Abstract
A set of methodologies and techniques for automatic detection of bridges in pan-chromatic, high-resolution satellite images is presented. These methods rely on (a) radiometric features and neural networks to classify each pixel into several terrain types, and (b) fixed rules to find bridges in this classification. They can be easily extended to other kinds of geographical objects, and integrated with existing techniques using geometric features. The proposed method has been tested in a number of experiments.
This author is also with Institut Géographique National; 2–4 av. Pasteur; 94165 Saint-Mandécedex; France.
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Trias-Sanz, R., Loménie, N. (2003). Automatic Bridge Detection in High-Resolution Satellite Images. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds) Computer Vision Systems. ICVS 2003. Lecture Notes in Computer Science, vol 2626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36592-3_17
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DOI: https://doi.org/10.1007/3-540-36592-3_17
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