Acta Optica Sinica, Volume. 45, Issue 6, 0628008(2025)

Dense Hybrid Attention Network for Remote Sensing Building Change Detection

Qinglin Tian1,*... Donghua Lu1, Yao Li2 and Chengkai Pei1 |Show fewer author(s)
Author Affiliations
  • 1National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, Beijing Research Institute of Uranium Geology, Beijing 100029, China
  • 2School of Geographical Sciences, Southwest University, Chongqing 400715, China
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    References(35)

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    [18] Wang C, Wang S, Chen X et al. Object-level change detection of multi-sensor optical remote sensing images combined with UNet++ and multi-level difference module[J]. Acta Geodaetica et Cartographica Sinica, 52, 283-296(2023).

    [19] Jiang M, Zhang X C, Sun Y et al. Full-scale feature aggregation network for high-resolution remote sensing image change detection[J]. Acta Geodaetica et Cartographica Sinica, 52, 1738-1748(2023).

    [22] Zhang J B, Yan Z X, Ma S F. Multi-scale cross dual attention network for building change detection in remote sensing images[J]. Journal of Geo-Information Science, 25, 2487-2500(2023).

    [25] Liu Y, Guo H T, Lu J et al. Remote sensing image change detection method based on adaptive boundary sensing[J]. Acta Optica Sinica, 44, 1828001(2024).

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    Qinglin Tian, Donghua Lu, Yao Li, Chengkai Pei. Dense Hybrid Attention Network for Remote Sensing Building Change Detection[J]. Acta Optica Sinica, 2025, 45(6): 0628008

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Aug. 16, 2024

    Accepted: Sep. 30, 2024

    Published Online: Mar. 17, 2025

    The Author Email: Tian Qinglin (736924158@qq.com)

    DOI:10.3788/AOS241436

    CSTR:32393.14.AOS241436

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