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Empirical Inference Conference Paper 2009

Active learning for classification of remote sensing images

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Empirical Inference
Claudio Persello

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.

Author(s): Bruzzone, L. and Persello, C.
Links:
Pages: III-693-III-696
Year: 2009
Month: July
Day: 0
Publisher: IEEE
Bibtex Type: Conference Paper (inproceedings)
Address: Piscataway, NJ, USA
DOI: 10.1109/IGARSS.2009.5417857
Event Name: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009)
Event Place: Cape Town, South Africa
Digital: 0
Electronic Archiving: grant_archive
ISBN: 978-1-4244-3394-0

BibTex 

@inproceedings{BruzzoneP2009_2,
  title = {Active learning for classification of remote sensing images },
  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.},
  pages = {III-693-III-696 },
  publisher = {IEEE},
  address = {Piscataway, NJ, USA},
  month = jul,
  year = {2009},
  slug = {bruzzonep2009_2},
  author = {Bruzzone, L. and Persello, C.},
  month_numeric = {7}
}