Enhanced change detection using nonlinear feature extraction

Michele Volpi*, Giona Matasci, Devis Tuia, Mikhail Kanevski

*Corresponding author for this work

Research output: Contribution to conferenceConference paper

1 Citation (Scopus)

Abstract

This paper presents an application of the kernel principal component analysis aiming at spectrally aligning optical images before the application of change detection techniques. The approach relies on the extraction of nonlinear features from a selected subset of pixels representing unchanged areas in the bi-temporal images. Both images are then projected into the new space defined by the eigenvectors associated to largest variance (eigenvalues). In the transformed space, unchanged pixels are mapped next to each other, thus reducing within-class variance. The difference image that results from subtracting the projected datasets is likely to provide a more suitable representation for detecting changes. A subset of two Landsat TM scenes validates the proposed approach. The new representation is studied thanks to the change vector analysis and to the support vector domain description.

Original languageEnglish
Pages6757-6760
Number of pages4
DOIs
Publication statusPublished - Dec 2012
Externally publishedYes
Event2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Germany
Duration: 22 Jul 201227 Jul 2012

Conference

Conference2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
CountryGermany
CityMunich
Period22/07/1227/07/12

Keywords

  • Change detection
  • Image alignment
  • Kernel PCA
  • Nonlinear feature extraction
  • Preprocessing

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  • Cite this

    Volpi, M., Matasci, G., Tuia, D., & Kanevski, M. (2012). Enhanced change detection using nonlinear feature extraction. 6757-6760. Paper presented at 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012, Munich, Germany. https://doi.org/10.1109/IGARSS.2012.6352554