Using UAV RGB Image for Object-based Feature Detection

This study investigates the use of Object-based Machine Learning (OBML) algorithms to detect features such as building rooftop materials and vegetation in Red-Green-Blue (RGB) images captured by Unmanned Aerial Vehicles (UAVs) over the Chenkalady Divisional Secretariat in Sri Lanka. It employs Object-based Image Analysis (OBIA) for feature identification through object-based segmentation, feature extraction, and classification using random forest classifiers. The analysis utilizes high-spatial resolution RGB imagery from the OpenAerialMap (OAM) platform, calibrated radiometrically via the Empirical Line Method (ELM). The method achieves over 95% accuracy in segregating rooftop materials based on distinct texture and colour patterns at an optimal segmentation scale. Vegetation indices, such as the Normalized Green-Red Difference Index (NGRDI) and the RGB Vegetation Index (RGBVI) enhance the resources for identifying tree species and building types. Results demonstrate the algorithm’s effectiveness in identifying features, particularly in classifying coconut trees with a 45% precision rate using a 100-sample examination for location accuracy. The nearest neighbour method achieves an 86% accuracy rate within a 1-meter radius. In addition, vegetation indices highlight vegetation zones and help identify different tree species. Accuracy assessment using 100 randomly distributed points shows that both NGRDI and RGBVI vegetation indices achieve an overall accuracy of 96% and a kappa coefficient of 0.97. This study advances feature detection methods in UAV imagery, emphasizing the synergy between deep learning techniques. The proposed algorithm demonstrates promising results in accurately identifying diverse landscape features, paving the way for enhanced applications in environmental monitoring, disaster management, and urban planning using UAVs equipped with visible light sensors that provide high-resolution imagery.