Regression and Artificial Neural Network based Improved Classification of LISS-III Satellite Image

Remote sensing is one of the very important parts in the advance image processing which is used to acquiring satellite image to know about geo-spatial information. Remote sensing is widely used to monitor different resources available on earth surface, out of which the land used area monitoring is one of very important part. There are many techniques and methods are proposed for satellite image classification based on different theme. Here, in this paper theme of classification is land used and land area cover mapping using LISS-III satellite image. LISS-III satellite image have been classified in different area covered by water, forest, mangroves and developments by selecting the pixel based classification. Here multiple linear regressions is used on initial training data set, after evaluation the new features data set is applied to artificial neural network to check the impact of best training data set on the classification accuracy. The accuracy of classification algorithm is calculated using confusion matrix and Kappa coefficient. It is observed with help of confusion matrix and Kappa coefficient that the accuracy of ANN classifier without evaluated training data set is 90.19% and Kappa Coefficient is 0.8533 and when training data set evaluated with the help of multiple Linear regression then ANN accuracy is 98.74% and the Kappa Coefficient is 0.9766 which is improved and very good. So with the proposed method classification accuracy of ANN classifier is improvement for classification of LISS-III satellite image.