  Single image super-resolution is an effective way to enhance the spatial
resolution of remote sensing image, which is crucial for many applications such
as target detection and image classification. However, existing methods based
on the neural network usually have small receptive fields and ignore the image
detail. We propose a novel method named deep memory connected network (DMCN)
based on a convolutional neural network to reconstruct high-quality
super-resolution images. We build local and global memory connections to
combine image detail with environmental information. To further reduce
parameters and ease time-consuming, we propose downsampling units, shrinking
the spatial size of feature maps. We test DMCN on three remote sensing datasets
with different spatial resolution. Experimental results indicate that our
method yields promising improvements in both accuracy and visual performance
over the current state-of-the-art.
