An automated statistical analysis approach to noise reduction for photon-counting lidar systems

Satellite-based and airborne lidar instrumentation has been demonstrated to be a strategic tool in increasing our understanding of Earth's polar cryosphere, specifically that of total mass balance, which is a significant factor in estimating sea level rise due to climate change. The Ice, Cloud and land Elevation Satellite 2 (ICESat-2) will provide accurate estimates of local glacial topographical changes with increased measurement precision and change detection capability. Measurements obtained from the airborne Multiple Altimeter Beam Experimental Lidar (MABEL) instrument, an experimental photon-counting lidar - which uses multiple transmit/receive beams to resolve cross-track slope and elevation changes - are critical to project scientists to develop algorithms that will be used for the upcoming ICESat-2 mission. To aid in this effort, a new noise reduction technique has been developed. The general approach is to 1) divide the received photons into bins, 2) calculate the mode for each bin, and 3) compare each photon elevation to the mode for that bin, retaining only those photons that fall within a set threshold. Because the proposed technique uses statistical analysis to separate the surface elevation photons from the solar background photons, finding the surface return is computationally very light, making it ideal for large data sets, such as those associated with photon-counting lidars.