Comparative Study of RBF and Naïve Bayes Classifier for Road Detection Using High Resolution Satellite Images

Abstract

The detection of the road is one of an area of satellite image classification. The satellite image classification plays a vital role in various area of monitoring different resources available on the earth surface. Here, the high-resolution satellite data from Google earth is acquired from a different region of Mumbai, Maharashtra, India region for detection of road. This research paper used two different algorithms i.e. radial basis function neural network and Naive Bayes classifiers for the detection of reading features from the high-resolution satellite image. Both algorithms are implemented using the Matlab simulation toolbox. Radial Basis Function and Naïve Bayes is a supervised classification technique applied on High-Resolution Satellite Image. Extraction of Road from the satellite image is a very difficult task because in the rural areas there are many unstructured roads which may consist of mud and concrete. After applying the algorithms on the image high-resolution satellite, the accuracy of classifiers is calculated using confusion matrix and Kappa coefficient. The accuracy of Naive Bayes found to be 91% with Kappa Value 0.698 and the accuracy of radial basis function found to be 99% with a Kappa value of 0.9831. The accuracy calculation using confusion matrix and Kappa value shows that the radial basis function neural network classifier is better than Naive Bayes classifiers for the detection of the road using high-resolution satellite image.