Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft

Castano, Rebecca; Mazzoni, Dominic; Tang, Nghia; Greeley, Ron; Doggett, Thomas; Cichy, Ben; Chien, Steve; Davies, Ashley

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NASA NTRS · 20070011543

Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft

Castano, Rebecca·Mazzoni, Dominic·Tang, Nghia·Greeley, Ron·Doggett, Thomas·Cichy, Ben·Chien, Steve·Davies, Ashley

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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.

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classification cyrosphere machine learning Support Vector Machine (SVM)feature detection

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Castano, Rebecca, Mazzoni, Dominic, Tang, Nghia, Greeley, Ron, Doggett, Thomas, Cichy, Ben, Chien, Steve, Davies, Ashley. 2006-08-20. Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft. https://ntrs.nasa.gov/citations/20070011543

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