Unsupervised change detection via hierarchical support vector clustering

Frank De Morsier*, Devis Tuia, Volker Gass, Jean Philippe Thiran, Maurice Borgeaud

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paper

4 Citations (Scopus)

Abstract

When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of nested hierarchical support vector clustering descriptions, select the appropriate one using a cluster validity measure and finally merge the clusters into two classes, corresponding to changed and unchanged areas. Experiments on two multispectral datasets confirm the power and appropriateness of the proposed system.

Original languageEnglish
Title of host publication2012 IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2012
ISBN (Electronic)9781467349628
DOIs
Publication statusPublished - Dec 2012
Externally publishedYes
Event2012 IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2012 - Tsukuba Science City, Japan
Duration: 11 Nov 201211 Nov 2012

Publication series

Name2012 IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2012

Conference

Conference2012 IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2012
CountryJapan
CityTsukuba Science City
Period11/11/1211/11/12

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