Accuracy assessment of supervised classification methods for extraction land use maps using remote sensing and GIS techniques

Abstract

Abstract Remotely sensed imagery identifying as the best type of data has information throughout the world. The imagery has a importance information, since it can show up-date-to day information, and provide a truly information. Different kinds of classifiers have been used to perform that. However, there is no once test for Land cover and Land use in Hilla city. The study aims to create land use classification by making a comparison between different algorithms in Hilla city, Babylon, Iraq. The WorldView-2 imagery is used to perform the per-processing, analysing of our comparison. Under the steps of pre-processing, the several corrections were made and performed on the imagery. For processing stages, two approaches were used; (1) Support Vector Machine and (2) Maximum Likelihood. The result reveals, that the Support Vector Machine method has the most significant of overall accuracy equal to 94.48% with kappa coefficient equal to 0.90, and these values much better and higher than those of Maximum Likelihood algorithm in estimating and extracting of Land cover/Land use. Therefore, this algorithm has been suggested to be applied as an optimal classifier for extraction of land use maps due to its higher accuracy and better consistency within the study area.