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Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multisensor and multitemporal images. These multiple data represent a precious asset, as they allow the study of target spectral responses and of changes in the surface structure. Because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features to be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters), and a birth-death Markov chain method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computation time
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