SVM-based boosting of active learning strategies for efficient domain adaptation

Giona Matasci*, Devis Tuia, Mikhail Kanevski

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

41 Citations (Scopus)


We propose a procedure that efficiently adapts a classifier trained on a source image to a target image with similar spectral properties. The adaptation is carried out by adding new relevant training samples with active queries in the target domain following a strategy specifically designed for the case where class distributions have shifted between the two acquisitions. In fact, the procedure consists of two nested algorithms. An active selection of the pixels to be labeled is performed on a set of candidates of the target image in order to select the most informative pixels. Along the inclusion of the pixels to the training set, the weights associated with these samples are iteratively updated using different criteria, depending on their origin (source or target image). We study this adaptation framework in combination with a SVM classifier accepting instance weights. Experiments on two VHR QuickBird images and on a hyperspectral AVIRIS image prove the validity of the proposed adaptive approach with respect to existing techniques not involving any adjustments to the target domain.

Original languageEnglish
Article number6353565
Pages (from-to)1335-1343
Number of pages9
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number5
Publication statusPublished - 27 Nov 2012
Externally publishedYes


  • Active learning
  • domain adaptation
  • image classification
  • instance weights
  • SVM
  • TrAdaBoost

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