Spatial Feature Selection

Spatial Feature Selection is a dimensionality reduction technique that identifies and retains the most relevant spatial attributes or variables for modeling spatial patterns and processes, differentiating itself by explicitly accounting for spatial autocorrelation and spatial heterogeneity in the selection criteria.

98

Publications

7.8K

Citations

335

Authors

188

Institutions

Publications per year

2017–2026

47

Authors

335

  1. KR
    Kyle R. Cave

    University of Southampton, United Kingdom

    3 publications · 332 citations

    3

    h-index

  2. AS
    Anna Schubö

    Philipps University of Marburg, Germany

    2 publications · 84 citations

    2

    h-index

  3. AH
    Anna Heuer

    Philipps University of Marburg, Germany

    2 publications · 84 citations

    2

    h-index

  4. CR
    Christoph Reudenbach

    Philipps University of Marburg, Germany

    2 publications · 953 citations

    2

    h-index

  5. CC
    Christopher Conrad

    University of Würzburg, Germany

    2 publications · 86 citations

    2

    h-index

1–5 of 335

Venues

  1. Remote Sensing

    6 publications · 608 citations

    6

    h-index

  2. Sensors

    3 publications · 113 citations

    3

    h-index

  3. Journal of Neurophysiology

    JN · 2 publications · 60 citations

    2

    h-index

  4. Attention Perception & Psychophysics

    2 publications · 33 citations

    2

    h-index

  5. 2

    h-index

1–5