Automatic feature learning for spatio-spectral image classification with sparse SVM

Devis Tuia, Michele Volpi, Mauro Dalla Mura, Alain Rakotomamonjy, Rémi Flamary

Research output: Contribution to journalArticleAcademicpeer-review

56 Citations (Scopus)


Including spatial information is a key step for successful remote sensing image classification. In particular, when dealing with high spatial resolution, if local variability is strongly reduced by spatial filtering, the classification performance results are boosted. In this paper, we consider the triple objective of designing a spatial/spectral classifier, which is compact (uses as few features as possible), discriminative (enhances class separation), and robust (works well in small sample situations). We achieve this triple objective by discovering the relevant features in the (possibly infinite) space of spatial filters by optimizing a margin-maximization criterion. Instead of imposing a filter bank with predefined filter types and parameters, we let the model figure out which set of filters is optimal for class separation. To do so, we randomly generate spatial filter banks and use an active-set criterion to rank the candidate features according to their benefits to margin maximization (and, thus, to generalization) if added to the model. Experiments on multispectral very high spatial resolution (VHR) and hyperspectral VHR data show that the proposed algorithm, which is sparse and linear, finds discriminative features and achieves at least the same performances as models using a large filter bank defined in advance by prior knowledge.

Original languageEnglish
Article number6708428
Pages (from-to)6062-6074
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number10
Publication statusPublished - Oct 2014
Externally publishedYes


  • Attribute profiles
  • feature selection
  • hyperspectral
  • mathematical morphology
  • texture
  • very high resolution

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