An investigation of supervised LCLU classification performance over UAV based orthophoto

Abstract

Nowadays, the development of digital image processing techniques has contributed to the determination of land cover land use (LCLU) through digital images. In this study, a supervised classification has been made over an orthophoto of an area in Harran University Osmanbey Campus. The purpose of the study is to examine the performance of the three popular supervised classification techniques that are Maximum Likelihood, Minimum Distance, and Mahalanobis Distance methods. In the study, a confusion matrix was produced, and overall accuracy and overall kappa were calculated with manually generated ground truth data. According to results, the highest overall accuracy was calculated for Maximum likelihood classification with a rate of 84.5 % and the Minimum Distance method has the lowest overall accuracy (43%). The research shows that due to the lack of spectral information the supervised classification methods shows omission and commission errors. This fact has a direct effect on overall accuracy calculation.