HR: 15:10h
AN: IN33D-07    [Abstracts]
TI: Data Prospecting with CORPRAL: Pre-attentive Vision Model at Work
AU: * Galkin, I A
EM: Ivan_Galkin@uml.edu
AF: University of Massachusetts Lowell, Center for Atmospheric Research, 600 Suffolk Street, Lowell, MA 01854 United States
AU: Reinisch, B W
EM: Bodo_Reinisch@uml.edu
AF: University of Massachusetts Lowell, Center for Atmospheric Research, 600 Suffolk Street, Lowell, MA 01854 United States
AU: Khmyrov, G M
EM: Grigori_Khmyrov@uml.edu
AF: University of Massachusetts Lowell, Center for Atmospheric Research, 600 Suffolk Street, Lowell, MA 01854 United States
AU: Kozlov, A V
EM: Alexndre_Kozlov@uml.edu
AF: University of Massachusetts Lowell, Center for Atmospheric Research, 600 Suffolk Street, Lowell, MA 01854 United States
AU: Grinstein, J
EM: jordangrin@hotmail.com
AF: University of Massachusetts Lowell, Center for Atmospheric Research, 600 Suffolk Street, Lowell, MA 01854 United States
AU: Fung, S F
EM: shing.f.fung@nasa.gov
AF: NASA Goddard Space Flight Center, Space Physics Data Facility, Code 632, Greenbelt, MD 20771 United States
AB: The Cognitive Online Rpi Plasmagram Rating Algorithm (CORPRAL) is the automated data prospecting tool developed to find interesting examples of signal propagation in the 1.2 million plasmagram image archive from the Radio Plasma Imager (RPI) onboard the IMAGE spacecraft. The RPI instrument is a spaceborne radar whose transmitted radio waves may reflect from important magnetospheric structures, such as the plasmapause and the magnetopause, and return to the spacecraft location to be detected. The CORPRAL prospector draws attention of the human analysts to the RPI plasmagrams that contain traces of remote reflections (~18% of all images), thus helping to relieve the search efforts in the otherwise overwhelming RPI data repository. To find the echo traces in plasmagrams, CORPRAL employs a pre-attentive vision model that replicates human ability to identify important objects in the field of view without willful concentration of attention. The importance of such objects is determined by their `saliency', the ability to stand out against the background. The saliency evaluation is done subconsciously, without a priori concepts of the object shape. The RPI imagery dataset providesd an excellent testbed for studies of the model performance on low saliency objects immersed in irregular background noise. The paper discusses results of statistical evaluation of the CORPRAL performance on a collection of ~25,000 plasmagram images interpreted manually. Overall accuracy of plasmagram interpretation varies depending on the applied measuring program but remains in the mid-90% range. As the prevalence of plasmagrams that contain traces goes below 10%, we do observe lower positive predicted values (PPV) of the CORPRAL analysis (~50%), indicating that automatically selected plasmagrams are interesting only with 50% chance. We will discuss future development efforts and provide examples and scenarios where RPI data prospecting is instrumental in knowledge discovery.
UR: http://car.uml.edu/rpi/corpral/
DE: 2700 MAGNETOSPHERIC PHYSICS (6939)
DE: 6994 Instruments and techniques (1241)
SC: Earth and Space Science Informatics [IN]
MN: Fall Meeting 2005