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.

Planetary crater detection and registration using marked point processes, multiple birth and death algorithms, and region-based analysis

SOLARNA, DAVID;Moser, Gabriele;Serpico, Sebastiano B.
2017

Abstract

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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/893585
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