Radiometric correction, which is often used as a preliminary step for remote sensing classification, is required in the efficient extraction of land-cover information from remote sensing images, particularly in areas with rough terrain. The objective of this study is to examine the effect of different radiation correction methods of Landsat TM data on land-cover remote sensing classification. Three different radiometric correction methods (ATCOR 3, FLAASH and Look-Up Table (LUT)) were introduced, and the Maximum Likelihood Classifier (MLC) and Support Vector Machines (SVMs) were used for classifiers. The training samples were selected from three corrected images based on geographical coordinates and classifier training to classify the three corrected images mutually. The results indicate the following. (1) The samples selected from the classification image for classifier training significantly improve classification accuracy compared with the samples selected from other images. (2) The classification results of the three radiometric correction images are different, whereas the results of the ATCOR3 and FLAASH methods are similar. (3) The effect of radiometric correction on the classification accuracy of each category is different: radiometric correction affects "Forest" significantly and other categories variably.