Active learning for detecting a spectrally variable subject in color infrared imagery

Authors:
Patricia G. Foschi;Huan Liu
Affiliations:
Romberg Tiburon Center for Environmental Studies, San Francisco State University, Tiburon, CA;Department of Computer Science and Engineering, Arizona State University, Tempe, AZ
Venue:
Pattern Recognition Letters - Special issue: Pattern recognition for remote sensing (PRRS 2002)
Year:
2004

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Abstract

To classify Egeria densa, Brazilian waterweed, in scan-digitized color infrared aerial photographs, we are developing an interactive computer system based on data-mining techniques with active learning capabilities. Key components of the system are: feature extraction, automatic classification, active learning, and experimental evaluation.