Abstract

Photon counting lidar signals generally require smoothing to suppress random noise. While the process of reducing the resolution of the profile reduces random errors, it can also create systematic errors due to the smearing of high gradient signals. The balance between random and systematic error... Show morePhoton counting lidar signals generally require smoothing to suppress random noise. While the process of reducing the resolution of the profile reduces random errors, it can also create systematic errors due to the smearing of high gradient signals. The balance between random and systematic errors is generally scene dependent and difficult to find, because errors caused by blurring are generally not analytically quantified. In this work, we introduce the use of Poisson thinning, which allows optimal selection of filter parameters for a particular scene based on quantitative evaluation criteria. Implementation of the optimization step is relatively simple and computationally inexpensive for most photon counting lidar processing. Show less