The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is the world’s first satellite-borne photon-counting laser altimeter with unprecedented detection performance. Noise removal is an important process applied to raw data and determines the quality of the end product. Assuming that the sparse spatial distribution of noise photons makes them more easily isolated than signal photons, we propose a noise-removal algorithm without input parameters based on quadtree isolation. MATLAS was used to evaluate the performance of our algorithm. We compare our algorithm to the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm. Experimental results show that our algorithm accurately extracts signal photons from raw data and is superior to the improved DBSCAN in accuracy and time efficiency. This novel algorithm makes it possible to efficiently remove noise from photon-counting light detection and ranging (LiDAR) data.

The new generation of spaceborne laser altimeter, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which can simultaneously generate laser reflections on the water surface and underwater, is a potential data source for exploring water depth in nearshore environments. To achieve this scientific goal, accurate bathymetric photon extraction is needed. This study proposed a pre-pruning quadtree isolation (PQI) method with changing threshold. Firstly, the pre-pruning step is introduced. Photons are transformed into different levels in the isolated quadtree structure according to spatial density. Then, the frequency histogram of photon elevation and isolated level (IL) is generated, the IL thresholds in different depth ranges are calculated by the Otsu method, and the bathymetric photons are extracted. The results in the Culebra archive show that this method achieved a 92.71% F1 score. Noise rate and water depth are the main factors affecting the extraction of sounding photons. When the photon density gradually increases from 2–4 pts/m to 6–8 pts/m, the F1 score of PQI decreases by no more than two percent. In different depth ranges, the extraction results of PQI are also better than those of comparison methods. Therefore, PQI can provide reliable theoretical support for nearshore areas lacking water depth data.

The refraction phenomenon causes ICESat-2 nearshore bathymetry errors by deviating seafloor photons’ coordinates. A refraction correction method based on ATL03 photon parameter tracking was proposed to improve the ICESat-2 bathymetry accuracy. The method begins by searching for sea–air intersections using photon parameters. Instead of relying on mathematical operations, it uses logical relations to establish a relationship between the seafloor and the surface, which improves efficiency. Then, a refraction correction model is designed based on Snell’s law for different sea surface fluctuations. This model is clear and suitable for scholars new to refraction correction. The results show the effectiveness of the proposed method since the RMSE is reduced by 1.8842 m~5.2319 m compared with the raw data. Our method has better tolerance than other methods at different water depth ranges.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), launched in September 2018, has been widely used in forestry and surveying. A high-accuracy digital elevation model (DEM)/digital surface model (DSM) for terrain matching can effectively evaluate the ICESat-2 design requirements and provide essential data support for further study. The conventional terrain-matching methods regard the laser ground track as a whole, ignoring the individual differences caused by the interaction of photons during flight. Therefore, a novel terrain-matching method using a two-dimensional affine transformation model was proposed to describe the deformation of laser tracks. The least-square optimizes the model parameters with the high-accuracy terrain data to obtain the best matching result. The results in McMurdo Dry Valley (MDV), Antarctica, and Zhengzhou (ZZ), China, demonstrate that the proposed method can verify geolocation accuracy and indicate that the average horizontal accuracy of ICESat-2 V5 data is about 3.86 m in MDV and 4.67 m in ZZ. It shows that ICESat-2 has good positioning accuracy, even in mountainous areas with complex terrain. Additionally, the random forest (RF) model was calculated to analyze the influence of four factors on geographic location accuracy. The slope and signal-to-noise ratio (SNR) are considered the crucial factors affecting the accuracy of ICESat-2 data.

Existing airborne laser scanning (ALS) point cloud semantic segmentation approaches are limited by their overreliances on sufficient point-wise annotations that further confine their generalization ability to new scenes. To overcome these problems, a novel three-stage multi-prototype relational network (Thr-MPRNet) is proposed for few-shot ALS point cloud semantic segmentation by transferring knowledge from well-annotated photogrammetric point clouds. In MPRNet, a 3D few-shot learning structure containing a feature learner and a relation learner is built to learn meta-knowledge from multiple point-wise tasks, and a multi-prototype generator is designed to represent the semantic distribution of point clouds that can dynamically adapt to large-scale scenarios. Then, to transfer knowledge across different domains, MPRNet is trained in a unified framework with three task-based learning stages. Prior knowledge is first meta-learned from the source photogrammetric point clouds and then transferred to novel target datasets with a few labeled ALS point clouds. Finally, the MPRNet can be flexibly generalized to the unlabeled target ALS point clouds without further retraining from scratch. In the experiments, the SensatUrban dataset is used as the source photogrammetric point clouds, and two ALS point cloud datasets (ISPRS and DALES) are used to evaluate the few-shot semantic segmentation ability of the proposed method. The experiments demonstrate that Thr-MPRNet obtains promising generalization performance on different target datasets. More importantly, it outperforms supervised networks with 10% labeled samples. In summary, the proposed method achieves state-of-the-art cross-domain semantic segmentation performance and greatly alleviates the dependence on ALS point cloud annotations.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) equips with a novel photon-counting LiDAR system, which can generate underwater reflections in nearshore environments. However, due to the water reflection, scattering, and absorption, the distribution of bathymetric photons in the nearshore data varies with depth. The existing bathymetric photon extraction algorithms need more adaptability to seafloor topography. The changing density of bathymetric photons and the fluctuation of underwater topography make the noise removal of nearshore data full of challenges. This study proposed a bathymetric photon extraction algorithm using pre-pruning quadtree isolation (PQI). Firstly, the pre-pruning step judges whether to stop the growth of quadtree in advance during quadtree isolation (QI) to avoid excessive division of noise photons. Secondly, the maximum inter-class variance algorithm (also called the Otsu method) obtains the best threshold of isolation depth and extracts bathymetric photons. The algorithm was tested on the Florida coast. The results show that the PQI algorithm can wholly and accurately extract bathymetric photons with different acquisition times from the data. The F1-score of the extracted results is 93.96%. This study provides an intelligent solution to processing bathymetric data in nearshore environments worldwide.