Image classification algorithm based on the RBF neural network and K-means

This Letter presents the implementation of an algorithm using the radial basis function (RBF) neural networks combined with the technique of K-means for the classification of optical and radar remote sensing images. During the convergence of RBF networks, K-means serves for the initialization of cla...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of remote sensing Vol. 19; no. 15; pp. 3003 - 3009
Main Authors Rollet, R., Benie, G. B., Li, W., Wang, S., Boucher, J-M.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis Group 01.01.1998
Taylor and Francis
Subjects
Applied geophysics
Earth sciences
Earth, ocean, space
Exact sciences and technology
Internal geophysics
Canada
Western Canada
Saskatchewan
algorithms
imagery
accuracy
remote sensing
classification
North America
radar methods
Online AccessGet full text

Cover

More Information
Summary:This Letter presents the implementation of an algorithm using the radial basis function (RBF) neural networks combined with the technique of K-means for the classification of optical and radar remote sensing images. During the convergence of RBF networks, K-means serves for the initialization of class centres. An automatic self-organizing classification algorithm is constructed based on the RBF networks and split and merge technique. Experimental results show that this algorithm is an effective classifier compared to some conventional methods.
ISSN:0143-1161
1366-5901
DOI:10.1080/014311698214398