Multi-sensor change detection based on nonlinear canonical correlations

Michele Volpi, Frank De Morsier, Gustavo Camps-Valls, Mikhail Kanevski, Devis Tuia

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

8 Citations (Scopus)

Abstract

The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of such datasets. However, for change detection existing methods are often unable to deal with heterogeneous image sources and very few consider possible nonlinearities in the data. Additionally, the availability of labeled information is very limited in change detection applications. For these reasons, we present the use of a semi-supervised kernel-based feature extraction technique. It incorporates a manifold regularization accounting for the geometric distribution and jointly addressing the small sample problem. An exhaustive example using Landsat 5 data illustrates the potential of the method for multi-sensor change detection.

Original languageEnglish
Title of host publication2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
Pages1944-1947
Number of pages4
DOIs
Publication statusPublished - Dec 2013
Externally publishedYes
Event2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Melbourne, VIC, Australia
Duration: 21 Jul 201326 Jul 2013

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
CountryAustralia
CityMelbourne, VIC
Period21/07/1326/07/13

Keywords

  • Change detection
  • Feature extraction
  • Multi-sensor
  • Multimodal
  • Radiometric normalization

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