Browsing by Author "Narasipura, O.S."

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    Crop classification using gene expression programming technique
    (2013) Narasipura, O.S.; John, R.L.; Choudhry, N.; Kubusada, Y.; Bhageshpur, G.
    Precise classification of agricultural crops provides vital information on the type and extent of crops cultivated in a particular area. This information plays an important role in planning further cultivation activities. Image classification forms the core of the solution to the crop coverage identification problem. In this paper we present the experimental results obtained by using Gene Expression Programming (GEP) to classify the crop data obtained from satellite images. We have adopted supervised one-against-all learning technique to perform the classification of data. Gene Expression Programming provides an efficient method for obtaining classification rules in the form of a mathematical expression for a given data set containing input and output variables. We have also compared the classification efficiencies obtained with those of other classifiers namely Support vector machines and Artificial neural networks. Sensitivity Analysis has also been carried out to determine the significance of each input variable. � 2013 Springer-Verlag.
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    Crop classification using gene expression programming technique
    (Springer Verlag service@springer.de, 2013) Narasipura, O.S.; John, R.L.; Choudhry, N.; Kubusada, Y.; Bhageshpur, G.
    Precise classification of agricultural crops provides vital information on the type and extent of crops cultivated in a particular area. This information plays an important role in planning further cultivation activities. Image classification forms the core of the solution to the crop coverage identification problem. In this paper we present the experimental results obtained by using Gene Expression Programming (GEP) to classify the crop data obtained from satellite images. We have adopted supervised one-against-all learning technique to perform the classification of data. Gene Expression Programming provides an efficient method for obtaining classification rules in the form of a mathematical expression for a given data set containing input and output variables. We have also compared the classification efficiencies obtained with those of other classifiers namely Support vector machines and Artificial neural networks. Sensitivity Analysis has also been carried out to determine the significance of each input variable. © 2013 Springer-Verlag.