Land Cover Classification of Remotely Sensed Satellite Data using Bayesian and Hybrid classifier

In this paper an attempt has been made to develop classification algorithm for remotely sensed satellite data using Bayesian and hybrid classification approach. Bayesian classification is a probabilistic technique which is capable of classifying every pattern until no pattern remains unclassified. Hybrid classification involves developing training patterns using unsupervised classification followed by classifying the pixels using supervised classification. It is observed that the overall accuracy was found to be 90.53% using the Bayesian classifier and 91.57% using the Hybrid classifier.