Unsupervised change detection by kernel clustering

Michele Volpi*, Devis Tuia, Gustavo Camps-Valls, Mikhail Kanevski

*Corresponding author for this work

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

13 Citations (Scopus)


This paper presents a novel unsupervised clustering scheme to find changes in two or more coregistered remote sensing images acquired at different times. This method is able to find nonlinear boundaries to the change detection problem by exploiting a kernel-based clustering algorithm. The kernel k-means algorithm is used in order to cluster the two groups of pixels belonging to the 'change' and 'no change' classes (binary mapping). In this paper, we provide an effective way to solve the two main challenges of such approaches: i) the initialization of the clustering scheme and ii) a way to estimate the kernel function hyperparameter(s) without an explicit training set. The former is solved by initializing the algorithm on the basis of the Spectral Change Vector (SCV) magnitude and the latter is optimized by minimizing a cost function inspired by the geometrical properties of the clustering algorithm. Experiments on VHR optimal imagery prove the consistency of the proposed approach.

Original languageEnglish
Title of host publicationImage and Signal Processing for Remote Sensing XVI
Publication statusPublished - 22 Oct 2010
Externally publishedYes
EventImage and Signal Processing for Remote Sensing XVI - Toulouse, France
Duration: 20 Sep 201022 Sep 2010

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceImage and Signal Processing for Remote Sensing XVI


  • Clustering
  • Kernel k-means
  • Remote sensing
  • Unsupervised change detection
  • VHR imagery

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