Combined genetic K-means and radial basis function neural network technique for classifying and predicting soil moisture

Chi Cheng Hung; Venkata Atluri; Tommy L. Coleman

doi:10.1117/12.373092

17 December 1999 Combined genetic K-means and radial basis function neural network technique for classifying and predicting soil moisture

Chi Cheng Hung, Venkata Atluri, Tommy L. Coleman

Author Affiliations +

Proceedings Volume 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications; (1999) https://doi.org/10.1117/12.373092
Event: Remote Sensing, 1999, Florence, Italy

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.

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Chi Cheng Hung, Venkata Atluri, and Tommy L. Coleman "Combined genetic K-means and radial basis function neural network technique for classifying and predicting soil moisture", Proc. SPIE 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications, (17 December 1999); https://doi.org/10.1117/12.373092

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