Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions

Devis Tuia*, Rémi Flamary, Nicolas Courty

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

54 Citations (Scopus)


In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and their parameters using expert knowledge, we let the model find them within random draws in the (possibly infinite) space of possible filters. We define an active set feature learner that includes in the model only features that improve the classifier. To this end, we consider a fast and linear classifier, multiclass logistic classification, and show that with a good representation (the filters discovered), such a simple classifier can reach at least state of the art performances. We apply the proposed active set learner in four hyperspectral image classification problems, including agricultural and urban classification at different resolutions, as well as multimodal data. We also propose a hierarchical setting, which allows to generate more complex banks of features that can better describe the nonlinearities present in the data.

Original languageEnglish
Pages (from-to)272-285
Number of pages14
JournalISPRS Journal of Photogrammetry and Remote Sensing
Publication statusPublished - Jul 2015
Externally publishedYes


  • Active set
  • Deep learning
  • Feature selection
  • Hierarchical feature extraction
  • Hyperspectral imaging
  • Multimodal


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