Super-resolution reconstruction of remote sensing image by fusion of receptive field and attention
Zhenjiu Xiao, Ting Su, Yuqi Zhai · 2023
Abstract:Aiming at the problem that the existing models are not enough to extract the spatial details and feature channel information of remote sensing images, a super-resolution reconstruction model of remote sensing images is proposed, which integrates the receptive field and the attention mechanism. In the depth feature extraction stage of the model, several cascaded receptive field and coordinate attention blocks (RFCAB) are designed to fully extract the depth features of the image: Firstly, an RFB-CA module is designed inside the residual in residual dense block (RRDB). The model can use convolution of different scales to extract multi-scale spatial features and make both channel and space dimensions get attention. At the same time, in the process of learning features, the model pays more attention to the useful channels for the current task, so as to improve the feature representation ability of the model. In order to further improve the recovery ability of the model to detail information, a multi-scale fusion module (MSFM) was designed to obtain more detailed features by weighted fusion of features at different levels. In the 2x, 3x and 4x overscore reconstruction of DOTA dataset, the PSNR/SSIM value of this model is increased by 0.13dB/0.003, 0.17dB/0.007 and 0.24dB/0.013 compared with ESRGAN, respectively. In the 2x, 3x and 4x over-fraction reconstruction of AID dataset, the PSNR/SSIM value is increased by 0.15dB/0.004, 0.20dB/0.009 and 0.26dB/0.015 compared with ESRGAN, respectively. The experimental results show that the reconstruction effect of this model is better than other classical algorithms, and it has certain practical significance.