Urban image classification with semisupervised multiscale cluster kernels

Details

Serval ID
serval:BIB_053C0DD49B64
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Urban image classification with semisupervised multiscale cluster kernels
Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Author(s)
Tuia D., Camps-Valls G.
ISSN-L
1939-1404
Publication state
Published
Issued date
2010
Peer-reviewed
Oui
Volume
4
Pages
65-74
Language
english
Notes
Tuia2010a
Abstract
This paper presents a semisupervised support vector machine (SVM)
that integrates the information of both labeled and unlabeled pixels
efficiently. Method's performance is illustrated in the relevant
problem of very high resolution image classification of urban areas.
The SVM is trained with the linear combination of two kernels: a
base kernel working only with labeled examples is deformed by a likelihood
kernel encoding similarities between labeled and unlabeled examples.
Results obtained on very high resolution (VHR) multispectral and
hyperspectral images show the relevance of the method in the context
of urban image classification. Also, its simplicity and the few parameters
involved make the method versatile and workable by unexperienced
users.
Create date
25/11/2013 17:18
Last modification date
20/08/2019 12:27
Usage data