Vegetation extraction through UAV RGB imagery and efficient feature selection.

Accurate identification of vegetation in mining areas is crucial for conducting pre-mining ecological assessments and post-mining ecological monitoring. However, the vegetation in the mining area is always highly heterogeneous including both field crops and naturally scattered growing vegetation, which brings great challenges for fine vegetation mapping. Feature combinations are an important factor to influence the vegetation mapping. Thus, to effectively identify the vegetation, this study utilized an unmanned aerial vehicle (UAV) RGB image to extract vegetation indexes and textures, and then selected features based on standard deviation and difference coefficient. By integrating selected optimal features with RGB images, different combinations were constructed and classified using Support Vector Machine (SVM). The results demonstrated that the combination of RGB and all selected features yielded the highest accuracy, followed by the combination of RGB and a single type of texture, and then the combination of RGB and VIs, which indicated that texture features were more important than VIs for vegetation identification. The OA and Kappa for the best combination were 87.76% and 0.8351 for study area A, and 88.74% and 0.8505 for study area B, indicating the effectiveness of the adopted method. Besides, compared with the commonly used random forest (RF) feature selection method, the adopted method avoided complex parameter settings and constructed a superior optimal combination, which further proved the simplicity and effectiveness of difference coefficient-based feature selection methods for vegetation classification in highly heterogeneous environments, contributing to more accurate ecological assessments and monitoring.