A vehicle detection method taking shadow areas into account for high resolution aerial imagery

Detecting cars from high-resolution remote sensing images is vulnerable to the effect of shadows in the image, as a result, cars in shadow areas are hard to be detected due to their weak visual features. In order to solve this problem, a vehicle detection method which takes the shaded areas into account is proposed in this letter. Firstly, we extract shadows in road areas using a shadow detection algorithm which is based on color features, and then we enhance the shaded areas by histogram equalization to improve cars' visual characteristics; Secondly, vehicle detection models M1,M2 are trained in shaded and non-shaded regions respectively using HOG features combined with SVM classification method. Finally, we use M1 to extract cars in shadows and the ones which are not in shadows are detected by M2, then the final result is obtained through the combination of the two outcomes. Experiments using multiple sets of test images show that: in contrast with traditional car counting method, the proposed one has the ability to improve the performance of vehicle detection, the probability of wrong detection is decreased as well.