DOI: 10.1002/grl.50817
论文题名: Smart, texture-sensitive instrument classification for in situ rock and layer analysis
作者: Wagstaff K.L. ; Thompson D.R. ; Abbey W. ; Allwood A. ; Bekker D.L. ; Cabrol N.A. ; Fuchs T. ; Ortega K.
刊名: Geophysical Research Letters
ISSN: 0094-8648
EISSN: 1944-8379
出版年: 2013
卷: 40, 期: 16 起始页码: 4188
结束页码: 4193
语种: 英语
英文关键词: classification
; onboard analysis
Scopus关键词: Future spacecraft
; Image Classifiers
; Mars Exploration Rover
; Onboard analysis
; Random forest classifier
; Science missions
; Smart instruments
; Surface materials
; Classification (of information)
; Decision trees
; Field programmable gate arrays (FPGA)
; Instruments
; Textures
; array
; classification
; data set
; in situ measurement
; instrumentation
; machinery
; Mars
; pixel
; Emmelichthyidae
英文摘要: 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. Key Points Smart instruments can analyze their own data for science investigations Random forest classifiers can effectively address texture-based image tasks Future spacecraft can train and deploy their own image classifiers. © 2013. American Geophysical Union. All Rights Reserved. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84882737914&doi=10.1002%2fgrl.50817&partnerID=40&md5=a32b0e2713e116230043158c35f35a6f
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/5912
Appears in Collections: 气候减缓与适应
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作者单位: Machine Learning and Instrument Autonomy, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Recommended Citation:
Wagstaff K.L.,Thompson D.R.,Abbey W.,et al. Smart, texture-sensitive instrument classification for in situ rock and layer analysis[J]. Geophysical Research Letters,2013-01-01,40(16).