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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2014/39811
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| Title: | Onboard classifiers for science event detection on a remote sensing spacecraft |
| Authors: | Castano, Rebecca
Mazzoni, Dominic
Tang, Nghia
Greeley, Ron
Doggett, Thomas
Cichy, Ben
Chien, Steve
Davies, Ashley |
| Keywords: | feature detection
Support Vector Machine (SVM)
cyrosphere
machine learning
classification |
| Issue Date: | 20-Aug-2006 |
| Publisher: | Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2006. |
| Citation: | Knowledge Discovery and Data Mining, Philadelphia, Pennsylvania, August 20-23, 2006. |
| Abstract: | Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up. Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating
environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier. |
| URI: | http://hdl.handle.net/2014/39811 |
| Appears in Collections: | JPL TRS 1992+
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