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redOrbit Staff & Wire Reports – Your Universe Online
While the instruments on NASA’s Mars rover Curiosity are able to easily identify the chemical composition of rocks, measure the speed of the wind and snap amazing images from mast-mounted cameras, the process of analyzing soil images can be a somewhat daunting task, according to researchers from Louisiana State University (LSU).
After all, the university points out, many times there are several thousand images to analyze, and the soil particles are typically only five to 10 pixels wide. Now, however, a research team led by Suniti Karunatillake of the LSU Department of Geology and Geophysics has come to the rescue with a new algorithm that should make the task easier.
Karunatillake and colleagues from Rider University, Stony Brook University and the US Geological Survey (USGS) in Flagstaff, Arizona joined forces to create an image analysis and segmentation algorithm specifically to help NASA scientists complete this basic, but nonetheless challenging, part of their mission.
“Planetary scientists use images to identify the distribution of grain sizes of large-scale (centimeter or larger diameter) rocks and small-scale (less than 1 cm) grains,” the university explained in a statement. “These grain sizes tell scientists about the processes that distributed the particles from their source regions to where they are now. For example, were they derived from a water source, blown by wind, or show hydrodynamic sorting?”
The algorithm has been implemented in Mathematica, a computational software program used in several different scientific, mathematical, computing or engineering-related fields. It reportedly uses a variety of different image processing steps to first segment the image into foreground and background grains, then continues the process until nearly all coarser and finer grains are outlined, the researchers explained.
“The code processes a single image within 1 to 5 minutes,” LSU officials said. “The semi-automated algorithm, while comparing favorably with manual (human) segmentation, provides better consistency across multiple images than a human. The researchers are exploring the use of this algorithm to quantify grain sizes in the images from the Mars Exploration Rovers Microscopic Imager (MI) as well as Curiosity’s Mars Hand Lens Imager (MAHLI).”
“The grain size distributions identified in those images have the potential to reveal subtle trends with composition not considered previously,” they added. “Ability to identify most of the grains in images also makes detailed, area-weighted, sedimentology possible. Applications extend to terrestrial data from less accessible sites such as deep lake basins or undisturbed river bed sediments.”
On November 7, the Curiosity rover experienced an unexpected software reboot (also known as a warm reset) during a communications pass with the Mars Reconnaissance Orbiter, according to NASA. Following that incident, the rover spent a few days in safe mode before being successfully transitioned back into nominal surface operations mode on Sunday. Curiosity science operations resumed on Thursday.