Abstract: Background: Using the high resolution image to establish the remote sensing classification model and extracting the urban land information, can provide the information support for the urban land use planning and management. The accuracy of traditional classification models is unsatisfactory and there is the problem of over-fitting. With the development of the model algorithm, the Random Forest (RF) ensemble-learning algorithm has the potential to solve these problems. This study attempts to establish a high resolution remote sensing image classification model based on the random forest method and study its performance. Materials and Methods: This study presents a comparison between the classification results obtained with RFs and the Support Vector Machine (SVM) by using Hymap high-resolution remotely sensed image of Berlin. Before the establishment of the model we first band to pick out the right band and texture data. After that, use data selection, optimization parameter model, then RF and SVM model is established for the high resolution image classification, finally the validation process using the total classification accuracy and Kappa coefficient. Results: The study results show that for the RF algorithm the overall classification accuracy reaches 92.6%, the Kappa coefficient is 0.9024, for the SVM algorithm the overall classification accuracy is 91.2%, the Kappa coefficient is 0.8840. Conclusion: Using the random forest method for high resolution remote sensing image classification is feasible, the main performance is better than the SVM, compared with SVM, the random forest algorithm has fewer parameters, more easily parameter optimization, higher classification accuracy, easier to serve the production practice.