At present, most of the building scene classification methods in remote sensing images use manual annotation method, which requires a lot of time. To solve this problem, this paper proposes a high resolution remote sensing building scene classification method using automatic sample selection. Firstly, the feature space of multidimensional highresolution remote sensing image with spectral features, geometric features and depth features is established. Secondly, the buildings were initially extracted by decision tree, and the scene density histogram of buildings was constructed. Then, the natural discontinuity method was used to classify the building density, and the proportion method was used to extract some scene images from each type of scene as training samples. Finally, ResNet50 network is used to classify building scenes. Taking Hunnan District of Shenyang City, Liaoning Province as the study area and Google Earth remote sensing images as the experimental data, the experimental results show that the proposed method can achieve unsupervised scene classification, and the overall classification accuracy and Kappa coefficient are 089 and 082, respectively, which are improved by 3% and 8% compared with the original sample selection method.