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This work aims to define a new strategy for extracting and stereo matching of buildings using very high resolution multi spectral IKONOS images having a ratio base/height about 0.53, we do not have the intrinsic and extrinsic parameters of the images acquisition system. These images contain dense urban scenes including various kinds of roads, cars, vegetation and buildings. We are interested by buildings, some of them have different shapes or colours and others have close colours or shapes, so, they generate a lot of ''false matches''. To solve this issue, we propose in this paper an approach based on soft computing field in order to extract regions of interest (buildings) and to match them, it contains two main steps: region segmentation and thresholding step using a specific fuzzy thresholding algorithm and a neural Hopfield matching stage based on new constraints including geometric and photometric regions properties. The presented strategy is nearly all automatic, it is fast and simple and the results of its applied tests on several kinds of stereo dense urban images are satisfactory. |
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