A Multi-Degradation Aided Method for Unsupervised Remote Sensing Image Super Resolution With Convolution Neural Networks

• Published in: IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-14
• Main Authors: Zhang, Ning, Wang, Yongcheng, Zhang, Xin, Xu, Dongdong, Wang, Xiaodong, Ben, Guangli, Zhao, Zhikang, Li, Zheng
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Convolution; Deep learning; Deep neural network (DNN); Degradation; Gallium nitride; Hyperspectral imaging; Image contrast; Image degradation; Image processing; Image reconstruction; Image resolution; Kernel; Methods; multi-degradation; Neural networks; Remote sensing; remote sensing image; Resolution; Satellite imagery; Spatial resolution; super-resolution (SR); unsupervised learning
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2020.3042460

In remote sensing, it is desirable to improve image resolution by using the image super-resolution (SR) technique. However, there are two challenges: the first one is that high-resolution (HR) images are insufficient or unavailable; another one is that the single degradation model such as bicubic (BIC) cannot super-resolve favorable images in the real world. To address the above two problems, this article presents a multi-degradation, unsupervised SR method based on deep learning. This framework consists of a degrader {D} to fit the image degradation model and a generator {G} to generate SR image. By introducing {D} , calculating the loss function between SR image and HR image as supervised SR methods did can be converted into calculating loss between low resolution (LR) image and image degraded by SR image, thereby realizing unsupervised learning. Experiments on several degradation models show that our method renders the state-of-the-art results compared with existing unsupervised SR methods, and achieves competitive results in contrast with supervised SR methods. Moreover, for real remote sensing images obtained by the Jilin-1 satellite, our method obtained more plausible results visually, which demonstrate the potential in real-world applications.