Complex terrains in coastal zones and shallow water areas around islands and reefs can be quickly detected via airborne LiDAR bathymetry (ALB) technology. Due to equipment placement deviations and measurement uncertainty, measurement deviations can occur in the overlapping areas of adjacent strips, so it is particularly necessary to register point clouds. To address areas with few seafloor structures and to overcome the challenges of extracting features in water areas with small terrain changes, a registration method for ALB seafloor point clouds based on the adaptive matching of corresponding points is proposed. First, the normal vector zenith angle, curvature change, and omnidirectional variance in the seafloor points are calculated. Then, the corresponding points are adaptively matched according to the terrain feature similarity and distance constraints. Finally, the random sample consensus (RANSAC) algorithm and iterative closest point (ICP) algorithm are used for coarse registration and fine registration, respectively. The experimental results show that the method provides high accuracy and a uniform distribution of corresponding points. The root mean square error (RMSE) values when the registration method is applied to a flat area and coral reef area are 0.102 and 0.041 m, respectively. Compared with that of the ICP alignment algorithm, the accuracy of the proposed method is improved by 0.114 and 0.227 m, respectively, and compared with that of the normal distributions transform (NDT) algorithm, the accuracy is improved by 0.316 and 0.452 m, respectively; hence, the proposed method provides an effective solution for the registration of ALB data.

Alongside geometric information accepted widely, airborne laser bathymetry (ALB) typically records the radiometric properties of sensed targets and assists with strip registration, ground (sediment)-type classification, and geometric modeling. Continuous intensity stripping frequently occurs in the ALB-recorded intensity images due to automatic gain control (AGC), an established circuit to compensate for echo power variations caused by distance changes. This situation is exacerbated by the delayed gain control response and inadequate adaptability to target. To address this issue, an adaptive local joint-weighted method for continuous intensity stripping correction is designed. In this method, an index-sharing mechanism is established between intensity, point cloud, emission angle, and return number for rapid localization and intervention of intensity. Unordered intensities are divided into scan line units based on the signal emission period, and subsequent stripped intensity range identification is achieved by assessing the intensity difference between adjacent scan lines. Neighboring scan lines and neighborhood intensities are weighted to jointly reset the stripped intensity values. In the new method, a bidirectional shifting strategy is implemented to attenuate the degradation of the intensity correction accuracy due to the gradual accumulation of correction defects. From the two measurement missions using the ALB, it is concluded that the stripping intensity is mainly distributed in the region of maximum water depth detectable by the sensor and areas with mixed high and low returns. Compared with the original intensity, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the corrected intensity decreased by 35% and 40%, respectively, and the variation coefficient of the stripped intensity and the neighboring intensity decreased from 0.4 to 0.18.

The avalanche photodiode equipped in LiDAR heavily relies on the reliability of the gain amplifier to consistently output a sufficient voltage signal, enabling the weak return signal to be captured. However, in certain situations, an increase in voltage will lead to saturation, which distorts the full waveform and results in inaccurate coordinate and intensity readings, causing issues in various applications. To minimize data voids and eliminate artifacts related to the analysis of clipped waveforms, this study focuses on developing an automated framework [Detection and Restoration of Saturated Waveforms without Prior Information (DRSW)] for detecting and recovering saturated waveforms associated with high reflectivity or near-field targets without any prior knowledge. The framework designs four feature descriptors based on topographic, intensity, and waveform characteristics to differentiate between saturated and unsaturated signals, enabling automatic segmentation into two categories. To address the challenges posed by signal saturation-induced peak flattening and broadening, a combined halves-Gaussian model (CHGM) is crucially presented to describe the rising and falling edges of saturated waveform with different halves of normal Gaussian functions (halves-Gaussian functions). In the CHGM, background noise is segmented rather than as a whole to improve modeling accuracy. The effectiveness of CHGM in reconstructing saturated waveforms is evaluated through rigorous comparative experiments with cubic spline, Kriging, and Gaussian mixture model (GMM) methods. The results indicate that the maximum correction error of the CHGM is limited to within 7 DN, and the peak position remains unaltered without any shift.

Single-wavelength bathymetric LiDAR (532 nm) can provide seamless meter- and submeter-scale DEMs of both the terrestrial surface and seafloor. However, the mixed terrestrial and bathymetric surfaces obtained by this sensor are challenging for full-waveform (FW) signal detection. This study addresses the issues in two FW mixed surfaces: accurate classification of terrestrial and non-terrestrial waveforms from the original waveforms without auxiliary information, and flexible detection of peaks based on a new FW theoretical model. A novel FW signal-detection model (FWSD) for single-wavelength bathymetric LiDAR is proposed without complex feature extraction and iterative procedure through waveform classification and segmentation. The raw FW are divided into 5 categories for subsequent signal detection by utilizing a convolutional neural network that merges local descriptors with contextual information. The signal detection task is then split into FW segment recognition and peak extraction using a new FW model, which integrates a leapfrog sliding window FW segmentation, an improved extreme learning machine (ELM) algorithm for FW segment recognition and a flexible signal detection framework. In order to search for the optimal initial parameters for ELM, a self-annealing particle swarm optimization (SAPSO) algorithm is introduced, and the output weight is adjusted by online sequence to improve its generalization. When combined with the Richardson–Lucy deconvolution (RLD) algorithm, FWSD can be adapted to deal with shallow water waveforms. Finally, a test demonstration with an airborne dataset shows that FWSD has higher detection efficiency and higher accuracy than a generalized Gaussian model optimized using the Levenberg–Marquardt algorithm (LM-GGM) and RLD algorithm.

The airborne light detection and ranging (LiDAR) bathymetry (ALB) system is an extension of the ubiquitous topographic LiDAR mapping system and has been most simply characterized as adding a green laser to the infrared laser of topo systems. Due to the low point cloud density and monotonous objects in the scene, it is difficult to mosaicing the ALB strips. Therefore, the existing airborne laser scanning strip stitching algorithm has poor performance for ALB strips. In this article, a coarse-to-fine strip mosaicing model for ALB is proposed. The framework is fast and efficient and can handle large ALB data. An improved alpha shapes algorithm can fast and accurately determine the overlap region of strip is applied. Due to different data accuracy and spatial characteristics, the water area and land area are processed separately. A weight distribution-based coarse-to-fine registration model is designed for underwater areas. The topological constraint term is added to the nonrigid iterative closest point (ICP) cost function to prevent excessive deformation caused by outliers. The implicit B-spline surface fitting algorithm using the 3L algorithm and the least-squares trend surface fitting algorithm are applied separately to assign weights for overlapping strips to solve the limitation of no control or less control. Moreover, a random sample consensus (RANSAC)-ICP registration model characterized by the normal vector and curvature is constructed for land area. Finally, the comparisons with ICP highlight the superiority of the proposed approach in flexibility and accuracy. The root-mean-square error (RMSE) is 0.12 m and the maximum error is 0.36 m.