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Smart, texture‐sensitive instrument classification for in situ rock and layer analysis
Author(s) -
Wagstaff K. L.,
Thompson D. R.,
Abbey W.,
Allwood A.,
Bekker D. L.,
Cabrol N. A.,
Fuchs T.,
Ortega K.
Publication year - 2013
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/grl.50817
Subject(s) - computer science , classifier (uml) , mars exploration program , field programmable gate array , mars rover , artificial intelligence , remote sensing , pixel , exploration of mars , real time computing , pattern recognition (psychology) , embedded system , geology , astrobiology , physics
Science missions have limited lifetimes, necessitating an efficient investigation of the field site. The efficiency of onboard cameras, critical for planning, is limited by the need to downlink images to Earth for every decision. Recent advances have enabled rovers to take follow‐up actions without waiting hours or days for new instructions. We propose using built‐in processing by the instrument itself for adaptive data collection, faster reconnaissance, and increased mission science yield. We have developed a machine learning pixel classifier that is sensitive to texture differences in surface materials, enabling more sophisticated onboard classification than was previously possible. This classifier can be implemented in a Field Programmable Gate Array (FPGA) for maximal efficiency and minimal impact on the rest of the system's functions. In this paper, we report on initial results from applying the texture‐sensitive classifier to three example analysis tasks using data from the Mars Exploration Rovers.