Application of ECOC SVMS in Remote Sensing Image Classification
Image processing has been one of the efficient technologies for GIS data requisition. Support Vector Machines (SVMs) have peculiar advantages in handling problems with small sample sizes, nonlinearity, and high dimensionality. However, SVMs can only solve two-class problems while multi-class decisio...
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2014-11-01
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Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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doaj-defc34fbb6cc4e819d17fb007668a1172020-11-24T23:04:22ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342014-11-01XL-219119610.5194/isprsarchives-XL-2-191-2014Application of ECOC SVMS in Remote Sensing Image ClassificationZ. Yan0Y. Yang1School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, Jiangsu, 221116 P.R.ChinaSchool of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, Jiangsu, 221116 P.R.ChinaImage processing has been one of the efficient technologies for GIS data requisition. Support Vector Machines (SVMs) have peculiar advantages in handling problems with small sample sizes, nonlinearity, and high dimensionality. However, SVMs can only solve two-class problems while multi-class decision is impossible. Error correcting output coding (ECOC) SVMs enhance the ability of fault tolerance when solving multi-class classification problems, which makes ECOC SVMs suitable for remote sensing image classification. In this paper, the generalization ability of ECOC SVMs is discussed. ECOC SVMs with optimum coding matrices are selected by experiment, and applied to remote sensing image classification. Experimental results show that, compared with Conventional multi-class classification methods, less SVM sub-classifiers are needed for ECOC SVMs in remote sensing image classification, and the classification accuracy is also improved.http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-2/191/2014/isprsarchives-XL-2-191-2014.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Z. Yan Y. Yang |
spellingShingle |
Z. Yan Y. Yang Application of ECOC SVMS in Remote Sensing Image Classification The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
Z. Yan Y. Yang |
author_sort |
Z. Yan |
title |
Application of ECOC SVMS in Remote Sensing Image Classification |
title_short |
Application of ECOC SVMS in Remote Sensing Image Classification |
title_full |
Application of ECOC SVMS in Remote Sensing Image Classification |
title_fullStr |
Application of ECOC SVMS in Remote Sensing Image Classification |
title_full_unstemmed |
Application of ECOC SVMS in Remote Sensing Image Classification |
title_sort |
application of ecoc svms in remote sensing image classification |
publisher |
Copernicus Publications |
series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
1682-1750 2194-9034 |
publishDate |
2014-11-01 |
description |
Image processing has been one of the efficient technologies for GIS data requisition. Support Vector Machines (SVMs) have
peculiar advantages in handling problems with small sample sizes, nonlinearity, and high dimensionality. However, SVMs can only
solve two-class problems while multi-class decision is impossible. Error correcting output coding (ECOC) SVMs enhance the ability
of fault tolerance when solving multi-class classification problems, which makes ECOC SVMs suitable for remote sensing image
classification. In this paper, the generalization ability of ECOC SVMs is discussed. ECOC SVMs with optimum coding matrices are
selected by experiment, and applied to remote sensing image classification. Experimental results show that, compared with
Conventional multi-class classification methods, less SVM sub-classifiers are needed for ECOC SVMs in remote sensing image
classification, and the classification accuracy is also improved. |
url |
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-2/191/2014/isprsarchives-XL-2-191-2014.pdf |
work_keys_str_mv |
AT zyan applicationofecocsvmsinremotesensingimageclassification AT yyang applicationofecocsvmsinremotesensingimageclassification |
_version_ |
1725630929483857920 |