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16 August 2001 Automatic fusion of multiple-sensor and multiple-season images
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The aim of investigation was developing the data fusion algorithms dealing with the aerial and cosmic pictures taken in different seasons from the differing view points, or formed by differing kinds of sensors (visible, IR, SAR). This task couldn't be solved using the traditional correlation based approaches, thus we chose the structural juxtaposition of the stable characteristic details of pictures as the general technique for images matching and fusion. The structural matching usually was applied in the expert systems where the rather reliable results were based on the target specific algorithms. In the contrast to such classifiers our algorithm deals with the aerial and cosmic photographs of arbitrary contents for which the application specific algorithms couldn't be used. To deal with the arbitrary images we chose a structural description alphabet based on the simple contour components: arcs, angles, segments of straight lines, line branching. This alphabet is applicable to the arbitrary images, and its elements due to their simplicity are stable under different image transformations and distortions. To distinguish between the similar simple elements in the huge multitudes of image contours we applied the hierarchical contour descriptions: we grouped the contour elements belonging to the uninterrupted lines or to the separate image regions. Different types of structural matching were applied: the ones based on the simulated annealing and on the restricted examination of all hypotheses. The matching results reached were reliable both for the multiple season and multiple sensor images.
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Vadim R. Lutsiv, Igor A. Malyshev, and Vadim Pepelka "Automatic fusion of multiple-sensor and multiple-season images", Proc. SPIE 4380, Signal Processing, Sensor Fusion, and Target Recognition X, (16 August 2001);

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