The training sample size for some objects on the ground is quite small when applying a deep learning model to study the classification of remote sensing images. Meanwhile, diversified remote sensing image acquisition methods generate numerous multimodal remote sensing images with different spatial resolutions. Fusing these multi-modal remote sensing images to remedy the small sample size defect and achieve a highly precise classification of remote sensing images is an urgent problem to be solved. To this end, the present study proposes a fusion method for image classification based on the correlation of two spatial resolutions. A deep learning network is utilized to extract the high-level features of the remote sensing images in two spatial resolutions. Two types of high-level features are integrated via the proposed fusion strategy and further used as the input to train the whole network model. The experimental results demonstrate that the proposed fusion algorithm can achieve high classification accuracy. Further, because different fusion rules have different classification accuracies, a suitable selection can improve the classification accuracy.