A universal remote sensing image quality improvement method with deep learning
Yancong Wei, Qiangqiang Yuan, Huanfeng Shen, Liangpei Zhang · 2016
In this paper, we introduced a deep learning model: Convolutional neural network(CNN) from the field of natural image classification and restoration, to solve general quality improving tasks for remote sensing images, including super-resolution, denoising and haze removal. To take advantage of the content similarity among aerial images and the learning ability of deep learning models, we proposed the idea of training CNN on datasets collected from aerial images with specific degenerating factors, then apply the model to matched tasks. Experiments showed that our network achieved superior performance in quantified results, and visually reconstructed a satisfying majority of missing details from low-quality observations.