TY - JOUR
T1 - A martian case study of segmenting images automatically for granulometry and sedimentology, Part 1
T2 - Algorithm
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. 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 planetary exploration, delineating individual grains in images via segmentation is a key path to sedimentological comparisons with the extensive terrestrial literature. Samples that contain a substantial fine grain component, common at Meridiani and Gusev at Mars, would involve prohibitive effort if attempted manually. Unavailability of physical samples also precludes standard terrestrial methods such as sieving. Furthermore, planetary scientists have been thwarted by the dearth of segmentation algorithms customized for planetary applications, including Mars, and often rely on sub-optimal solutions adapted from medical software. We address this with an original algorithm optimized to segment whole images from the Microscopic Imager of the Mars Exploration Rovers. While our code operates with minimal human guidance, its default parameters can be modified easily for different geologic settings and imagers on Earth and other planets, such as the Curiosity Rover's Mars Hand Lens Instrument. We assess the algorithm's robustness in a companion work.
AB - In planetary exploration, delineating individual grains in images via segmentation is a key path to sedimentological comparisons with the extensive terrestrial literature. Samples that contain a substantial fine grain component, common at Meridiani and Gusev at Mars, would involve prohibitive effort if attempted manually. Unavailability of physical samples also precludes standard terrestrial methods such as sieving. Furthermore, planetary scientists have been thwarted by the dearth of segmentation algorithms customized for planetary applications, including Mars, and often rely on sub-optimal solutions adapted from medical software. We address this with an original algorithm optimized to segment whole images from the Microscopic Imager of the Mars Exploration Rovers. While our code operates with minimal human guidance, its default parameters can be modified easily for different geologic settings and imagers on Earth and other planets, such as the Curiosity Rover's Mars Hand Lens Instrument. We assess the algorithm's robustness in a companion work.
KW - Mars, surface
UR - http://www.scopus.com/inward/record.url?scp=84890891025&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890891025&partnerID=8YFLogxK
U2 - 10.1016/j.icarus.2013.10.001
DO - 10.1016/j.icarus.2013.10.001
M3 - Article
AN - SCOPUS:84890891025
SN - 0019-1035
VL - 229
SP - 400
EP - 407
JO - Icarus
JF - Icarus
ER -