Remote Sensing Feature Derivation

Remote Sensing Feature Derivation is a methodological framework within geospatial feature engineering that systematically extracts and transforms spectral, spatial, and temporal information from remote sensing data into meaningful, higher-level features through computational algorithms, distinguishing it from general feature extraction by its specific focus on sensor-derived Earth observation data.

9

Publications

375

Citations

36

Authors

18

Institutions

Publications per year

2017–2026

3

Authors

36

  1. HK
    Hermann Kaufmann

    GFZ Helmholtz Centre for Geosciences, Germany

    1 publications · 55 citations

    1

    h-index

  2. HH
    Hans Heinrich Blotevogel

    TU Dortmund University, Germany

    1 publications · 115 citations

    1

    h-index

  3. SG
    SamuelN. Goward

    University of Maryland, College Park, United States

    1 publications · 11 citations

    1

    h-index

  4. LD
    Lance D. Yarbrough

    University of Mississippi, United States

    1 publications · 23 citations

    1

    h-index

  5. ML
    Michael L. Grieneisen

    University of California, Davis, United States

    1 publications · 35 citations

    1

    h-index

1–5 of 36

Venues

  1. Ecological Informatics

    1 publications · 45 citations

    1

    h-index

  2. 1

    h-index

  3. IEEE Geoscience and Remote Sensing Letters

    GRSL · 1 publications · 24 citations

    1

    h-index

  4. Mathematical Geology

    1 publications · 47 citations

    1

    h-index

  5. The Science of The Total Environment

    1 publications · 35 citations

    1

    h-index

1–5