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A martian case study of segmenting images automatically for granulometry and sedimentology, Part 2: Assessment
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| Organization: | Louisiana State University |
| Organization: | State University of New York at Stony Brook |
| Department: | Department of Geosciences |
| Organization: | Astrogeology Science Center |
| Department: | U.S. Geological Survey |
| Organization: | Rider University |
| Department: | Department of Geological, Environmental, and Marine Sciences (GEMS) |
| Organization: | Arizona State University |
| Department: | School of Earth and Space Exploration |
| Organization: | Louisiana State University |
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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.
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Mars, surface, Data reduction techniques, Image processing
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