Active learning for classification of remote sensing images

2009

Conference Paper

ei

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.

@inproceedings{BruzzoneP2009_2,
  title = {Active learning for classification of remote sensing images },
  author = {Bruzzone, L. and Persello, C.},
  pages = {III-693-III-696 },
  publisher = {IEEE},
  address = {Piscataway, NJ, USA},
  month = jul,
  year = {2009},
  doi = {10.1109/IGARSS.2009.5417857  },
  month_numeric = {7}
}