Fuzzy Neural Network-Based Assessment of Road Traffic Situations Using Extracted Information Obtained from Optical High-Resolution Satellite Remote Sensing Images

This study proposes a comprehensive fuzzy neural network (FNN) traffic assessment method using the optical high-resolution remote sensing image (RSI) to process a non-quantified relationship between traffic information and the assessment result. Using the classic road extraction and vehicle detection method, the number of lanes and vehicle density and velocity are obtained as the model input variables. The FNN traffic assessment model is established using the Takagi-Sugeno-Kang network structure, which is constructed using the BP network based on four types of traffic situation. Using QuickBird and WorldView2 satellite 0.5 m resolution panchromatic images, the experimental results show a reasonable traffic assessment result.