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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 3
937-943

Joint spatial-temporal differential attention and hierarchical detail enhancement for remote sensing image change detection

Guan Zongsheng1a,1b
Shao Pan1a,1b
Yang Zichao2
Cheng Zemin1a,1b
Yu Kuai1a,1b
1. a. Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, b. College of Computer & Information Technology, China Three Gorges University, Yichang Hubei 443002, China
2. Advanced Copper Industry, Jiangxi University of Science & Technology, Yingtan Jiangxi 335000, China
DOI: 10.19734/j.issn.1001-3695.2024.05.0218

Abstract

Currently, deep learning remote sensing image change detection methods based on U-Net contain many pseudo-change information, and most of them lack effective interaction between layer-level features. Aiming at the above problems, based on the classical U-Net network, this paper proposed a joint spatial-temporal differential attention and hierarchical detail enhancement method for high-resolution remote sensing image change detection. Firstly, this paper extracted the single time-phase features and cascade features of the two periods of images respectively, and based on the Euclidean distance and difference features of the single time-phase features of the two periods, it proposed a spatial-temporal differential attention module to strengthen the learning of cascade features to the changing region. Secondly, using hybrid spatial channel attention to interact information between adjacent hierarchical features to construct a hierarchical detail enhancement module that facilitated feature decoding. Finally, this paper designed a lightweight multi-scale boundary refinement module to extract multi-scale features and mitigate the loss of boundary information by combining the chunking strategy and atrous strip convolution. Experimental results on four commonly used public datasets show that the method achieves the best evaluation metrics compared to eight existing change detection networks.

Foundation Support

国家自然科学基金资助项目(41901341,42101469)
湖北省自然科学基金资助项目(2024AFB867)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.05.0218
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 3
Section: Technology of Graphic & Image
Pages: 937-943
Serial Number: 1001-3695(2025)03-038-0937-07

Publish History

[2025-03-05] Printed Article

Cite This Article

管宗胜, 邵攀, 杨子超, 等. 联合时空差异注意力与层级细节增强的遥感影像变化检测 [J]. 计算机应用研究, 2025, 42 (3): 937-943. (Guan Zongsheng, Shao Pan, Yang Zichao, et al. Joint spatial-temporal differential attention and hierarchical detail enhancement for remote sensing image change detection [J]. Application Research of Computers, 2025, 42 (3): 937-943. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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