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Journal of Applied Sciences
  Year: 2010 | Volume: 10 | Issue: 18 | Page No.: 2010-2018
DOI: 10.3923/jas.2010.2010.2018
 
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A Neural Method based on New Constraints for Stereo Matching of Urban High-resolution Satellite Imagery
E. Zigh and M.F. Belbachir

Abstract:
This study presented a simple and fast method for the stereo matching of urban high-resolution satellite images’ pairs. We are interested in the built and the primitive used to match the images is the region, for that, our approach includes two phases: the first one consists of a region segmentation of the images, the second is a neural region matching which is based on new constraints including the geometric and photometric regions properties. In this second phase, the Hopfield neural network used for the matching has the particularity to be initialized by a classical matching method. The interest to make this combination is double: the classical region matching makes it possible to ensure a better initialization of the Hopfield network and this last comes to improve the stereo matching rate and to minimize ambiguities resulting from the classical matching. This network solves the optimization problem by minimizing a cost function whose minimum value represents the best solution, the nodes are the assumptions (the possible correspondences) and the connections between them are the constraints. We compared the method given above with another: classical region matching method applied alone to which we allow the thresholds of the neural system, the results are less good than those of the first one (reduced number of ambiguities but a weak matching rate). Thus, the proposed method is effective to ensure at the same time a reduction in ambiguities and an elevation of the matching rate. It is simple and has a weak cost in computing.
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How to cite this article:

E. Zigh and M.F. Belbachir, 2010. A Neural Method based on New Constraints for Stereo Matching of Urban High-resolution Satellite Imagery. Journal of Applied Sciences, 10: 2010-2018.

DOI: 10.3923/jas.2010.2010.2018

URL: https://scialert.net/abstract/?doi=jas.2010.2010.2018

 
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