Airborne LiDAR Bathymetry (ALB) system can quickly and efficiently obtain the integrated overwater and underwater data of sea islands, reefs and their adjacent areas.However, due to the fact that most of the measurement areas are shallow near-shore waters with slow terrain changes, the obtained point cloud is low in density and large in thickness, resulting in rare registration characteristics.Few studies have been done on the registration of ALB data due to the difficulty in extracting their homonymous features.To address this problem, we employ three registration methods including Fast Point Feature Histograms (FPFH), Longest Common Subsequence (LCSS) and Generalized Iterative Closest Point (GICP) to register the ALB point cloud data in the South China Sea.The registration performance comparison shows that the LCSS line sequence outperforms the other two methods in registration accuracy and reliability.Moreover, the LCSS can tackle the problems of single information and noise in the corresponding feature matching, improve the robust estimation of corresponding points in the feature curve, and enhance the robustness of airstrip data registration.It can be concluded that the LCSS is an effective solution for ALB data registration.