The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) can measure the elevations of the Earth’s surface using a sampling strategy with unprecedented spatial detail. In the daytime of mountainous areas where the signal–noise ratio (SNR) of weak beam data is very low, current algorithms do not always perform well on extracting signal photons from weak beam data (i.e., many signal photons were missed). This paper proposes an effective algorithm to extract signal photons from the weak beam data of ICESat-2 in mountainous areas. First, a theoretical equation of SNR for ICESat-2 measured photons in mountainous areas was derived to prove that the available information provided by strong beam data can be used to assist the signal extraction of weak beam data (that may have very low SNR in mountainous areas). Then, the relationship between the along-track slope and the noise level was used as the bridge to connect the strong and weak beam data. To be specific, the along-track slope of the weak beam was inversed by the slope–noise relationship obtained from strong beam data, and then was used to rotate the direction of the searching neighborhood in the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. With the help of this process, the number of signal photons included in the searching neighborhood will significantly increase in mountainous areas and will be easily detected from the measured noisy photons. The proposed algorithm was tested in the Tibetan Plateau, the Altun Mountains, and the Tianshan Mountains in different seasons, and the extraction results were compared with the results from the ATL03 datasets, the ATL08 datasets, and the classical DBSCAN algorithm. Based on the ground-truth signal photons obtained by visual inspection, the parameters of the classification precision, recall, and F-score of our algorithm and three other algorithms were calculated. The modified DBSCAN could achieve a good balance between the classification precision (93.49% averaged) and recall (89.34% averaged), and its F-score (more than 0.91) was higher than that of the other three methods, which successfully obtained a continuous surface profile from weak beam data with very low SNRs. In the future, the detected signal photons from weak beam data are promising to assess the elevation accuracy achieved by ICESat-2, estimate the along-track and cross-track slope, and further obtain the ground control points (GCPs) for stereo-mapping satellites in mountainous areas.

中文翻译：

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利用山区强束数据提供的信息提取ICESat-2弱束数据的信号光子提取方法

“冰，云和陆地高程卫星2（ICESat-2）”可以使用具有前所未有的空间细节的采样策略来测量地球表面的高程。在山区的白天，弱光束数据的信噪比（SNR）非常低，当前的算法在从弱光束数据中提取信号光子时并不总是表现良好（即，错过了许多信号光子）。提出了一种有效的从山区ICESat-2弱光束数据中提取信号光子的算法。首先，推导了ICESat-2山区测量光子的SNR的理论方程，以证明强束数据提供的可用信息可用于辅助弱束数据的信号提取（在山区可能具有非常低的SNR）地区）。然后，沿轨道斜率与噪声水平之间的关系被用作连接强光束和弱光束数据的桥梁。具体来说，通过从强波束数据获得的斜率-噪声关系来反转弱波束的沿轨道斜率，然后将其用于在基于噪声的基于密度的空间聚类中旋转搜索邻域的方向（DBSCAN）算法。借助此过程，搜索邻域中包含的信号光子数将在山区显着增加，并且很容易从测量的噪声光子中检测出来。在不同的季节对青藏高原，阿尔屯山和天山山脉的算法进行了测试，并将提取结果与ATL03数据集的结果进行了比较，ATL08数据集和经典的DBSCAN算法。根据目视检查得到的地面信号光子，对分类精度，召回率和计算了我们算法的F分数和其他三个算法。改进后的DBSCAN可以在分类精度（平均93.49％）和召回率（平均89.34％）之间取得良好的平衡，其F得分（大于0.91）高于其他三种方法，从而成功地获得了连续性。 SNR极低的弱光束数据产生的表面轮廓。将来，从弱光束数据中检测到的信号光子有望用于评估ICESat-2所达到的仰角精度，估计沿航迹和跨航迹的坡度，并进一步获得用于立体映射的地面控制点（GCP）山区的人造卫星。