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 M
₁
,M
₂
are trained in shaded and non-shaded regions respectively using HOG features combined with SVM classification method. Finally, we use M
₁
to extract cars in shadows and the ones which are not in shadows are detected by M
₂
, 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.