How to involve structural modeling for cartographic object recognition tasks in high-resolution satellite images?

Authors:
Guray Erus;Nicolas Loménie
Affiliations:
Department of Mathematics and Informatics, LIPADE Laboratory, SIP Team, Paris Descartes University, 45 rue des Saints-Pères, 75006 Paris, France;Department of Mathematics and Informatics, LIPADE Laboratory, SIP Team, Paris Descartes University, 45 rue des Saints-Pères, 75006 Paris, France and CNRS - IPAL - UMI 2955, Singapore
Venue:
Pattern Recognition Letters
Year:
2010

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Abstract

With the new generation of satellite systems, very high resolution satellite images will be available daily at a high delivery rate. The exploitation of such a huge amount of data will be made possible by the design of high performance analysis algorithms for decision making systems. In particular, the detection and recognition of complex man-made objects is a new challenge coming with this new level of resolution. In this study, we develop a system that recognizes such structured and compact objects like bridges or roundabouts. The original contribution of this work is the use of structural shape attributes in an appearance-based statistical learning method framework leading to valuable recognition and false alarm rates. This hybrid structural/statistical approach aims to construct an intermediate step between the low-level image characteristics and high-level semantic concepts.