The Miyun Reservoir area between the North China plain and the Mongolian plateau has various physical conditions and intensity of human disturbance. Thus, studying the method of monitoring the land cover of this area is important. On the basis of high-resolution remote sensing imagery, this article classified the land cover of the Miyun reservoir area and studied the application of auxiliary data in object-based classification method. The remote sensing data used were mainly high spatial-resolution images, including RapidEye and SPOT-5. Accurate geometric rectification was performed initially. To gain more object features for distinguishing the different classes more clearly, we referenced the digital elevation model and slope gradient data with spatial resolution of 25m and the thematic map of the land use with a scale of 1 ∶ 10000. Multi-temporal HJ-1 imagery was added to separate the evergreen needle forests and the dry land. A total of 687 field samples were collected from the Miyun reservoir area for classification and precision testing. The eCognition v8.7 software was also used in this study. First, the images were segmented into different image objects according to the object features in the spectra, where several parameters are needed. In RapidEye, five bands with the same weight were used for segmentation. Choosing the segmentation scales and parameters of the shape and compactness is important. Through constant experiments, we found that the suitable segmentation scale was 45. The configuration of the shape parameter will determine the weight of the spectra in the segmentation. Given that the classification was mainly based on the spectral feature, we set the shape parameter to 0.1; correspondingly, the spectra parameter was set to 0.9. The shape features of the image object included compactness and smoothness, which were both set to 0.5 because of the complex shape of the land cover. The smoothness and compactness of the objects were almost equally important. We established the sample database after segmentation. Every sample was an image object in the data base and had object features of spectra, shape, and texture. Basing on the sample database, we trained these samples and used the supervised classifier supplied by eCognition to classify the land cover automatically. However, the software still had some uncertainty in recognizing similar objects with different spectra and different objects with similar spectra. We used numerous auxiliary datasets to modify the SVM classifier results. Results revealed 26 types of land cover in the study area; 85% of which are deciduous broad-leaved shrubs, deciduous broad-leaved forests, dry lands, and grasses. This study used two methods, namely, field validation and visual validation, in evaluating the product accuracy to ensure the objectivity and comprehensiveness of the accuracy evaluation. The result of the field validation accuracy was 85%, whereas that of the visual evaluation accuracy was 86%. This study distinguished the evergreen coniferous forest and cultivated land through numerous auxiliary data. Results proved that the auxiliary data were vital for improving classification accuracy of objects, especially similar objects with different spectra and different objects with similar spectra.