An Improved 3D Reconstruction Method for Satellite Images Based on Generative Adversarial Network Image Enhancement

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Three-dimensional reconstruction based on optical satellite images has always been a research hotspot in the field of photogrammetry. In particular, the 3D reconstruction of building areas has provided great help for urban planning, change detection and emergency response. The results of 3D reconstruction of satellite images are greatly affected by the input images, and this paper proposes an improvement method for 3D reconstruction of satellite images based on the generative adversarial network (GAN) image enhancement. In this method, the perceptual loss function is used to optimize the network, so that it can output high-definition satellite images for 3D reconstruction, so as to improve the completeness and accuracy of the reconstructed 3D model. We use the public benchmark dataset of satellite images to test the feasibility and effectiveness of the proposed method. The experiments show that compared with the satellite stereo pipeline (S2P) method and the bundle adjustment (BA) method, the proposed method can automatically reconstruct high-quality 3D point clouds.
Funding Information
  • Major Program of National Natural Science Foundation of China NSFC (12292980, 12292984, 2023YFA1009000, 2023YFA1009004, 2020YFA0712203, 2020YFA0712201, 12031016, BNSF-Z210003, 8091B042240, 2412022QD024)
  • National Key R&D Program of China (12292980, 12292984, 2023YFA1009000, 2023YFA1009004, 2020YFA0712203, 2020YFA0712201, 12031016, BNSF-Z210003, 8091B042240, 2412022QD024)
  • Key Projects of National Natural Science Foundation of China NSFC (12292980, 12292984, 2023YFA1009000, 2023YFA1009004, 2020YFA0712203, 2020YFA0712201, 12031016, BNSF-Z210003, 8091B042240, 2412022QD024)
  • Natural Science Foundation of Beijing Municipality (12292980, 12292984, 2023YFA1009000, 2023YFA1009004, 2020YFA0712203, 2020YFA0712201, 12031016, BNSF-Z210003, 8091B042240, 2412022QD024)
  • Department of Science, Technology and Information of the Ministry of Education (12292980, 12292984, 2023YFA1009000, 2023YFA1009004, 2020YFA0712203, 2020YFA0712201, 12031016, BNSF-Z210003, 8091B042240, 2412022QD024)
  • Fundamental Research Funds for the Central Universities (12292980, 12292984, 2023YFA1009000, 2023YFA1009004, 2020YFA0712203, 2020YFA0712201, 12031016, BNSF-Z210003, 8091B042240, 2412022QD024)

This publication has 27 references indexed in Scilit: