NDLI: A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Content Provider	IEEE Xplore Digital Library
Author	Tuia, D. Volpi, M. Copa, L. Kanevski, M. Munoz-Mari, J.
Copyright Year	2007
Abstract	Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.
Sponsorship	IEEE Signal Processing Society
Starting Page	606
Ending Page	617
Page Count	12
File Size	1566688
File Format	PDF
ISSN	19324553
Volume Number	5
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pixel Support vector machines Training Uncertainty Remote sensing Entropy Machine learning very high resolution (VHR) active learning hyperspectral image classification training set definition support vector machine (SVM)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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