Efficient scheme of classifier on land use pattern

Abstract

Remote sensing is the collection and interpretation of information about an object, area, without being in physical contact with the object. Major Application of remote sensing in field of agriculture are in management of land and water resources, area estimation and monitoring, crop nutrient deficiency detection, soil mapping etc., The scope of the present study is land use and land pattern classification using digital image classification methods, their comparisons and accuracy assessment. The Machine learning algorithms are played vital role to achieve efficient pattern classifications. The supervised classifier is identifying the classes using trained set while in an unsupervised classification the classifier itself develops the spectral classes. Test imagery were obtained through Sentinel-2B Satellite on 15th January 2018. Maximum Likelihood Supervised Classification and Unsupervised Classification were performed using ERDAS 2015 imagine processing. Classification accuracy validations of supervised classes were expressed using confusion matrix. The measures such as F-measure value, Kappa coefficients estimated. The test of significance of the Kappa coefficient was performed using Z- test. Maximum likelihood classification out performed with highest overall accuracy of 72.99 percent. This study helps the farmers and policymakers using early and accurate estimates of yields, managing resources, and estimate area of crop production.