Nowadays everywhere remote sensing images are used for wide variety of applications, creation of mapping products for military and civil applications, evaluation of environmental damage, monitoring of land use, radiation monitoring, urban planning, growth regulation, soil assessment, and crop yield appraisal. A few number of image classification algorithms have proved good precision in classifying remote sensing data. An efficient classifier is needed to classify the remote sensing imageries to extract information. We have used texture based supervised classification. Here we compared different classification methods. KNN, SVM and Neural network are used. All the three classifier gives good result but neural network classifier takes long time, the time complexity is very high. Land use mapping has been done by comparing the images and area of the land used is calculated.

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