Segmentation methods for detection of stationary vehicles in combined elevation and optical data

Detection of vehicles in remote sensing data represents a captivating and challenging task that has been studied during many years. The state-of-the-art detection tools can be subdivided into implicit and explicit methods; the latter ones provide detection results by means of some explicitly characterizing features. Mostly, these methods rely on optical aerial images in which vehicles appear distorted. However, 3D elevation data and orthophotos are increasingly available and typically used to perform a full context-based scene analysis of which vehicles are an indispensable part. In this paper, we propose to combine elevation and optical data for segmentation of vehicle-like objects. To do this, several strategies, their advantages and disadvantages, will be discussed. Since any segmentation method also produces numerous false alarms, we will briefly describe the complete vehicle detection pipeline. The results indicate that sensor data fusion is crucial for obtaining the most accurate results in a reasonable time. For example, using trapezoids or stripes formed in optical and elevation data allows one to detect almost all targets with a very high accuracy exceeding the results obtained from single sensor data. We perform an extensive evaluation of all presented methods and outline the main ideas for correction of the existing shortcomings and for a closer embedding of vehicle detection into the process of urban terrain reconstruction from sensor data.