NDLI: Remote sensing image segmentation using SVM with automatic selection for the kernel parameters

Content Provider	IEEE Xplore Digital Library
Author	Mohamed, R. El-Baz, A. Farag, A.
Copyright Year	2005
Description	Author affiliation: Comput. Vision & Image Process. Lab., Louisville Univ., KY, USA (Mohamed, R.; El-Baz, A.; Farag, A.)
Abstract	The kernel function plays a basic role in support vector machines (SVM) algorithms. This paper presents an automatic method for selecting the parameters of the Gaussian radial basis function (GRBF) kernel which is one of the commonly used forms in SVM regression algorithms. The proposed method uses the expectation maximization (EM) algorithm for the automatic selection. The SVM regression algorithm is used to solve two major problems in remote sensing image segmentation: the density estimation problem and the Markov random field modeling problem. The density estimation is used for class conditional probabilities in Bayesian setups. The MRF is used for region modeling in boosting the Bayesian image segmentation. The proposed algorithms are integrated in a framework for remote sensing image segmentation. Experimental evaluation of the proposed algorithms using synthetic data set and hyperspectral data set illustrates the outstanding performance of the proposed algorithms.
File Size	4270156
File Format	PDF
ISBN	0780392868
DOI	10.1109/ICIF.2005.1592026
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Remote sensing Image segmentation Support vector machines Kernel Probability Markov random fields Bayesian methods Covariance matrix Computer vision Image processing
Content Type	Text
Resource Type	Article

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