Supervised change detection in VHR images using contextual information and support vector machines

Michele Volpi*, Devis Tuia, Francesca Bovolo, Mikhail Kanevski, Lorenzo Bruzzone

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

143 Citations (Scopus)


In this paper we study an effective solution to deal with supervised change detection in very high geometrical resolution (VHR) images. High within-class variance as well as low between-class variance that characterize this kind of imagery make the detection and classification of ground cover transitions a difficult task. In order to achieve high detection accuracy, we propose the inclusion of spatial and contextual information issued from local textural statistics and mathematical morphology. To perform change detection, two architectures, initially developed for medium resolution images, are adapted for VHR: Direct Multi-date Classification and Difference Image Analysis. To cope with the high intra-class variability, we adopted a nonlinear classifier: the Support Vector Machines (SVM). The proposed approaches are successfully evaluated on two series of pansharpened QuickBird images.

Original languageEnglish
Pages (from-to)77-85
Number of pages9
JournalInternational Journal of applied Earth Observation and Geoinformation
Issue number1
Publication statusPublished - Feb 2013
Externally publishedYes


  • Change detection
  • Graylevel co-occurrence matrix
  • Mathematical morphology
  • Support vector machines
  • Very high resolution

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