Due to the lack of clear shape, texture characteristics, and abundant spectral or spatial information of urban objects, traditional per-/sub-pixel analysis and interpretation for moderate-resolution-remote sensing data are always confused by such shortcomings as dependence on special skills, requirements to a priori knowledge and training samples, complex process, time-consuming and subjective operations, etc. In order to alleviate such disadvantages, an automatic approach is proposed to classify vegetation, water, impervious surface areas (dark and bright), and bare land from the Operational Land Imager (OLI) sensor data of Landsat-8 in urban areas, which can be employed by common users to automatically obtain land-cover maps for urban applications. In detail, a preliminary classification result is achieved based on a new vegetation and water masking index (VWMI), the normalized difference vegetation index (NDVI), and a new normalized difference bare land index (NDBLI), which are acquired automatically from the remote-sensing images based on available knowledge of spectral properties. VWMI is designed to extract vegetation and water information together with a simpler threshold, while NDBLI is developed to identify dark impervious surfaces and bare land in this work. A modification strategy is further proposed to improve preliminary classification results by a linear model. For this purpose, a stable sample selection method based on the histogram is developed to select training samples from the preliminary classification result and to build a non-linear support vector machine (SVM) model to reclassify the classes. For validation and comparison purposes, the proposed approach is evaluated via experiments with real OLI data of two study areas, Nanjing and Ordos. The results demonstrate that the approach is effective in automatically obtaining urban land-cover classification maps from OLI data for thematic analysis.