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A martian case study of segmenting images automatically for granulometry and sedimentology, Part 2: Assessment

Suniti Karunatillake
Organization: Louisiana State University
Scott M. McLennan
Organization: State University of New York at Stony Brook
Department: Department of Geosciences
Kenneth E. Herkenhoff
Organization: Astrogeology Science Center
Department: U.S. Geological Survey
Jonathan M. Husch
Organization: Rider University
Department: Department of Geological, Environmental, and Marine Sciences (GEMS)
Craig Hardgrove
Organization: Arizona State University
Department: School of Earth and Space Exploration
J.R. Skok
Organization: Louisiana State University
Journal / Anthology

Year: 2014
Volume: 229
Page range: 408-417

In a companion work, we bridge the gap between mature segmentation software used in terrestrial sedimentology and emergent planetary segmentation with an original algorithm optimized to segment whole images from the Microscopic Imager (MI) of the Mars Exploration Rovers (MER). In this work, we compare its semi-automated outcome with manual photoanalyses using unconsolidated sediment at Gusev and Meridiani Planum sites for geologic context. On average, our code and manual segmentation converge to within 10% in the number and total area of identified grains in a pseudo-random, single blind comparison of 50 samples. Unlike manual segmentation, it also locates finer grains in an image with internal consistency, enabling robust comparisons across geologic contexts. When implemented in Mathematica- 8, the algorithm segments an entire MI image within minutes, surpassing the extent and speed possible with manual segmentation by about a factor of ten. These results indicate that our algorithm enables not only new sedimentological insight from the MER MI data, but also detailed sedimentology with the Mars Science Laboratory’s Mars Hand Lens Instrument.

*Science > Astronomy

Mars, surface, Data reduction techniques, Image processing