Autonomous Identification of Carbonates Using Near-IR Reflectance Spectra During the February 1999 Marsokhod Field Tests

Details Autonomous Identification of Carbonates Using Near-IR Reflectance Spectra During the February 1999 Marsokhod Field Tests Autonomous Identification of Carbonates Using Near-IR Reflectance Spectra During the February 1999 Marsokhod Field Tests

Sep 1999

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999dps....31.0814r&link_type=abstract

American Astronomical Society, DPS meeting #31, #08.14

Computer Science

Performance

Scientific paper

Future planetary missions face severe constraints on communications bandwidth. This is particularly true of rover missions, where data acquired during a traverse could rapidly overwhelm any conceivable communications link. One approach to addressing this problem would be to develop autonomous systems that can perform some preliminary science analysis onboard to identify, prioritize, and flag interesting data for return to Earth. Such a capability would enable the rover to perform some decision-making that potentially enhances the ultimate science return from a mission. As an initial step toward the independent rover scientific analysis we have developed an autonomous system to identify the presence of carbonates in near-IR reflectance spectra (0.35-2.5 mu m). This system consists of a set of feature-extraction algorithms that operate in conjunction with a rule-based system to identify carbonates based on the presence and characteristics of absorption bands near 2.33 and 2.5 mu m. This system was tested during the February 1999 Marsokhod field operations. A field portable spectrometer was used to acquire measurements of reflected sunlight from a variety of targets over a wide range of viewing geometries and distances. When appropriate, the autonomous system successfully recognized all the noisy spectra and performed no further evaluation. Among those that it did evaluate for carbonates, the system had a success rate of 80% compared with the performance of the field science team, a success rate of 82-90% compared with the performance of the remote science team, and a false positive rate of 0%. This high level of performance is particularly encouraging given the simplicity of the rule set and could likely be improved by suitable modifications of the inference rules. In the future we intend to evaluate additional algorithms and methods that might be employed for autonomous data analysis and subsequent selection or prioritization onboard future planetary and space missions.

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