Unsupervised alignment of image manifolds with centrality measures

Devis Tuia, Michele Volpi, Gustau Camps-Valls

Research output: Chapter in Book/Report/Conference proceedingConference paper

4 Citations (Scopus)


The re-use of available labeled samples to classify newly acquired data is a hot topic in pattern analysis and machine learning. Classification algorithms developed with data from one domain cannot be directly used in another related domain, unless the data representation or the classifier have been adapted to the new data distribution. This is crucial in satellite/airborne image analysis: when confronted to domain shifts issued from changes in acquisition or illumination conditions, image classifiers tend to become inaccurate. In this paper, we introduce a method to align data manifolds that represent the same land cover classes, but have undergone spectral distortions. The proposed method relies on a semi-supervised manifold alignment technique and relaxes the requirement of labeled data in all domains by exploiting centrality measures over graphs to match the manifolds. Experiments on multispectral pixel classification at very high spatial resolution show the potential of the method.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781479952083
Publication statusPublished - 4 Dec 2014
Externally publishedYes
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference22nd International Conference on Pattern Recognition, ICPR 2014


  • Centrality measures
  • Graph analysis
  • Manifold alignment
  • Remote sensing
  • Very high resolution

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