Abstract: This study aims to explore the object-oriented classification based on moderate-resolution remote sensing data. Using the Landsat8 OLI, Landsat5 TM, and GF1 data obtained from the northern mountainous area and the southern plain area in Hebei Province, this study compared the land cover classification effects of four classifiers: support vector machine (SVM), random forest (RF), decision tree (DT), and naive Bayes (NB). Moreover, it analyzed the impacts of critical parameters in SVM, RF, and DT on the classification results. The findings indicate that the classification results of the classifiers vary slightly in the two study areas, with their effects decreased in the order of SVM, NB, RF, and DT. The classification accuracies of SVM and DT fluctuated significantly with parameter changes. With C values not below 10³ and gamma values not exceeding 10^-1, SVM can yield classification accuracies above 90% in all cases. With depth values over 3, DT exhibits relatively high and stable classification accuracies. With parameter changes, RF manifests slightly varying classification accuracies with nonsignificant variation patterns. The results of this study serve as a reference for exploring the object-oriented land cover classification based on moderate-resolution remote sensing data.