VEHICLE DETECTION AND ROADSIDE TREE SHADOW REMOVAL IN HIGH RESOLUTION SATELLITE IMAGES

Over the last few years, the increased availability of high resolution remote sensing imagery has opened new opportunities for road traffic monitoring applications. Vehicle detection from satellite images has a potential ability to cover large geographical areas and can provide valuable additional information to traditional ground based counting equipment. However, shadows cast from trees and other vegetation growing along the side of the road cause challenges since it can be confused with dark vehicles during classification. As the intensity properties of dark vehicles and vegetation shadow segments are visually inseparable in the panchromatic image, their separation must be exclusively based on shape and context. We first present a method for extraction of dark regions corresponding to potential shadows by the use of contextual information from a vegetation mask and road vector data. Then we propose an algorithm for separating vehicles from shadows by analyzing the curvature properties of the dark regions. The extracted segments are then carried on to the classification stage of the vehicle detection processing chain. The algorithm is evaluated on Quickbird panchromatic satellite images with 0.6m resolution. The results show that we are able to detected vehicles that are fully connected with the cast shadow, and at the same time ignore false detections from tree shadows. The performance evaluation shows that we are able to obtain a detection rate as high as 94.5%, and a false alarm rate as low as 6%.