Automatic land cover classification using learning techniques with dynamic features

Abstract

Accurate land cover classification is required for government and private research bodies to monitor and to report road maps, forest area, agriculture land and other land classification problems. These reviews suggest that the progress of land use/cover classification method grows along-side the launch of a replacement sequence of Land-set and advancement within the computer or applied science. As land cover changes over the time. Thus monitoring and mapping of land cover and its changes over large areas is made possible by measure of Google earth Pro data collected through Smart GIS, Qgis, ArcGIS, ERDAS, IMAGEINE and Envir. This paper presents study of various land use/cover classification techniques. The method of land use/cover classification is applied to a land-set imagery followed by supervised and unsupervised, object based, pixel based, knowledge based, sub pixel based and contextual based classifiers. This paper covers the various classification approaches, methods, classifiers and techniques. Further analysis is required on the application of hybrid land use/cover classification classifiers as they’re precise.