Layered Denoising and Classification of Photon Point Cloud Data From ICESat-2 in Forest Area

Junfan Bao, Ningning Zhu, Zhen Dong, Sheng Nie, Wenxia Dai, Ruibo Chen, Ruixiong Kou, Bisheng Yang

Abstract: Ice, Cloud, and Land Elevation Satellite (ICESat-2) carries the Advanced Topographic Laser Altimeter System (ATLAS), which enhances along-track sampling density but introduces substantial noise in photon point cloud data. Therefore, this study establishes a denoising and classification feature parameter system grounded in the 3-D spatial distribution characteristics of photon point clouds. Modeling is conducted in two layers: one layer for upper noise photons and canopy signal photons, and another layer for lower noise photons and ground signal photons. Machine learning and neural network algorithms are utilized to denoise and classify the original photon point clouds from ICESat-2, aiming to obtain a transferable and universally applicable supervised classification model for denoising photon point clouds. Recall, precision, and the harmonic mean of recall and precision ( $F1$ score) are used as evaluation metrics to verify the accuracy of local, transfer, and global models. The results indicate that under various forest types and external conditions, the proposed photon point cloud layered denoising and classification model (LDCM) outperforms the differential regressive and Gaussian adaptive nearest neighbor (DRAGANN; ICESat-2 ATL08 production algorithm), ordering points to identify the clustering structure (OPTICS), and adaptive elevation difference thresholding (AEDTA) algorithms in terms of accuracy. Compared to the DRAGANN algorithm, the maximum accuracy improvement is 60%, with an average improvement of approximately 20%; compared to the OPTICS algorithm, the maximum accuracy improvement is 36%, with an average improvement of about 28%; compared to the AEDTA algorithm, the maximum accuracy improvement is 27%, with an average improvement of about 14%. The $F1$ score for the validation set of the machine learning and neural network algorithms is above 0.94, with the categorical boosting (CatBoost) algorithm achieving the best performance. Both the transfer model and the global model have $F1$ scores above 0.90. Therefore, the proposed photon point cloud LDCM not only demonstrates excellent classification accuracy but also exhibits good transferability and general applicability.