Bayesian Change Detection

Bayesian Change Detection is a probabilistic framework for identifying temporal alterations in spatial data by modeling uncertainty through Bayesian inference, distinguishing itself from other change detection methods through its explicit incorporation of prior knowledge and posterior probability updates.

183

Publications

9.1K

Citations

457

Authors

267

Institutions

Publications per year

2017–2026

45

Authors

457

  1. VV
    Venugopal V. Veeravalli

    University of Illinois Urbana-Champaign, United States

    6 publications · 624 citations

    6

    h-index

  2. AG
    Alexander G. Tartakovsky

    University of Southern California, United States

    4 publications · 352 citations

    4

    h-index

  3. NS
    N. Serinken

    Communications Research Centre Canada, Canada

    4 publications · 254 citations

    4

    h-index

  4. OÜ
    O. Üreten

    Communications Research Centre Canada, Canada

    4 publications · 254 citations

    4

    h-index

  5. OS
    Ousmane Seidou

    University of Ottawa, Canada

    4 publications · 206 citations

    4

    h-index

1–5 of 457

Venues

  1. IEEE Transactions on Information Theory

    7 publications · 409 citations

    7

    h-index

  2. International Journal of Climatology

    5 publications · 245 citations

    5

    h-index

  3. Journal of Climate

    5 publications · 222 citations

    5

    h-index

  4. Water Resources Research

    5 publications · 239 citations

    5

    h-index

  5. PLoS Computational Biology

    4 publications · 198 citations

    4

    h-index

1–5