Post-classification Change Analysis

Post-Classification Change Analysis is a computational method that identifies and quantifies alterations in data instances or model predictions after an initial classification has been performed, distinguishing itself by focusing on temporal or conditional shifts in outcomes rather than on the primary classification task.

73

Publications

4.8K

Citations

229

Authors

108

Institutions

Publications per year

2017–2026

23

Authors

229

  1. YX
    Yidi Xu

    Tsinghua University, China

    2 publications · 163 citations

    2

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  2. PG
    Peng Gong

    Tsinghua University, China

    2 publications · 163 citations

    2

    h-index

  3. XP
    Xavier Pons

    Universitat Autònoma de Barcelona, Spain

    2 publications · 284 citations

    2

    h-index

  4. LY
    Le Yu

    Tsinghua University, China

    2 publications · 163 citations

    2

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  5. RR
    René R. Colditz

    National Commission for the Knowledge and Use of Biodiversity, Mexico

    2 publications · 53 citations

    2

    h-index

1–5 of 229

Venues

  1. International Journal of Remote Sensing

    11 publications · 946 citations

    11

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  2. Remote Sensing

    9 publications · 418 citations

    9

    h-index

  3. Remote Sensing of Environment

    4 publications · 568 citations

    4

    h-index

  4. 4

    h-index

  5. 3

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1–5