To solve the low accuracy problem of remote sensing image scene classification due to small sample sizes, a classification method was proposed based on Multiscale Features Fusion (MSFF). First, the remote sensing images were scaled to obtain several different scale images of the same remote sensing image. Thereafter, they were inputted into a Deep Convolutional Neural Network (DCNN) for convolutional operation, and the different scale features of the convolutional and the fully connected layers were reduced and coded or average pooled. Finally, the different scale features were coded and fused, and a multikernel support vector machine was used to classify the scenes. In the two public remote sensing image data sets UCM Land-Use and NWPU-RESISC45, the highest classification accuracy of the experiment are 98.91% and 99.33%, respectively. This method can use image features of different scales and low, middle and high-level semantic representations combined, thus the fusion feature is more recognizable. Furthermore, the use of a multikernel support vector machine improves the generalization of the deep network learning ability, so the classification effect is better.