Imputation of missing parts in UAV orthomosaics using PlanetScope and Sentinel-2 data: a case study in a grass-dominated área.
In this study, we propose a methodological framework to impute missing parts of UAV orthomosaics using PlanetScope (PS) and Sentinel-2 (S2) data and the random forest (RF) algorithm of an integrated crop?livestock system (ICLS) covered by grass at the time.
Saved in:
Main Authors: | , , , , , , , , , |
---|---|
Other Authors: | |
Format: | Artigo de periódico biblioteca |
Language: | Ingles English |
Published: |
International Journal of Geo-Information, v. 12, n. 2, 41, Feb. 2023.
2023-01-30
|
Subjects: | Índice de vegetação, Aprendizado de máquina, Random forest, Data intercalibration, Spatial gap-filling method, Spatial imputation method, Machine learning, Agricultura de Precisão, Sensoriamento Remoto, Precision agriculture, Remote sensing, Unmanned aerial vehicles, Normalized difference vegetation index, |
Online Access: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1151339 https://doi.org/ 10.3390/ijgi12020041 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|