2018
DOI: 10.1029/2018ea000399
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A New Enceladus Global Control Network, Image Mosaic, and Updated Pointing Kernels From Cassini's 13‐Year Mission

Abstract: NASA's Cassini spacecraft spent 13 years exploring the Saturn system, including 23 targeted flybys of the small, geologically active moon Enceladus. These flybys provided a wealth of image data from Cassini's Imaging Science Subsystem. To improve the usability of the Enceladus data set, we created a new, global photogrammetric control network for Enceladus that enabled compilation of a versatile cartographic package to support geologic mapping and other investigations. The network used 586 images in four image… Show more

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Cited by 17 publications
(34 citation statements)
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“…The pixel location is calculated as the intersection between Enceladus mean radius (252.1 km) and the VIMS pixel boresight (Nicholson et al, 2019). Our projected VIMS cubes are geometrically consistent with the latest ISS global map (Bland et al, 2018) as it can been on a few examples on Fig. 11.…”
Section: Vims Dataset and Methods For Computing Global Mapssupporting
confidence: 54%
“…The pixel location is calculated as the intersection between Enceladus mean radius (252.1 km) and the VIMS pixel boresight (Nicholson et al, 2019). Our projected VIMS cubes are geometrically consistent with the latest ISS global map (Bland et al, 2018) as it can been on a few examples on Fig. 11.…”
Section: Vims Dataset and Methods For Computing Global Mapssupporting
confidence: 54%
“…A total of 354 long rectilinear‐to‐curvilinear structures which were related to high tonal variations were identified on the Enceladus Cassini global grayscale mosaic assembled from the Cassini Imaging Science Subsystem (ISS) images by the USGS astrogeology program (Bland et al., 2018). The spatial scale of this base mosaic ranges between 500 and 50 m/pixel.…”
Section: Structural Analyses and Resultsmentioning
confidence: 99%
“…In particular, the use of automated, rather than manual methods for locating and measuring tie points, has allowed for a greatly increased number of points per image, and points measured on multiple overlapping images, compared to what was previously being measured manually. As described in Bland, Becker et al (2018), including multiple images ("image measures") in a single tie point enables robust point localization, the determination of point location uncertainty, and the detection of outliers. Additionally, improvements in computational efficiency permit the use of nearly the entire Voyager and Galileo data set (see Section 2) and a relatively dense tie-point network.…”
Section: Previous Workmentioning
confidence: 99%
“…The location of a point in three-dimensional space (e.g., latitude, longitude, radius) can be determined from just two measures, but in that case, the precision is unconstrained. With three or more measures, precision information can be obtained, and the statistical robustness of the location determination improves as the number of measures increases (see Bland, Becker et al [2018] for details).…”
Section: Control Networkmentioning
confidence: 99%