High-resolution remote sensing image scene classification aims to accurately perceive complex surface scenes， which is significant for the understanding and information extraction of high-resolution remote sensing images. A new scene classification method based on feature aggregated convolution neural network （FACNN） and capsule network（CapsNet）， named FACNNCN， is proposed in this paper. For the proposed method， the distinguish ability and robustness of convolutional features for scene classification are enhanced by adding aggregated features. Meanwhile， the spatial relationship between geographic entity and scene is represented based on CapsNet. Therefore， the proposed method can overcome some drawbacks usually found in existing high-resolution remote sensing image scene classification methods based on CNNs. For example， the extracted representative features of scene images are insufficient and the spatial features of geographical objects are lack of consideration. The method proposed in this paper is tested on two public high-resolution remote sensing image scene classification datasets （UC Merced Land-Use and NWPU-RESISC45）. Experimental results show that the classification accuracy of FACNNCN is better than those of comparison methods.