Road Vehicle Detection and Classification from Very-High-Resolution Color Digital Orthoimagery based on Object-Oriented Method

In the paper, we adopted an object-oriented image analysis method to detect and classify road vehicles from airborne color digital orthoimagery at a ground pixel resolution of 20 cm. Firstly; a vector-generated road mask was used to constrain detection and classification of vehicles to road region. Secondly, image segmentation and edge detection algorithms were performed to separate vehicles from the background in the road region. Then, a fuzzy logic classifier was constructed to classify the extracted object regions into the vehicle and the non-vehicle regions by using the feature information of image objects. Finally, based on the calculated average length and width of vehicles, we classified vehicles into three categories, that is, small, medium and big. And the counts of the three vehicle classes were derived. The automatic counts match manual counts very well.