Automatic object detection on aerial images using local descriptors and image synthesis

Authors:
Xavier Perrotton;Marc Sturzel;Michel Roux
Affiliations:
EADS FRANCE Innovation Works, Suresnes, France and Institut Télécom, Télécom ParisTech, LTCI, UMR, CNRS, Paris, France;EADS FRANCE Innovation Works, Suresnes, France;Institut Télécom, Télécom ParisTech, LTCI, UMR, CNRS, Paris, France
Venue:
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Year:
2008

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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.