Soft computing strategy for stereo matching of multi spectral urban very high resolution IKONOS images

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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► Fuzzy thresholding proposed method realizes at the same time both segmentation and thresholding.
► It allows good results of buildings extraction step without requiring a high solution cost or other technological resources.
► Hopfield neural stereo matching method is based on new constraints, it is initialized by a classical matching technic.
► Hopfield neural stereo matching proposed method improves matching rate and decrease ambiguities.
► All proposed strategy exploits soft computing proprieties to achieve simplicity, good results and low solution cost.