Abstract
The presented work aims at defining techniques for the detection and localisation of objects, such as aircrafts in clutter backgrounds, on aerial or satellite images. A boosting algorithm is used to select discriminating features and a descriptor robust to background and target texture variations is introduced. Several classical descriptors have been studied and compared to the new descriptor, the HDHR. It is based on the assumption that targets and backgrounds have different textures. Image synthesis is then used to generate large amounts of learning data: the Adaboost has thus access to sufficiently representative data to take into account the variability of real operational scenes. Observed results prove that a vision system can be trained on adapted simulated data and yet be efficient on real images.
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Perrotton, X., Sturzel, M., Roux, M. (2008). Automatic Object Detection on Aerial Images Using Local Descriptors and Image Synthesis. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_29
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DOI: https://doi.org/10.1007/978-3-540-79547-6_29
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