Deep Learning-based Change Detection

Deep Learning-Based Change Detection is a change detection methodology that employs deep neural networks to automatically learn and identify temporal changes from multi-temporal data, distinguishing itself from traditional methods through its capacity for hierarchical feature extraction and end-to-end learning.

590

Publications

45.3K

Citations

1.9K

Authors

536

Institutions

Publications per year

2017–2026

553

Authors

1.9K

  1. MG
    Maoguo Gong

    Xidian University, China

    22 publications · 2.8K citations

    21

    h-index

  2. LJ
    Licheng Jiao

    Xidian University, China

    20 publications · 1.7K citations

    17

    h-index

  3. LZ
    Liangpei Zhang

    Wuhan University, China

    16 publications · 2.8K citations

    16

    h-index

  4. LB
    Lorenzo Bruzzone

    University of Trento, Italy

    16 publications · 1.6K citations

    15

    h-index

  5. SS
    Sudipan Saha

    Technical University of Munich, Germany

    12 publications · 611 citations

    12

    h-index

1–5 of 1.9K

Venues

  1. IEEE Transactions on Geoscience and Remote Sensing

    119 publications · 11.9K citations

    61

    h-index

  2. Remote Sensing

    69 publications · 4.7K citations

    32

    h-index

  3. IEEE Geoscience and Remote Sensing Letters

    GRSL · 43 publications · 4.6K citations

    31

    h-index

  4. 27

    h-index

  5. 19

    h-index

1–5