Published August 27, 2021
| Version 0.3
Software
Open
Guided Regularized Random Forest (GRRF) optimization for remote sensing data
- 1. University of Natural Resources and Life Science (BOKU))
- 2. University of Twente
Description
Demo of the paper An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing.
The code classifies a hyperspectral image using GRRF selection, RF using the same number of features and RF using all features.
The authors would like to thank to Houtao Deng for developing the RRF package.
Files
GRRF-optimization.zip
Files
(26.8 MB)
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Additional details
References
- Izquierdo-Verdiguier, E. and Zurita-Milla, R., 2020. An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing. International Journal of Applied Earth Observation and Geoinformation, 88, p.102051
- Izquierdo-Verdiguier, E., Zurita-Milla, R., & Rolf, A. (2017). On the use of guided regularized random forests to identify crops in smallholder farm fields. In 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) (pp. 1-3).
- Izquierdo-Verdiguier, E. and Zurita-Milla, R. (2018). Use of Guided Regularized Random Forest for Biophysical Parameter Retrieval. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 5776-5779, IEEE
- Aguilar, R., Zurita-Milla, R., Izquierdo-Verdiguier, E., & A De By, R. (2018). A cloud-based multi-temporal ensemble classifier to map smallholder farming systems. Remote sensing, 10(5), 729
- Sanchez-Ruiz, S., Moreno-Martinez, A., Izquierdo-Verdiguier, E., Chiesi, M., Maselli, F., & Gilabert, M. A. (2019). Growing stock volume from multi-temporal landsat imagery through google earth engine. International Journal of Applied Earth Observation and Geoinformation, 83, 101913