Object-based Detection and Classification of Vehicles from High-resolution Aerial Photography

Vehicle counts and truck percentages are important input variables in both noise Pollution and air quality models, but the acquisition of these variables through fixed-point methods can be expensive, labor-intensive, and provide incomplete spatial sampling. The increasing availability and decreasing cost of high spatial resolution imagery provides on opportunity to improve the descriptive ability of traffic volume analysis. This study describes on object-based classification technique to extract vehicle volumes and vehicle type distributions from aerial photos sampled throughout large metropolitan area. We developed rules for optimizing segmentation parameters, and used feature space optimization to choose classification attributes and develop fuzzy-set memberships for classification. Vehicles were extracted from street areas with 91.8 percent accuracy. Furthermore, separation of vehicles into classes based on cor, medium-sized truck, and buses/heavy truck definitions was achieved with 87.5 percent accuracy. We discuss implications of these results for traffic volume analysis and parameterization of existing noise and air pollution models, and suggest future work for traffic assessment Using high-resolution remotely-sensed imagery