论文标题
使用逼真的培训数据超级分辨商业卫星图像
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data
论文作者
论文摘要
在基于机器学习的单图像超分辨率中，降解模型嵌入了培训数据生成中。但是，大多数现有的卫星图像超分辨率方法都使用带有固定内核的简单下采样模型来创建训练图像。这些方法在综合数据上效果很好，但在实际卫星图像上表现不佳。我们为商业卫星图像产品提供了一个现实的培训数据生成模型，该模型不仅包括卫星上的成像过程，还包括地面上的后处理过程。我们还提出了针对卫星图像优化的卷积神经网络。实验表明，拟议的培训数据生成模型能够改善实际卫星图像的超分辨率性能。
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.
