Open AccessA Light Model for Super-Resolution of Remote Sensing Images
Author(s) -
Song Tingting
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2171/1/012029
Subject(s) - computer science , block (permutation group theory) , benchmark (surveying) , residual , artificial intelligence , remote sensing , image resolution , field (mathematics) , computer vision , data mining , algorithm , geography , mathematics , geometry , geodesy , pure mathematics
Remote sensing image super-resolution is an important research topic, which helps to improve the quality of remote sensing images. However, because remote sensing images are usually quite large while the satellite equipment often have low computing capacity and small memory, it is appealing to develop lightweight and fast models to perform the super-resolution task for the remote sensing images. In this paper, we empirically study the effectiveness of conventional super-resolution approaches for remote sensing images, and propose an effective way to reduce the model parameters and computational cost. Specifically, motivated by Res2Net, we design a new multi-scale hierarchy residual block to replace the ResBlock in EDSR to provide a more diverse receptive field for each residual block. The proposed modified method has fewer parameters and faster speed compared with the original EDSR. Moreover, we also build two benchmark super-resolution datasets (i.e., DOTA-SR and LEVIR-SR) from DOTA and LEVIR-CD, respectively, for experimental evaluation. We perform experiments on the two datasets, and results show that our method is light and have comparable performance over the existing super-resolution baselines.