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29 September 2006 Image registration using RST-clustering and its application in remote sensing
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In this paper we address the problem of registering images acquired under unknown conditions including acquisition at different times, from different points of view and possibly with different type of sensors, where conventional approaches based on feature correspondence or area correlation are likely to fail or provide unreliable estimates. The result of image registration can be used as initial step for many remote sensing applications such as change detection, terrain reconstruction and image-based sensor navigation. The key idea of the proposed method is to estimate a global parametric transformation between images (e.g. perspective or affine transformation) from a set of local, region-based estimates of rotation-scale-translation (RST) transformation. These RST-transformations form a cluster in rotation-scale space. Each RST-transformation is registered by matching in log-polar space the regions centered at locations of the corresponding interest points. Estimation of the correspondence between interest points is performed simultaneously with registration of the local RST-transformations. Then a sub-set of corresponding points or, equivalently, a sub-set of local RST-transformations is selected by a robust estimation method and a global transformation, which is not biased by outliers, is computed from it. The method is capable of registering images without any a priori knowledge about the transformation between them. The method was tested on many images taken under different conditions by different sensors and on thousands of calibrated image pairs. In all cases the method shows very accurate registration results. We demonstrate the performance of our approach using several datasets and compare it with another state-of-the-art method based on the SIFT descriptor.
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Alexander Sibiryakov and Miroslaw Bober "Image registration using RST-clustering and its application in remote sensing", Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 63650G (29 September 2006);

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