Attention-based Change Detection

Attention-Based Change Detection is a change detection method that employs attention mechanisms to selectively focus on informative regions or features in multi-temporal data, thereby enhancing the model's ability to identify meaningful changes while suppressing irrelevant variations.

188

Publications

19K

Citations

608

Authors

224

Institutions

Publications per year

2017–2026

106

Authors

608

  1. MX
    Min Xia

    Nanjing University of Information Science and Technology, China

    8 publications · 471 citations

    8

    h-index

  2. HL
    Haifeng Lin

    Nanjing Forestry University, China

    6 publications · 331 citations

    6

    h-index

  3. KH
    Kai Hu

    Nanjing University of Information Science and Technology, China

    4 publications · 152 citations

    4

    h-index

  4. LW
    Liguo Weng

    Nanjing University of Information Science and Technology, China

    4 publications · 267 citations

    4

    h-index

  5. RA
    Ronald A. Rensink

    University of British Columbia, Canada

    4 publications · 3.4K citations

    4

    h-index

1–5 of 608

Venues

  1. 24

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  2. Remote Sensing

    16 publications · 2.4K citations

    13

    h-index

  3. Visual Cognition

    13 publications · 1K citations

    12

    h-index

  4. 11

    h-index

  5. IEEE Geoscience and Remote Sensing Letters

    GRSL · 12 publications · 1.1K citations

    10

    h-index

1–5