It is very essential and important in urban areas for the automatic detection of geographical objects such as buildings, trees, and vehicles by using remotely sensed images. In this study, the stationary vehicles were detected from very high spatial resolution stereo color (Red, Green, Blue) unmanned aerial vehicles (UAV) images in urban areas. In the first step of the approach used, digital surface model (DSM) is generated from the stereo images. Then, digital terrain model (DTM) is generated from the DSM, and by using the DSM orthophotos are generated from IHA images. Next, the normalized digital surface model (nDSM) is calculated by taking the difference between the DSM and DTM to obtain only the ground objects. After that, using the obtained nDSM data as an additional band, the multi-resolution segmentation and then object-based classification of the orthophoto are carried out. The approach was applied on two areas with different characteristics at Hacettepe University, Beytepe Campus. After comparing the results with the reference data, the vehicle detection accuracy was computed as 78.53% for the first test field (Field # 1) and it was computed as 92.15% for the second test field (Field # 2). The results show that the detection of stationary vehicles from very high spatial resolution UAV images can be performed with high accuracy using the proposed approach.