在海陆交接区域，激光光斑中可能同时存在海洋和陆地，从而产生海陆混合波形。如果能准确识别出海陆混合波形，则可以利用这些混合波形来提取水边线的精确位置。本文顾及海陆混合波形的存在，提出一种基于机载海洋激光雷达(Airborne oceanic LiDAR, AOL)红外激光的海陆混合波形识别方法，并进一步提出利用海陆混合波形的水边线提取方法。首先，综合提取AOL红外激光全波形特征并进行特征降维；其次，对AOL波形进行模糊分类，得到隶属度矩阵，根据Otsu最大类间方差法确定海陆混合波形；再次，利用DBSCAN密度聚类算法对误分类的海陆混合波形进行识别和剔除；然后，结合海陆混合波形对应激光点的位置信息，利用PAEK平滑算法得到水边线位置信息。最后，对激光雷达方程进行扩展，给出红外激光海陆混合雷达方程的表达形式，并采用雷达方程理论分析和实测波形数据验证相结合的方式对海洋、陆地、海陆混合波形进行差异分析，为红外激光光斑内海陆并存引起的海陆混合波形研究提供理论基础。利用Optech CZMIL系统采集的实测AOL数据集验证了本文红外激光海陆混合波形法以及海陆混合雷达方程的正确性和有效性。与传统的AOL高程阈值法相比，本文提出的AOL混合波形法将水边线提取偏差的均值和标准差分别降低了24.07%和9.76%，SSIM指数提高了0.031，为AOL潮滩水边线提取提供了一种全新方式。

Ocean–land interfaces (OLIs) are instantaneous boundaries between fluctuating ocean surfaces and land and provide fundamental information for scientific research on ocean hydrology, ocean–land resource management, and sea level rise. However, detecting OLIs with high accuracy and resolution in automated manners is a challenging task. Airborne oceanic LiDARs (AOLs) are high-resolution, efficient, and flexible measurement systems that can be used for integrated ocean and land measurements. In areas where the ocean meets land, both ocean and land may exist in the laser spot of AOL, resulting in mixed ocean–land waveforms. If these mixed waveforms can be accurately identified, they can be used to detect the precise location of the ocean–land interface. Considering the existence of mixed ocean–land waveforms, a mixed waveform method based on AOL mixed ocean–land waveforms is proposed for ocean–land interface determination in this paper. First, the waveform features of the AOL infrared lasers are extracted, and principal component analysis is performed to reduce redundant features. Second, the waveform features are used to classify the AOL waveforms to obtain a membership matrix, and the Otsu method is used to determine the mixed ocean–land waveforms. Third, the DBSCAN algorithm is used to identify and eliminate misclassified mixed waveforms. Fourth, the PAEK algorithm is applied to smooth the laser points corresponding to the mixed ocean–land waveforms and output the ocean–land interface. Finally, the expression of the infrared laser-radar equation for mixed ocean and land is provided, and a method combining theoretical analysis and measured data verification is used to analyze the differences between ocean, land, and mixed ocean and land waveforms. The correctness and effectiveness of the methods proposed in this paper were verified via raw AOL datasets collected by the Optech CZMIL system. Compared with the traditional AOL elevation threshold method, the proposed AOL mixed waveform method reduced the mean and standard deviation of the ocean–land interface bias by 24.07% and 9.76%, respectively, and improved the SSIM index by 0.031, providing a new approach for detecting the ocean–land interface on tidal flats via AOL. The coexistence of water and land within AOL laser spots generates mixed ocean–land waveforms, and identifying these mixed waveforms has important theoretical and practical value. This study proposes an identification method for infrared laser mixed waveforms based on waveform fuzzy classification and Otsu threshold determination and an ocean–land interface extraction method using those identified mixed waveforms. Furthermore, this study extends the laser-radar equation by proposing an infrared laser-radar equation for infrared laser interactions with mixed ocean and land, providing a theoretical basis for studying mixed infrared laser waveforms. On the basis of this equation, a differential analysis was conducted on the ocean, land, and mixed ocean–land waveforms. The correctness and practicality of the infrared laser-radar equation for mixed ocean and land were verified via visualization analysis results of raw waveform data.