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Proceedings Paper

Combined genetic K-means and radial basis function neural network technique for classifying and predicting soil moisture
Author(s): Chi Cheng Hung; Venkata Atluri; Tommy L. Coleman
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Paper Abstract

A combined technique of genetic k-means and radial basis function neural network (RBFNN) is used in this study to process remote sensing data and classify soil basing on its moisture content. Radial basis function neural network is used for its advantages of rapid training, generality and simplicity over feed-forward backpropagation neural network. The genetic k-means clustering is used to choose the initial radial basis centers and widths for the RBFNN. An attempt is also made to study the performance of the RBFNN with the centers and widths chosen using the classical k-means clustering. The results showed that genetic algorithms give global optimal centers and widths for the RBFNN. The results also indicated that this hybrid technique can be used in soil moisture classification and prediction.

Paper Details

Date Published: 17 December 1999
PDF: 4 pages
Proc. SPIE 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications, (17 December 1999); doi: 10.1117/12.373092
Show Author Affiliations
Chi Cheng Hung, Alabama A&M Univ. (United States)
Venkata Atluri, Alabama A&M Univ. (United States)
Tommy L. Coleman, Alabama A&M Univ. (United States)


Published in SPIE Proceedings Vol. 3868:
Remote Sensing for Earth Science, Ocean, and Sea Ice Applications
Giovanna Cecchi; Edwin T. Engman; Eugenio Zilioli, Editor(s)

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