Finite-angle Magnetosphere Boundary CT Reconstruction Technique Based on Generative Adversarial Networks
Jiaxuan Wang, Dalin Li, Xiaodong Peng, Tianran Sun · Chinese Journal of Space Science · 2021
Soft X-ray imaging detection of the Earth's magnetosphere is the frontier direction of recent magnetosphere research. Research on the method of reconstructing 3D magnetic layer boundary from 2D X-ray image is an important research topic related to imaging detection. Traditional Computer Tomography (CT) reconstruction methods cannot obtain good reconstruction results when image data is small, or even fail to reconstruct 3D structures. Considering the constraints on the spatial distribution of the Earth's magnetosphere, the orbital design of current satellite missions is difficult to meet the full angle coverage of the scanning angle, and the magnetosphere can only be observed from a finite angle, which brings problem to the CT reconstruction of the magnetosphere. As the basis of the 3D reconstruction research, this paper examines a simplified 2D magnetospheric reconstruction method, and uses CT technology based on adversarial neural network to reconstruct the simplified magnetosphere layer boundary structure. First, we use an improved Generative Adversarial Networks (GAN) to complete the finite-angle satellite scanned image, and then the magnetosphere layer is reconstructed using an Algebraic Reconstruction Technique (ART) reconstruction method. Experiments show that when the scanning angle is greater than 90°, the Generative Adversarial Networks can effectively and accurately complete the missing image, and the reconstruction effect is better.