Evaluation of Xgboost and Lgbm Performance in Tree Species Classification with Sentinel-2 Data

H. Los, G. Sousa Mendes, D. Cordeiro, N. Grosso, Hugo Costa, P. Benevides, Mário Caetano

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Abstract

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.
Original languageEnglish
Title of host publicationIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationProceedings
PublisherIEEE
Pages5803-5806
Number of pages4
ISBN (Print)978-1-6654-0369-6
DOIs
Publication statusPublished - 11 Jul 2021
EventIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium - Brussels, Belgium
Duration: 11 Jul 202116 Jul 2021

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2021-July

Conference

ConferenceIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium
Country/TerritoryBelgium
CityBrussels
Period11/07/2116/07/21

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