Enhanced Remote Sensing Image SRGAN Algorithm and Its Application in Improving the Accuracy of 3D Reconstruction

Remote sensing images are important data sources for terrain mapping, 3D reconstruction, and other tasks. The spatial resolution of remote sensing images determines the representation ability of the measured object on the image and plays an important role in the positioning accuracy and reconstruction effect of 3D model in the later stage. In view of the characteristics of high resolution remote sensing images including large scale, complex target features, and rich details, an enhanced SRGAN algorithm for remote sensing image reconstruction is proposed to meet the needs of 3D model reconstruction. The proposed algorithm overcomes the problems of edge effect and fuzzy reconstruction using traditional methods for super-resolution reconstruction. In traditional methods, there is limitation that simple convolutional networks can only extract the shallow feature information of the image and cannot retain the rich details of the image with the increasing resolution. The proposed algorithm is based on the generative adversarial networks using deep learning, in which dense residual blocks are used to extract deep features, and multi-scale discrimination is introduced into the discriminant model. In the training, the generation model and the discrimination model learn features together and are optimized to finally obtain a super-resolution reconstruction model suitable for remote sensing image application. This model can improve the resolution and image quality of remote sensing images, and ensure the integrity and accuracy of feature texture, detail information, and high-frequency target. In our study, the proposed algorithm is compared with the Bicubic, SRGAN, and ESRGAN algorithms. Our results show that the PSNR of the proposed algorithm is improved by about three units, the Penetration Index (PI) is stable and closer to one, and the SSIM and clarity index Q are also improved. In 3D reconstruction, the number of image dense matching points is increased, and the error is reduced. The measured point values of the model are closer to the measured point values from the field. The visual perception of the model is also more real and delicate, which indicates that the precision and positioning accuracy of the 3D model can be significantly improved using the remote sensing images constructed by the proposed algorithm. The results demonstrate the proposed algorithm that considers the characteristics of remote sensing images performs better than other algorithms for the super-resolution reconstruction, and the geometric accuracy and visual accuracy of the real 3D models based on the constructed images are also significantly improved. © 2022, Science Press. All right reserved.