吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 143-147.

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Hyperspectral imagery classification based on SVM and RVM

QI Bin, ZHAO Chun-hui, WANG Yu-lei   

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  • Received:2012-05-25 Published:2013-06-01

Abstract:

Hyperspectral remote sensing is the multi-dimensional information obtaining technology,which combines target detection and spectral imaging technology together.That is,it could obtain the two-dimensional object distribution information and one-dimensional spectral feature characteristic information at the same time.Compare with multi-spectral remote sensing,hyperspectral images contain abundant spectral information for the targets,which could greatly reflect the detailed characteristic of the ground,and makes the precise classification possible.Through the comparison of the theory between support vector machine(SVM)and relevance vector machine (RVM),this study utilized the two high-dimensional data processing methods in the classification of the same hyperspectral imagery.The experiment results show that the overall classification of SVM is slightly higher than that of RVM.

Key words: hyperspectral imagery classification, support vector machine, relevance vector machine

CLC Number: 

  • TP751.1

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