期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B3
页码：973-979
出版社：Copernicus Publications
摘要：In this paper several possibilities of vehicle extraction from different airborne sensor systems are described. Three major frequency domains of remote sensing are considered, namely (i) visual, (ii) thermal IR and (iii) radar. Due to the complementing acquired scene properties, the image processing methods have to be tailored for the peculiarities of the different kinds of sensor data. (i)Vehicle detection in aerial images relies upon local and global features. For modelling a vehicle on the local level, a 3D- wireframe representation is used describing prominent geometric and radiometric features of cars including their shadow region. A vehicle is extracted by a "top-down" matching of the model to the image. On the global level, vehicle queues are modelled by ribbons that exhibit typical symmetries and spacing of vehicles over a larger distance. Fusing local and global extractions makes the result more complete. (ii) Particularly at night video sequences from an infrared camera yield suitable data to assess the activity of vehicles. At the resolution of approximately one meter vehicles appear as elongated spots. However, in urban areas many additional other objects have the same property. Vehicles may be discriminated from these objects either by their movement or by their temperature and their appearance in groups. Using map knowledge as context, a grouping of vehicles into rows along road margins is performed. (iii) The active scene illumination and large signal wavelength of SAR allows remote sensing on a day-night basis and under bad weather conditions. High-resolution SAR systems open the possibility to detect objects like vehicles and to determine the velocity of moving objects. Along-track interferometry allows estimation even small vehicle movements. However, in urban areas SAR specific illumination phenomena like foreshortening, layover, shadow, and multipath-propagation burden the interpretation. Particularly the visibility of the vehicles in inner city areas is in question. A high resolution LIDAR DEM is incorporated to determine the visibility of the roads by a SAR measurement from a given sensor trajectory and sensor orientation. Shadow and layover areas are detected by incoherent sampling of the DEM. In order to determine the optimal flight path a large number of simulations are carried out with varying viewing and aspect angles
