Astrometric Reduction of Cassini ISS Images of Enceladus in 2015 Based on Gaia DR1

Zhang, Q. F.; Lainey, V.; Vienne, A.; Cooper, Nick; Peng, Q. Y.; Wang, N.

doi:10.1017/s1743921317005555

Cited by 1 publication

(1 citation statement)

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“…The standard deviations of residuals were a few kilometers. Zhang et al (2017Zhang et al ( , 2018 have conducted astrometry of Enceladus using automatic method based on k-dimensional tree and Gaia DR1 star catalogue. Estimated standard deviations of residuals relative to JPL's SAT375 ephemeris were 2-4 kilometers.…”

Section: Introductionmentioning

confidence: 99%

Automatic removal of false image stars in disk-resolved images of the Cassini Imaging Science Subsystem

Zhang,

Lu,

Zhou

et al. 2020

Preprint

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Taking a large amount of images, the Cassini Imaging Science Subsystem (ISS) has been routinely used in astrometry. In ISS images, disk-resolved objects often lead to false detection of stars that disturb the camera pointing correction. The aim of this study was to develop an automated processing method to remove the false image stars in disk-resolved objects in ISS images. The method included the following steps: extracting edges, segmenting boundary arcs, fitting circles and excluding false image stars. The proposed method was tested using 200 ISS images. Preliminary experimental results show that it can remove the false image stars in more than 95% of ISS images with disk-resolved objects in a fully automatic manner, i.e. outperforming the traditional circle detection based on Circular Hough Transform (CHT) by 17%. In addition, its speed is more than twice as fast as that of the CHT method. It is also more robust (no manual parameter tuning is needed) when compared with CHT. The proposed method was also applied to a set of ISS images of Rhea to eliminate the mismatch in pointing correction in automatic procedure. Experiment results showed that the precision of final astrometry results can be improve by roughly 2 times than that of automatic procedure without the method. It proved that the proposed method is helpful in the astrometry of ISS images in fully automatic manner.

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Section: Introductionmentioning

confidence: 99%