NDLI: Improving onboard analysis of Hyperion images by filtering mislabeled training data examples

Content Provider	IEEE Xplore Digital Library
Author	Rebbapragada, U. Mandrake, L. Wagstaff, K.L. Gleeson, D. Castano, R. Chien, S. Brodley, C.E.
Copyright Year	2009
Description	Author affiliation: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, 91109, USA (Mandrake, L.; Wagstaff, K.L.; Gleeson, D.; Castano, R.; Chien, S.) \|\| Dept. of Computer Science, 161 College Ave., Medford, MA 02155, USA (Rebbapragada, U.; Brodley, C.E.)
Abstract	This paper presents PWEM, a technique for detecting class label noise in training data. PWEM detects mislabeled examples by assigning to each training example a probability that its label is correct. PWEM calculates this probability by clustering examples from pairs of classes together and analyzing the distribution of labels within each cluster to derive the probability of each label's correctness. We discuss how one can use the probabilities output by PWEM to filter, mitigate, or correct mislabeled training examples. We then provide an in-depth discussion of how we applied PWEM to a sulfur detector that labels pixels from Hyperion images of the Borup-Fiord pass in Northern Canada. PWEM assigned a large number of the sulfur training examples low probabilities, indicating severe mislabeling within the sulfur class. The filtering of those low confidence examples resulted in a cleaner training set and improved the median false positive rate of the classifier by at least 29%.
Starting Page	1
Ending Page	9
File Size	1128120
Page Count	9
File Format	PDF
ISBN	9781424426218
ISSN	1095323X
DOI	10.1109/AERO.2009.4839580
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image analysis Filtering Training data Probability Clustering algorithms Filters Detectors Pixel Humans Labeling
Content Type	Text
Resource Type	Article
Subject	Aerospace Engineering Space and Planetary Science

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