Multi-temporal Data Fusion

Multi-Temporal Data Fusion is a computational framework that integrates remotely sensed observations acquired at different times to generate a coherent, enhanced representation of Earth surface dynamics, distinguished by its capacity to harmonize temporal, spatial, and spectral variabilities across time-series imagery for improved change detection and monitoring.

453

Publications

28.6K

Citations

1.7K

Authors

605

Institutions

Publications per year

2017–2026

320

Authors

1.7K

  1. HS
    Huanfeng Shen

    Wuhan University, China

    17 publications · 999 citations

    15

    h-index

  2. XZ
    Xiaolin Zhu

    Hong Kong Polytechnic University, Hong Kong SAR China

    14 publications · 3.5K citations

    14

    h-index

  3. JC
    Jin Chen

    Beijing Normal University, China

    11 publications · 2.6K citations

    12

    h-index

  4. FG
    Feng Gao

    Agricultural Research Service, United States

    11 publications · 5.1K citations

    11

    h-index

  5. LZ
    Liangpei Zhang

    Wuhan University, China

    9 publications · 703 citations

    10

    h-index

1–5 of 1.7K

Venues

  1. Remote Sensing

    69 publications · 4.4K citations

    37

    h-index

  2. Remote Sensing of Environment

    29 publications · 5.6K citations

    29

    h-index

  3. 25

    h-index

  4. 19

    h-index

  5. 13

    h-index

1–5