Adaptive denoising and classification algorithms for ICESat-2 airborne experimental photon cloud data of 2018

The ice, cloud and land elevation satellite-2 (ICESat-2) is equipped with advanced photon counting lidar. The system is a multi-beam micro pulse photon counting radar, which has the advantages of low energy consumption, high measurement sensitivity, high repetition rate and high space operation altitude. However, due to the characteristics of the lidar, the data returned is the elevation profile photon cloud data. Due to the nature of the instrument, the data is easily affected by noise photons, observation time, observation area and so on. The photon cloud data contains a lot of background light noise. Before using the photon cloud data for canopy extraction, the efficient and high-precision photon denoising and classification algorithm is as follows It's very necessary. Based on the above problems, this paper proposes an improved triple denoising algorithm. Firstly, DBSCAN clustering algorithm is selected for the coarse denoising of photons. The eps parameters of clustering algorithm have a great influence on it. In this paper, by analyzing the correlation between the density of photon cloud and the parameters of the algorithm and the denoising results, it is proposed to select the optimal eps parameters adaptively for rough denoising according to the internal characteristics of the photon cloud The signal photons are not lost and the noise photons are removed effectively. Then, two fine denoising algorithms are carried out to remove the noise photons located at the top of the canopy and below the ground line. Finally, the optical cloud is classified and fitted to the ground line and the canopy top line. Finally, the remaining noise photons are removed according to the fitting ground line and canopy top line interval. In this paper, the algorithm is applied to ICESat-2 airborne test data (MABEL). The experimental results show that the average de-noising accuracy of the algorithm is 94.5% for nighttime data, 96.3% for F1-score, 86.7% for daytime data and 91.7% for F1-score. The results show that the denoising parameters can be selected adaptively according to the photon density of the data However, the results also show that it can not achieve good results in areas where the density of signal photons and noise photons is excessively similar. However, the overall accuracy evaluation shows that the F1-score of all segments is 91%, 92% and 95% respectively in the three times denoising algorithm. The results show that the following two denoising algorithms can accurately remove most of the remaining noise photons which are not completely removed, and significantly improve the denoising accuracy of photon cloud, which provides a guarantee for the accurate extraction of the subsequent photon categories. The overall experimental results show that the algorithm has good denoising effect and stability for MABEL photon cloud data. In the section data of denoising, the qualitative results show that the photon classification algorithm in this paper can select the canopy vertex, ground point and forest photons from the photon data based on the denoising results. The final photon classification results show that the algorithm can extract the forest profile structure from the complex photon cloud data, and retain most of the signal photons in the canopy, which can provide some reference for the subsequent tree height extraction and biomass calculation of ICESat-2 data. © 2020, Science Press. All right reserved.