Open AccessResearch on Key Techniques for Super-resolution Reconstruction of Satellite Remote Sensing Images of Transmission Lines
Author(s) -
Weiguo Si,
Jiajia Han,
Zhi Yang,
Xiao Ma,
Yuanjing Deng,
Meng Luo,
Chuang Li,
Binbin Zhao,
Jian Wang,
Bin Zhao
Publication year - 2021
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 - 10.1088/1742-6596/1848/1/012092
Subject(s) - computer science , remote sensing , artificial intelligence , computer vision , transmission (telecommunications) , convolutional neural network , interpolation (computer graphics) , bicubic interpolation , frame (networking) , satellite , image resolution , line (geometry) , image (mathematics) , geography , multivariate interpolation , telecommunications , bilinear interpolation , engineering , mathematics , geometry , aerospace engineering
The data point target on the satellite remote sensing image of the transmission line is restricted by satellite remote sensing imaging equipment and transmission conditions, which makes it difficult to guarantee the clarity of the transmission line. Image super-resolution technology aims to recover high-resolution images from low-resolution images to improve the detailed information of the transmission line itself, which is of great significance for the intelligent inspection of transmission line satellite remote sensing and hidden danger monitoring. Aiming at the problems of traditional methods relying on multi-frame image sequences and the reconstruction results are too smooth, this paper proposes a single-frame remote sensing image super-resolution method based on the boundary balance generation confrontation network. Experimental results show that this method can provide more high-frequency information, and the reconstruction result is closest to the real image. Compared with neighbor interpolation, bicubic interpolation and other methods based on deep convolutional neural networks, the PSNR of the results in this paper is significantly improved, and effectively enhances the detailed information of the transmission line and the surrounding environment.