Single-photon point cloud parallelogram adaptive denoising algorithm
Yangleijing Li, Guoqing Zhou, Lin Li, Ruixiang Li, Ying Yao · 2024
Newly designed single-photon radar mounted on satellites can gather high-precision three-dimensional data. It is vulnerable to noise, though. This paper offers a parallelogram denoising kernel approach based on multi-feature adaption to solve the irregular background noise and the challenges of signal extraction in steep slope locations. In contrast to conventional circular or elliptical denoising kernels, this approach better matches the properties of single-photon point cloud data. Using a variety of characteristics, including slope and spatial density, it can recognize signals in an adaptive manner. While new radars show excellent accuracy capabilities, noise introduces an error to the measurement. The approach presented in this work solves the signal extraction problem well.