Machine Learning Classification Ensemble of Multitemporal Sentinel-2 Images: The Case of a Mixed Mediterranean Ecosystem

被引:29
|
作者
Vasilakos, Christos [1 ]
Kavroudakis, Dimitris [1 ]
Georganta, Aikaterini [1 ]
机构
[1] Univ Aegean, Dept Geog, Mitilini 81100, Greece
关键词
remote sensing; classification ensemble; machine learning; Sentinel-2; geographic information system (GIS); LAND-COVER CLASSIFICATION; SUPPORT VECTOR MACHINES; ARTIFICIAL NEURAL-NETWORKS; HYPERSPECTRAL IMAGES; RANDOM FOREST; ALGORITHM; SYSTEM; DISCRIMINATION; CLASSIFIERS; ACCURACY;
D O I
10.3390/rs12122005
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Land cover type classification still remains an active research topic while new sensors and methods become available. Applications such as environmental monitoring, natural resource management, and change detection require more accurate, detailed, and constantly updated land-cover type mapping. These needs are fulfilled by newer sensors with high spatial and spectral resolution along with modern data processing algorithms. Sentinel-2 sensor provides data with high spatial, spectral, and temporal resolution for the in classification of highly fragmented landscape. This study applies six traditional data classifiers and nine ensemble methods on multitemporal Sentinel-2 image datasets for identifying land cover types in the heterogeneous Mediterranean landscape of Lesvos Island, Greece. Support vector machine, random forest, artificial neural network, decision tree, linear discriminant analysis, and k-nearest neighbor classifiers are applied and compared with nine ensemble classifiers on the basis of different voting methods. kappa statistic, F1-score, and Matthews correlation coefficient metrics were used in the assembly of the voting methods. Support vector machine outperformed the base classifiers with kappa of 0.91. Support vector machine also outperformed the ensemble classifiers in an unseen dataset. Five voting methods performed better than the rest of the classifiers. A diversity study based on four different metrics revealed that an ensemble can be avoided if a base classifier shows an identifiable superiority. Therefore, ensemble approaches should include a careful selection of base-classifiers based on a diversity analysis.
引用
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页数:25
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