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  Active learning for classification of remote sensing images

Bruzzone, L., & Persello, C. (2009). Active learning for classification of remote sensing images. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009) (pp. III-693-III-696). Piscataway, NJ, USA: IEEE.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C3FB-C Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C3FC-A
Genre: Conference Paper

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Bruzzone, L, Author
Persello, C1, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: This paper presents an analysis of active learning techniques for the classification of remote sensing images and proposes a novel active learning method based on support vector machines (SVMs). The proposed method exploits a query function for the inclusion of batches of unlabeled samples in the training set, which is based on the evaluation of two criteria: uncertainty and diversity. This query function adopts a stochastic approach to the selection of unlabeled samples, which is based on a function of uncertainty estimated from the distribution of errors on the validation set (which is assumed available for the model selection of the SVM classifier). Experimental results carried out on a very high resolution image confirm the effectiveness of the proposed active learning technique, which results more accurate than standard methods.

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 Dates: 2009-07
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 978-1-4244-3394-0
DOI: 10.1109/IGARSS.2009.5417857
BibTex Citekey: BruzzoneP2009_2
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Title: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009)
Place of Event: Cape Town, South Africa
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Title: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009)
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: III-693-III-696 Identifier: -