CLASSIFICATION OF SATELLITE IMAGES USING ANN

This paper describes the MLP NN classifier performing optimally in classifying the different land types from Landsat data. In fact classification process is a compulsory step in any remote sensing research. Therefore, the main objective of this paper is to assess classification accuracy of classified lands on benchmark Landsat data from UCI machine learning repository. The six land type classes namely red soil, cotton crop soil, damp grey soil, soil with vegetation stubble, very damp grey soil can be identified. Result showed that overall classification accuracy is 87.57%, which is considered acceptable.Results show that this new neural network model is more accurate than the other NN models. These results suggest that this model is effective for classification of satellite image data.