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Evaluation of Xgboost and Lgbm Performance in Tree Species Classification with Sentinel-2 Data
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Orientador(es)
Resumo(s)
Tree species classification with satellite data has become more and more popular since Sentinel-2 launch. We compared efficacy and effectiveness of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LGBM) with widely used in remote sensing Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) algorithms. Analyses were performed over an area in Portugal with multi-temporal Sentinel-2 data registered in April, June, August and October 2018. The selected classes were: cork oak, holm oak, eucalyptus, other broadleaved, maritime pine, stone pine and other coniferous. Algorithm efficacy was measured through F1-score and accuracy while efficiency was measured through the median time needed for each fit. XGBoost and LGBM outperformed efficacy of other algorithms, which was already high (above 90% for the best variant of each algorithm). In terms of efficacy, LGBM overcame all algorithms, including XGBoost.
Descrição
Los, H., Mendes, G. S., Cordeiro, D., Grosso, N., Costa, H., Benevides, P., & Caetano, M. (2021). Evaluation of Xgboost and Lgbm Performance in Tree Species Classification with Sentinel-2 Data. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 5803-5806). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553031
Palavras-chave
Computer Science Applications General Earth and Planetary Sciences
Contexto Educativo
Citação
Editora
Institute of Electrical and Electronics Engineers (IEEE)