TY - JOUR
T1 - A martian case study of segmenting images automatically for granulometry and sedimentology, Part 2
T2 - Assessment
AU - Karunatillake, Suniti
AU - McLennan, Scott M.
AU - Herkenhoff, Kenneth E.
AU - Husch, Jonathan M.
AU - Hardgrove, Craig
AU - Skok, J. R.
N1 - Funding Information:
Peter Overmann, Shadi Ashnai, Theodore Gray, and other members of the Wolfram Research Image Processing Team provided analytical and coding solutions to key hurdles, without which we would have been unsuccessful in developing our algorithm. Peter Borg at Rider University provided a critical imaging campaign for preliminary terrestrial sediment analyses. We thank Dave Rubin and Aileen Yingst for vital feedback on an earlier version of the manuscript, which also helped to clarify our path for future research. We were supported by NASA Mars Data Analysis Program Grants NNX07AN96G, NNX10AQ23G, NNZ11AI94G, and NNX13AI98G. Louisiana State University’s Geology and Geophysics Department in the College of Science provided postdoctoral funding to support J.R. Skok. Undergraduate students Jade Bing, Jacqueline Bleakley, Thomas Vajtay, and Thomas Weindl at Rider University provided helpful suggestions, enhancing the readability of our work.
PY - 2014/2
Y1 - 2014/2
N2 - 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.
AB - 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.
KW - Data reduction techniques
KW - Image processing
KW - Mars, surface
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U2 - 10.1016/j.icarus.2013.09.021
DO - 10.1016/j.icarus.2013.09.021
M3 - Article
AN - SCOPUS:84890865973
SN - 0019-1035
VL - 229
SP - 408
EP - 417
JO - Icarus
JF - Icarus
ER -