We develop an aerial image super-resolution method by training convolutional neural networks (CNNs) with respect to wavelet analysis. To this end, we commence by performing wavelet decomposition to aerial images for multiscale representations. We then train multiple CNNs for approximating the wavelet multiscale representations, separately. The multiple CNNs thus trained characterize aerial images in multiple directions and multiscale frequency bands, and thus enable image restoration subject to sophisticated culture variability. For inference, the trained CNNs regress wavelet multiscale representations from a low-resolution aerial image, followed by wavelet synthesis that forms a restored high-resolution aerial image. Experimental results validate the effectiveness of our method for restoring complicated aerial images.