EarthRef.org Reference Database (ERR)
Development and Maintenance by the EarthRef.org Database Team

Detailed Reference Information
Galkin et al. 2004
Galkin, I., Reinisch, B., Grinstein, G., Khmyrov, G., Kozlov, A., Huang, X. and Fung, S. (2004). Automated exploration of the radio plasma imager data. Journal of Geophysical Research 109: doi: 10.1029/2004JA010439. issn: 0148-0227.

As research instruments with large information capacities become a reality, automated systems for intelligent data analysis become a necessity. Scientific archives containing huge volumes of data preclude manual manipulation or intervention and require automated exploration and mining that can at least preclassify information in categories. The large data set from the radio plasma imager (RPI) instrument on board the IMAGE satellite shows a critical need for such exploration in order to identify and archive features of interest in the volumes of visual information. In this research we have developed such a preclassifier through a model of preattentive vision capable of detecting and extracting traces of echoes from the RPI plasmagrams. The overall design of our model complies with Marr's paradigm of vision, where elements of increasing perceptual strength are built bottom up under the Gestalt constraints of good continuation and smoothness. The specifics of the RPI data, however, demanded extension of this paradigm to achieve greater robustness for signature analysis. Our preattentive model now employs a feedback neural network that refines alignment of the oriented edge elements (edgels) detected in the plasmagram image by subjecting them to collective global-scale optimization. The level of interaction between the oriented edgels is determined by their distance and mutual orientation in accordance with the Yen and Finkel model of the striate cortex that encompasses findings in psychophysical studies of human vision. The developed models have been implemented in an operational system CORPRAL (Cognitive Online RPI Plasmagram Ranking Algorithm) that currently scans daily submissions of the RPI plasmagrams for the presence of echo traces. Qualifying plasmagrams are tagged in the mission database, making them available for a variety of queries. We discuss CORPRAL performance and its impact on scientific analysis of RPI data.

BACKGROUND DATA FILES

Abstract

Keywords
Magnetospheric Physics, Instruments and techniques, Radio Science, Instruments and techniques, Space Plasma Physics, Instruments and techniques, Radio Science, Signal processing, radio plasma imager, automated, discovery, exploration, intelligent systems, magnetosphere
Journal
Journal of Geophysical Research
http://www.agu.org/journals/jb/
Publisher
American Geophysical Union
2000 Florida Avenue N.W.
Washington, D.C. 20009-1277
USA
1-202-462-6900
1-202-328-0566
service@agu.org
Click to clear formClick to return to previous pageClick to submit