摘要：Land use cover (LUC) classification is one of the most important applications of optical remotely senseddata, while LUC mapping outcomes are used for global, local mapping, ecosystem assessment andenvironmental process monitoring. Hence, in this study, in order to evaluate the advantages and drawbacksof supervised classification schemes, the paper chose the optical image data of Landsat 8 OLI in Miyuncounty to test supervised classification and introduced Parallelepiped Method (PM), Minimum Distance(MD), Maximum Likelihood Classifier (MLC) and Support Vector Machines (SVMs) to improve classificationaccuracy of LUC mapping and to obtain the reliable LUC distribution. The four classified images reveal thatthe study area is dominated by considerable areas of forest land, with the overall accuracy found to be87.89% (kappa = 0.8524) using SVMs, 85.26% (kappa = 0.8205) using MLC, 82.11% (kappa = 0.7813)using MD, and 74.74% (kappa = 0.6920) using PM. Based on the overall accuracy and kappa statistics,SVMs might be the first option in terms of classification accuracy without taking into account of the timecostly and standard PC and laptops. MLC was the second accurate model classifiers from the classifiedimage, which was always used to obtain LUC map information for economic potential in time and cost; and PMhas shown the lowest overall classification accuracy with greater omission errors and commission errors.
