Super-Resolving Commercial Satellite Imagery Using Realistic Training Data

• URL: http://arxiv.org/abs/2002.11248v1
• Date: Wed, 26 Feb 2020 01:18:51 GMT
• Title: Super-Resolving Commercial Satellite Imagery Using Realistic Training Data
• Authors: Xiang Zhu, Hossein Talebi, Xinwei Shi, Feng Yang, Peyman Milanfar
• Abstract summary: We propose a realistic training data generation model for commercial satellite imagery products. Experiments show that the proposed training data generation model is able to improve super-resolution performance on real satellite images.
• Score: 15.642067804645396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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