Non-linear low-rank and sparse representation for hyperspectral image analysis

Frank De Morsier, Devis Tuia, Maurice Borgeaucft, Volker Gass, Jean Philippe Thiran

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

1 Citation (Scopus)

Abstract

In this paper, we tackle the problem of unsupervised classification of hyperspectral images. We propose a clustering method based on graphs representing the data structure, which is assumed to be an union of multiple manifolds. The method constraints the pixels to be expressed as a low-rank and sparse combination of the others in a reproducing kernel Hilbert spaces (RKHS). This captures the global (low-rank) and local (sparse) structures. Spectral clustering is applied on the graph to assign the pixels to the different manifolds. A large scale approach is proposed, in which the optimization is first performed on a subset of the data and then it is applied to the whole image using a non-linear collaborative representation respecting the manifolds structure. Experiments on two hyperspectral images show very good unsupervised classification results compared to competitive approaches.

Original languageEnglish
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4648-4651
Number of pages4
ISBN (Print)9781479957750
DOIs
Publication statusPublished - 4 Nov 2014
Externally publishedYes
EventJoint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 - Quebec City, Canada
Duration: 13 Jul 201418 Jul 2014

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

ConferenceJoint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
CountryCanada
CityQuebec City
Period13/07/1418/07/14

Keywords

  • classification
  • kernel
  • low-rank
  • manifold clustering
  • sparse
  • unsupervised

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