Research Progress of Transformer-based Remote Sensing Image Change Detection

ZHUO Li; YU Wanting; JIA Tongyao; LI Jiafeng

doi:10.11936/bjutxb2024010034

Journal of Beijing University of Technology > 2025 > > DOI: 10.11936/bjutxb2024010034

ZHUO Li, YU Wanting, JIA Tongyao, LI Jiafeng. Research Progress of Transformer-based Remote Sensing Image Change Detection[J]. Journal of Beijing University of Technology. DOI: 10.11936/bjutxb2024010034

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Research Progress of Transformer-based Remote Sensing Image Change Detection

ZHUO Li^1,2,
YU Wanting^1,2, ,,
JIA Tongyao^1,2,
LI Jiafeng^1,2

1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China

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Received Date: January 30, 2024
Revised Date: March 29, 2024
Available Online: May 15, 2025

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Abstract

Abstract

Due to complex factors, including illumination, seasons, phenology, solar height and angle changes, as well as the scattered and diverse nature of the target areas to be detected, along with the variability in scale and direction, remote sensing image change detection enfaces great technical challenges. In recent years, Transformer has demonstrated remarkable success in various fields such as natural language processing, object detection, and image segmentation, becoming a research focus. This paper reviews the latest research progress of Transformer-based remote sensing image change detection, and analyzes two types of methods based on pure transformer and convolutional neural Network (CNN) + Transformer. Then, a comparative analysis of the detection performance of different methods on public datasets is conducted, highliting the respective merits and limitations of various methods. Finally, future possible development trends are discussed.
- Transformer,
- remote sensing image,
- change detection,
- pure Transformer,
- convolutional neural network (CNN),
- hybrid architecture

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References

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