Vehicles detection using GF-2 imagery based on watershed image segmentation

Road traffic volume monitoring plays an important role in transportation planning and spatial development, particularly in urban areas. The high-resolution satellite imagery provides a new data source to detect vehicles. Meanwhile, Satellite image covers large areas instantaneously, providing a possibility for snapshotting road traffic conditions. In this paper, we proposed an approach based on watershed image segmentation to detect the urban road vehicles from GF-2 imagery. The vehicles detection involves the two main steps: Firstly, a GIS road vector map and vegetation masks were applied to the image to guide vehicle detection by restricting the roads only. Secondly, watershed image segmentation was performed to separate bright and dark vehicles from the background in the road region. Then, a rule-based classifier was established to classify the image objects into the vehicle and the non-vehicle objects by using the spectral and shape feature information of image objects. Finally, the overall performance of the vehicle detection were compared with the manually counts, yielding overall accuracy of 81% with 93% classification accuracy. This detection accuracy may be considered acceptable for operational use in traffic monitoring.

Publication year:2016

Keywords:image segmetation, tracking