Unsupervised change detection with kernels

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

73 Citations (Scopus)

Abstract

In this letter, an unsupervised kernel-based approach to change detection is introduced. Nonlinear clustering is utilized to partition in two a selected subset of pixels representing both changed and unchanged areas. Once the optimal clustering is obtained, the learned representatives of each group are exploited to assign all the pixels composing the multitemporal scenes to the two classes of interest. Two approaches based on different assumptions of the difference image are proposed. The first accounts for the difference image in the original space, while the second defines a mapping describing the difference image directly in feature spaces. To optimize the parameters of the kernels, a novel unsupervised cost function is proposed. An evidence of the correctness, stability, and superiority of the proposed solution is provided through the analysis of two challenging change-detection problems.

Original languageEnglish
Article number6178771
Pages (from-to)1026-1030
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume9
Issue number6
DOIs
Publication statusPublished - 10 Apr 2012
Externally publishedYes

Keywords

  • Composite kernels
  • kernel k-means
  • kernel parameters
  • unsupervised change detection

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