论文标题
稀疏的INS数据3D介绍沿铁路走廊的地面变形检测
Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor
论文作者
论文摘要
对靠近铁路走廊的地面运动的监测，例如与地面沉降和/或抬高引起的降落的降落，对发现和预防可能的铁路断层引起了极大的兴趣。干涉合成孔径雷达（INSAR）数据可用于测量地面变形，但其使用构成了不同的挑战，因为数据高度稀疏并且可能特别嘈杂。在这里，我们提出了一个方案，用于处理和插值嘈杂，稀疏的Insar数据中的稀疏时空堆栈，有助于抑制噪声并通过深度学习和其他图像处理方法打开治疗的可能性。
Monitoring of ground movement close to the rail corridor, such as that associated with landslips caused by ground subsidence and/or uplift, is of great interest for the detection and prevention of possible railway faults. Interferometric synthetic-aperture radar (InSAR) data can be used to measure ground deformation, but its use poses distinct challenges, as the data is highly sparse and can be particularly noisy. Here we present a scheme for processing and interpolating noisy, sparse InSAR data into a dense spatio-temporal stack, helping suppress noise and opening up the possibility for treatment with deep learning and other image processing methods.
