NDLI: PSO Parameters Optimization Based Support Vector Machines for Hyperspectral Classification

Content Provider	IEEE Xplore Digital Library
Author	Sheng Ding Shunxin Li
Copyright Year	2009
Abstract	This study investigates a new approach in hyperspectral image classification. The new method of hyperspectral classification in this paper is a new SVM algorithm based particle swarm optimization (PSO-SVM). First, three classification methods are used to classified a benchmark hyperspectral images: SVM, ML (maximum-likelihood) and K-nn (K-nearest neighbor), the performances of SVMs are compared with two other traditional classifiers (maximum-likelihood classifier and the K-nearest neighbor classifier). The study indicates that the classification accuracy of SVM algorithm is better than ML and K-nn algorithms. The over accuracy of the SVM using RBF kernel is above 90% and the over accuracy of the traditional methods is below 81%. The kernel parameters setting for SVM in a training process impacts on the classification accuracy, to select accurate parameters of the RBF kernel function, we present a SVM algorithm based particle swarm optimization (PSO-SVM) to improve the classification accuracy compared original SVM classification, the experiment indicates our proposed PSO-SVM approach can improve the classification accuracy.
Starting Page	4066
Ending Page	4069
File Size	306672
Page Count	4
File Format	PDF
ISBN	9781424449095
e-ISBN	9781424457281
DOI	10.1109/ICISE.2009.859
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Hyperspectral sensors Space technology Support vector machine classification Educational institutions Particle swarm optimization Kernel Remote sensing Hyperspectral imaging
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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