NDLI: Critical class oriented active learning for hyperspectral image classification

Content Provider	IEEE Xplore Digital Library
Author	Wei Di Crawford, M.M.
Copyright Year	2011
Description	Author affiliation: School of Civil Engineering, 2Computational Science & Engineering, Purdue University, West Lafayette, IN 47907, USA (Wei Di; Crawford, M.M.)
Abstract	In order to focus on the hard classes in a multi-class classification task, a critical class oriented query strategy is proposed, which combines the concepts of “guided learning” and “active learning”. In conjunction with the SVM classifier, hard pair classes are first identified based on the instability of the classification hyperplane, whereby category level guidance for which class should be queried next is sought and then provided to the active query system. Samples with higher possibility of belonging to these classes as evaluated by the current learner are queried first. Two methods are proposed. The first method (SVM-CC) simply conducts category level query. The second method (SVM-CCMS) further incorporates the uncertainty measurement based on the idea of margin sampling, so as to directly focus on the most informative samples from the identified “trouble classes”. Experiments are conducted on AVIRIS and Hyperion data. Results are compared to Random Sampling and the state-of-the-art active learning method SVM based simple margin sampling SVMMS. Superior performance is obtained, whereas hard classes are successfully identified first.
Starting Page	3899
Ending Page	3902
File Size	325644
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6050083
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Hyperspectral imaging Accuracy Uncertainty Measurement uncertainty critical class Hyperspectral data classification active learning guided learning
Content Type	Text
Resource Type	Article

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