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Asian Journal of Applied Sciences
  Year: 2011 | Volume: 4 | Issue: 2 | Page No.: 186-194
DOI: 10.3923/ajaps.2011.186.194
Segmentation of Satellite Imagery using RBF Neural Network and Genetic Algorithm
H. Mahi and H.F. Izabatene

Abstract:
In this study, a Radial Basis Function Neural Network (RBFNN) is applied for the purpose of satellite image segmentation. During the unsupervised learning of the RBF network, unsupervised Genetic Algorithm (GA) is employed to automatically determine the hidden layer parameters. A comparative study between traditional unsupervised k-means clustering algorithm and the GA has been considered. Therefore, the RBF network as well as the GA has been evaluated on a QUICKBIRD satellite image without and with textural feature. Experimental results show that the RBF network combined to the GA is an attractive approach for segmentation of multispectral remote sensing imagery. Indeed, texture features were helpful for increasing the segmentation accuracy compared to the use of the spectral information alone. This study was carried out as part of research project on images segmentation using a new approach started March 2009 at the earth observation division, Center of Space Techniques, Arzew, Algeria.
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How to cite this article:

H. Mahi and H.F. Izabatene, 2011. Segmentation of Satellite Imagery using RBF Neural Network and Genetic Algorithm. Asian Journal of Applied Sciences, 4: 186-194.

DOI: 10.3923/ajaps.2011.186.194

URL: https://scialert.net/abstract/?doi=ajaps.2011.186.194

 
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