A Self-Adaptive Denoising Algorithm Based on Genetic Algorithm for Photon-Counting Lidar Data

The ice, cloud, and land elevation satellite-2 (ICESat-2) is equipped with a photon-counting laser altimeter system and demonstrates outstanding ability to measure elevations in the ever-changing earth. However, the ICESat-2 data contain several noise photons affected by solar returns, and there are no reference data of signal or noise photons for evaluating the performance of denoising algorithms. In this letter, we propose a self-adaptive denoising algorithm based on a genetic algorithm (SADA-GA) for the ICESat-2 data, which uses the real-coded genetic algorithm to adaptively search for the global optimal denoising parameters in different data sets. The SADA-GA addresses the limitation of the selection method of the two parameters K and T in the localized statistics-based algorithm that normally cannot be applied to different data sets. To evaluate the algorithm performance, we created an ICESat-2 data set named WHU-PCL and compared the SADA-GA with two classic methods. The qualitative and quantitative analyses showed that our method can extract signal photons more efficiently from different ICESat-2 data sets and achieve the
$F$
value of 0.99 in nighttime data. In addition, we analyzed the factors that affect the SADA-GA performance and found that the signal-to-noise ratio (SNR) is the most important parameter.