Typically, data collected by a spacecraft is downlinked to earth and pre-processed before any analysis is performed. Classifiers were developed 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. The cryosphere is the component on the surface of a planetary body composed of ice. The ice may exist in a variety of forms including snow, permafrost, floating ice, and glaciers. The cryosphere dynamically interacts with the atmosphere and can significantly affect the climate on a planetary body. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree (DT) 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 DT and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier. The manual and SVM classifiers have been uploaded to the EO-1 spacecraft and have been running onboard the spacecraft for over a year. Results of the onboard analysis are used by the Autonomous Sciencecraft Experiment (ASE) of NASA's New Millennium Program (NMP) onboard EO-1 to automatically target the spacecraft to collect follow-on imagery. The software demonstrates the potential for future deep space missions to use onboard decision making to capture short-lived science events.
Onboard classifiers for science event detection on a remote sensing spacecraft
Anbord-Klassifizierer zum Erkennen von wissenschaftlich bedeutsamen Ereignissen in einem Fernerkundungsraumfahrzeug
2006
7 Seiten, 5 Bilder, 4 Tabellen, 15 Quellen
Conference paper
English
Bandbreite (Frequenz) , Betriebserfahrung , Bilderkennung , Bildklassifikation , Bildpunkt , Bildübertragung , Bildverarbeitungssystem , Datenanalyse , Datengewinnung , Datenübertragung , Eis , Entscheidungsbaum , Entscheidungsfindung , Fernerkundung , Forschungssatellit , Klassierer , Klassifikationssystem , Konferenzbericht , Priorität , Raumsonde , Rechnerleistung , Satellitenrechner , Satellitenübertragung , Signalanalyse , systematisches Suchen , Systemvergleich , Überflutung , Vulkanausbruch , Weltraum , Wissensverarbeitung
Learning Classifiers for Science Event Detection in Remote Sensing Imagery
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