Classification-based vehicle detection in high-resolution satellite images |
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| Authors: | Line Eikvil Lars Aurdal Hans Koren |
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| Institution: | 1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Key Laboratory of Airborne Optical Imaging and Measurement, Chinese Academy of Sciences, Changchun 130033, China;4. Fraunhofer Institute for Computer Graphics Research & TU Darmstadt, 64283 Darmstadt, Germany;5. State Key Laboratory of Applied Optics, Chinese Academy of Sciences, Changchun 130033, China |
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| Abstract: | In this study, we have looked into the problem of vehicle detection in high-resolution satellite images. Based on the input from the local road authorities, we have focused not only on highways, but also on inner city roads, where more clutter is expected. The study site is the city of Oslo, Norway. To do vehicle detection in these areas, we propose an automatic approach, consisting of a segmentation step, followed by two stages of object classification. In the process, we utilize multispectral images, panchromatic images and a road network. The approach has been tested on Quickbird images, and the results that are obtained have been compared with manual counts and classifications. |
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