Automated exploration of the radio plasma imager data

Ivan Galkin, Bodo Reinisch, Georges Grinstein, Grigori Khmyrov, Alexander Kozlov, 
Xueqin Huang, Shing Fung


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 ÒCORPRALÓ
(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.

_______________
Journal of Geophysical Research, 109, A12210, doi:10.1029/2004JA010439, 2004