The impacts of vegetation indices from UAV-based RGB imagery on land cover classification using ensemble learning

Abstract

The production of land use and land cover (LULC) maps using UAV images obtained by RGB cameras offering very high spatial resolution has recently increased. Vegetation indices (VIs) have been widely used as an important ancillary data to increase the limited spectral information of the UAV image in pixel-based classification. The main goal of this study is to analyze the effect of frequently used RGB-based VIs including green leaf index (GLI), red- green-blue vegetation index (RGBVI) and triangular greenness index (TGI) on the classification of UAV images. For this purpose, five different dataset combinations comprising of RGB bands and VIs were formed. In order to evaluate their effects on thematic map accuracy, four ensemble learning methods, namely RF, XGBoost, LightGBM and CatBoost were utilized in classification process. Classification results showed that the use of RGB UAV image with VIs increased the overall accuracy (OA) values in all cases. On the other hand, the highest OA values were calculated with the use of Dataset-5 (i.e. RGB bands and all VIs considered). Additionally, the classification result of Dataset-4 (i.e. RGB bands and TGI) showed superior performance compared to Dataset-2 (i.e. RGB bands and GLI) and Dataset-3 (i.e. RGB bands and RGBVI). All in all, the TGI was found to be useful for improving classification accuracy of UAV image having limited spectral information compared to GLI and RGBVI. The improvement in overall accuracy reached to 2% with the use of RGB bands and TGI index. Furthermore, within the ensemble algorithms, CatBoost produced the highest overall accuracy (92.24%) with the dataset consist of RBG bands and all VIs considered. 

Keywords

• Ensemble learning
• UAV
• LULC
• LightGBM
• XGBoost

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