Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 566190
Title Active learning: Any value for classification of remotely sensed data?
Author(s) Crawford, Melba M.; Tuia, Devis; Yang, Hsiuhan Lexie
Source Proceedings of the IEEE 101 (2013)3. - ISSN 0018-9219 - p. 593 - 608.
DOI https://doi.org/10.1109/JPROC.2012.2231951
Publication type Refereed Article in a scientific journal
Publication year 2013
Keyword(s) Active learning - adaptation - classification - high-resolution multispectral - hyperspectral - multiview - spatial learning - support vector machines (SVMs)
Abstract

Active learning, which has a strong impact on processing data prior to the classification phase, is an active research area within the machine learning community, and is now being extended for remote sensing applications. To be effective, classification must rely on the most informative pixels, while the training set should be as compact as possible. Active learning heuristics provide capability to select unlabeled data that are the 'most informative' and to obtain the respective labels, contributing to both goals. Characteristics of remotely sensed image data provide both challenges and opportunities to exploit the potential advantages of active learning. We present an overview of active learning methods, then review the latest techniques proposed to cope with the problem of interactive sampling of training pixels for classification of remotely sensed data with support vector machines (SVMs). We discuss remote sensing specific approaches dealing with multisource and spatially and time-varying data, and provide examples for high-dimensional hyperspectral imagery.

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