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.


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    Titel :

    Onboard classifiers for science event detection on a remote sensing spacecraft


    Weitere Titelangaben:

    Anbord-Klassifizierer zum Erkennen von wissenschaftlich bedeutsamen Ereignissen in einem Fernerkundungsraumfahrzeug


    Beteiligte:
    Castano, Rebecca (Autor:in) / Tang, Nghia (Autor:in) / Doggett, Thomas (Autor:in) / Chien, Steve (Autor:in) / Mazzoni, Dominic (Autor:in) / Greeley, Ron (Autor:in) / Cichy, Ben (Autor:in) / Davies, Ashley (Autor:in)


    Erscheinungsdatum :

    2006


    Format / Umfang :

    7 Seiten, 5 Bilder, 4 Tabellen, 15 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Learning classifiers for science event detection in remote sensing imagery

    Castano, Rebecca / Mazzoni, Dominic / Tang, Nghia et al. | NTRS | 2005


    Learning Classifiers for Science Event Detection in Remote Sensing Imagery

    Castano, R. / Mazzoni, D. / Tang, N. et al. | British Library Conference Proceedings | 2005


    Science Benefits of Onboard Spacecraft Navigation

    Cangahuala, Al / Bhaskaran, Shyam / Owen, Bill | NTRS | 2012


    Onboard Navigation Support of Spacecraft RESURS-DK for Remote Sensing of the Earth

    Anshakov, G.P. / Manturov, A.I. / Mostovoy, Y.A. et al. | British Library Conference Proceedings | 2006


    Standardized Spacecraft Onboard Interfaces

    Smith, J. / Plummer, C. / Plancke, P. et al. | British Library Conference Proceedings | 2003