EVALUATION OF FEATURE SELECTION METHODS FOR VEGETATION MAPPING USING MULTITEMPORAL SENTINEL IMAGERY

7 citations as recorded by crossref.

1. Mapping of Allergenic Tree Species in Highly Urbanized Area Using PlanetScope Imagery—A Case Study of Zagreb, Croatia M. Gašparović et al. https://doi.org/10.3390/f14061193
2. Improving Neural Network classification of native forest in New Zealand with phenological features N. Ye et al. https://doi.org/10.1080/01431161.2023.2264496
3. Developing Infiltration Model: Random Forest for Micro-Hydro Power Planning A. Varhana et al. https://doi.org/10.1088/1755-1315/1418/1/012055
4. Supervised Classification of Tree Cover Classes in the Complex Mosaic Landscape of Eastern Rwanda N. Gutkin et al. https://doi.org/10.3390/rs15102606
5. Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping D. Dobrinić et al. https://doi.org/10.3390/s25020464
6. Spatiotemporal changes of Mollic Horizon thickness based residual random forest in typical province of northeastern China Z. Gao et al. https://doi.org/10.1016/j.catena.2025.109327
7. Advancing forest mapping: Pretraining strategies and deep-ensemble based uncertainty for predicting evergreen broad-leaved cover from Sentinel-2 time series B. Hiebl et al. https://doi.org/10.1016/j.jag.2025.104734