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Super-resolving Commercial Satellite Imagery Using Realistic Training Data

Xiang Zhu
Xinwei Shi
IEEE International Conference on Image Processing 2020 (2020)
Google Scholar


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 proposed 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 proposed 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.