Super Resolution Enhancement of Satellite Remote Sensing Images of Transmission Tower Based on Multi-map Residual Network and Wavelet Transform
Zhi Chun Yang, Binbin Zhao, Xiao Ma, Meng Luo, Jiajia Han, Weiguo Si · 2020
Existing satellite remote sensing images are often used to observe the fuzzy phenomenon of transmission line bodies. It is necessary to enhance super-resolution, but traditional superresolution technology is difficult to obtain rich details and edge information of transmission towers. This paper proposes a multiscale edge enhancement method combining multi-map residual convolutional neural network and wavelet transform to solve these problems. Specifically, we first use a multi-map residual convolutional neural network to directly take the low-resolution image as the initial input of the network, and then use a convolutional layer to extract features. Secondly, a multi-mapping network is established by residual learning, and a batch normalization layer is added to optimize the network to enrich the feature information needed for high-resolution image aggregation. Finally, we use deconvolution layers to complete image upsampling and output high-resolution images, so the initial image can directly complete the end-to-end mapping relationship between low-resolution images and high-resolution images without performing preprocessing. On this basis, multiscale edge enhancement is performed on the transmission tower based on wavelet transform to obtain the final super-resolution enhancement result. Experiments on different benchmark data sets show that the proposed method is superior to the existing methods in the four quantitative indicators of peak signal-to-noise ratio, structural similarity, entropy and image detail enhancement.