Semi-supervised Change Detection

Semi-Supervised Change Detection is a change detection methodology that belongs to the class of semi-supervised learning approaches, distinguished by its utilization of a limited set of labeled samples combined with a larger set of unlabeled data to identify spatial changes between multi-temporal remote sensing images.

68

Publications

3.8K

Citations

252

Authors

99

Institutions

Publications per year

2017–2026

46

Authors

252

  1. LB
    Lorenzo Bruzzone

    University of Trento, Italy

    5 publications · 820 citations

    5

    h-index

  2. LJ
    Licheng Jiao

    Xidian University, China

    4 publications · 98 citations

    4

    h-index

  3. SG
    Susmita Ghosh

    Jadavpur University, India

    3 publications · 163 citations

    3

    h-index

  4. KC
    Keming Chen

    Chinese Academy of Sciences, China

    3 publications · 73 citations

    3

    h-index

  5. AG
    Ashish Ghosh

    Indian Statistical Institute, India

    3 publications · 163 citations

    3

    h-index

1–5 of 252

Venues

  1. 17

    h-index

  2. Remote Sensing

    6 publications · 278 citations

    6

    h-index

  3. IEEE Geoscience and Remote Sensing Letters

    GRSL · 6 publications · 227 citations

    5

    h-index

  4. arXiv (Cornell University)

    Repository · 3 publications · 106 citations

    3

    h-index

  5. 3

    h-index

1–5