  In machine learning based single image super-resolution, the degradation
model is embedded in training data generation. However, most existing satellite
image super-resolution methods use a simple down-sampling model with a fixed
kernel to create training images. These methods work fine on synthetic data,
but do not perform well on real satellite images. We propose a realistic
training data generation model for commercial satellite imagery products, which
includes not only the imaging process on satellites but also the post-process
on the ground. We also propose a convolutional neural network optimized for
satellite images. Experiments show that the proposed training data generation
model is able to improve super-resolution performance on real satellite images.
