000181794 001__ 181794
000181794 005__ 20190812205646.0
000181794 0247_ $$2doi$$a10.1109/PPRS.2012.6398309
000181794 037__ $$aCONF
000181794 245__ $$aUnsupervised Change Detection via Hierarchical Support Vector Clustering
000181794 269__ $$a2012
000181794 260__ $$c2012
000181794 336__ $$aConference Papers
000181794 520__ $$aWhen 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.
000181794 6531_ $$aCluster Validity measure
000181794 6531_ $$amerging system
000181794 6531_ $$aremote sensing
000181794 6531_ $$aentire solution path
000181794 6531_ $$anested SVM
000181794 6531_ $$aLTS5
000181794 700__ $$0242940$$g166738$$aDe Morsier, Frank
000181794 700__ $$0245927$$g150680$$aTuia, Devis
000181794 700__ $$0245002$$g167538$$aBorgeaud, Maurice
000181794 700__ $$aGass, Volker$$g217963$$0246190
000181794 700__ $$aThiran, Jean-Philippe$$g115534$$0240323
000181794 7112_ $$dNovember 11-15, 2012$$cTsukuba, Japan$$aPattern Recognition Remote Sensing Workshop (ICPR 2012)
000181794 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/181794/files/NHSVC.pdf$$s2074902
000181794 909C0 $$xU11066$$pCTS$$0252355
000181794 909C0 $$pLASIG$$0252045$$xU10244
000181794 909C0 $$xU10954$$pLTS5$$0252394
000181794 909CO $$qGLOBAL_SET$$pconf$$pSTI$$pENAC$$ooai:infoscience.tind.io:181794
000181794 917Z8 $$x166738
000181794 917Z8 $$x166738
000181794 917Z8 $$x166738
000181794 917Z8 $$x166738
000181794 937__ $$aEPFL-CONF-181794
000181794 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000181794 980__ $$aCONF