Remote Sensing Technology and Application, Volume. 40, Issue 4, 783(2025)

Research Progress of Remote Sensing Change Detection based on Deep Learning: Pixel-level, Object-level, and Scene-level

Peijun DU1,2,3、*, Hong FANG1,2,3, Shanchuan GUO1,2,3, Chenghan YANG1,2,3, and Pengfei TANG1,2,3
Author Affiliations
  • 1School of Geography and Ocean Science, Nanjing University, Nanjing210023, China
  • 2Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing210023, China
  • 3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing210023, China
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    Figures & Tables(10)
    Flowchart of pixel-level binary change detection without manually labeled samples[50]
    QuickBird Images[50]
    Pixel-level binary change detection results of different methods[50]
    Flowchart of object-level change detection[14]
    Aerial orthophotos[14]
    Local results of pixel-level and object-level change detection maps[14]
    Pixel-level and object-level change detection accuracies for different fusion methods[14]
    Flowchart of scene-level binary change detection without manually labeled samples[18]
    Scene-level change detection dataset[18]
    Scene-level binary change detection results of different methods[18]
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    Peijun DU, Hong FANG, Shanchuan GUO, Chenghan YANG, Pengfei TANG. Research Progress of Remote Sensing Change Detection based on Deep Learning: Pixel-level, Object-level, and Scene-level[J]. Remote Sensing Technology and Application, 2025, 40(4): 783

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    Paper Information

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    Received: Jan. 2, 2025

    Accepted: --

    Published Online: Aug. 26, 2025

    The Author Email: Peijun DU (peijun@nju.edu.cn)

    DOI:10.11873/j.issn.1004-0323.2025.4.0783

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