Adaptive OPTICS Algorithm Denoising ICESat-2 Laser Photon Data

The ATL03 data provided by the ICESat-2 satellite contain a significant amount of noise photons. This noise profoundly impacts the extraction and application of signal photons. Presently, the most widely used density-based clustering method faces two main challenges: one relates to parameter configuration, and the other pertains to the algorithm’s elevated complexity. To address the previously mentioned challenges, this study proposes adaptive ordering points to identify the clustering structure (adaptive OPTICS) algorithm that comprises two denoising stages: rough denoising and fine denoising. In the rough denoising stage, a histogram thresholding technique is used to eliminate a large number of noise photons, thereby reserving computational resources and time for further denoising steps. In the fine denoising stage, the K-nearest neighbors (KNN) algorithm is used to calculate the average distance and establish an dataset, named as D. Following this, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is applied to the dataset D to perform clustering operations, dynamically determining the minimum sample size for the OPTICS algorithm. A section of Zhengzhou is selected as the experimental area for this research. The experimental results show that the accuracy of the denoising process using the adaptive OPTICS algorithm reaches 93.7%, outperforming both the conventional OPTICS algorithm and the ATL03 algorithm.