan
Format: Article
Language:English
Subjects:
Change detection
cross-scale guidance
Decoding
feature bidirectional interaction
Feature extraction
Fuses
Image reconstruction
Interference
Remote sensing
Semantics
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Summary:Remote sensing image change detection is a valuable technology for analyzing the earth observation data. It has significant application value in resource monitoring, disaster assessment, and urban planning. However, current change detection methods have not fully explored the interrelationships between bitemporal data, and the extraction process of change information lacks prior guidance and constraints. Therefore, it is easy to produce missed detections and false alarms when facing complex backgrounds and variable objects in remote sensing images. To tackle such issues, we propose a cross-scale guided high-order feature interaction change detection network for dual temporal images. Specifically, a cross-scale guided dual encoder-decoder backbone is proposed to constrain the reconstruction process of change objectives, and guide geometric prior to optimize the representation of target structures. Next, an efficient high-order feature interaction module is designed, employing multilevel receptive fields to enhance the perception ability for multiscale features. Moreover, we construct a bitemporal feature alignment fusion module, which decouples and filters out the interference of background pseudo changes through interactive perception of spatial-temporal differences. Comprehensive experimental validation is undertaken on four representative change detection datasets (LEVIR-CD, WHU-CD, DSIFN-CD, and S2Looking). The findings demonstrate that the network demonstrated state-of-the-art performance.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2024.3434966