Comparison and accuracy evaluation of denoising methods for ICESat-2 shallow-sea photon point clouds

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is the most advanced laser altimetry satellite at present, emitting a laser wavelength of 532 nm, which not only realizes the observation of land areas, but also provides a new solution for the shallow sea areas around the islands and reefs that are difficult to measure. However, due to the large number of noisy photons contained in the satellite raw data, it brings difficulties to the subsequent data processing work. In order to solve this problem, this paper uses two photon point cloud denoising algorithms proposed independently: one is the joint denoising algorithm of DBSCAN and OPTICS; the other is the denoising algorithm based on BP neural network, and the validity of the two methods in the shallow sea area around the islands is verified by comparing them with the experiments in a certain island area in the South China Sea.