Unsupervised Change Detection via Hierarchical Support Vector Clustering
Details
State: Public
Version: author
Serval ID
serval:BIB_1FC08A9B0F04
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Unsupervised Change Detection via Hierarchical Support Vector Clustering
Title of the conference
ICPR 2012, Pattern Recognition Remote Sensing Workshop
Address
Tsukuba, Japan, November 11-15, 2012
Publication state
Published
Issued date
2012
Language
english
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.
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.
Keywords
Cluster Validity measure, merging system, remote, sensing, entire solution path, nested SVM, LTS5
Create date
06/01/2014 22:07
Last modification date
20/08/2019 13:55
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