Supervised Change Detection

Supervised Change Detection is a change detection approach that belongs to the class of supervised learning methods, distinguished by its reliance on labeled training data to learn the patterns of change and non-change between temporal observations for subsequent classification.

55

Publications

4.5K

Citations

183

Authors

109

Institutions

Publications per year

2017–2026

31

Authors

183

  1. KC
    Keming Chen

    Chinese Academy of Sciences, China

    5 publications · 403 citations

    5

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  2. LB
    Lorenzo Bruzzone

    University of Trento, Italy

    4 publications · 775 citations

    4

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  3. XS
    Xian Sun

    Chinese Academy of Sciences, China

    4 publications · 916 citations

    4

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  4. MY
    Menglong Yan

    Chinese Academy of Sciences, China

    3 publications · 893 citations

    3

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  5. GX
    Guangluan Xu

    Chinese Academy of Sciences, China

    3 publications · 346 citations

    3

    h-index

1–5 of 183

Venues

  1. IEEE Geoscience and Remote Sensing Letters

    GRSL · 8 publications · 1.2K citations

    8

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  2. Remote Sensing

    6 publications · 445 citations

    6

    h-index

  3. 5

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  4. 5

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  5. 2

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1–5