Study of Effectiveness of Support Vector Machines for Multispectral Data
نویسندگان
چکیده
-Land use classification is an important part of many remote-sensing applications. A lot of research has gone into the application of classifiers to remote-sensing images. Multi-spectral satellite imagery is an economical, precise and appropriate method of obtaining information on land use and land cover. In this paper, we have proposed an efficient technique for classifying the multispectral satellite images using SVM into land cover and land use sectors. Support vector machines (SVMs) have recently been introduced into machine learning for pattern recognition and image classification. Results show that the SVM performs better than parallelepiped and maximum likelihood, even with small training data sets, and is almost unaffected by the Hughes phenomenon. Keywords--Classification, Parallelepiped, Maximum likelihood, Support vector machines, multispectral data.
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