现有利用激光测高波形的地物分类方法绝大多数基于机器学习的分类原理, 是一种基于经验的分类方法。从激光回波的理论模型出发, 通过推导纯海水表面回波和含有海冰的表面回波的解析模型, 对纯海水回波和含有海冰回波逐个采样点按时域距离加权计算总振幅差异值, 以该差异值作为依据建立一种半解析型的海水、海冰分类方法; 通过机载LiDAR将在格陵兰北部海冰区的实测点云数据判断GLAS激光脚点对应的地面类型, 对GLAS在该区域实测波形进行基于论文方法的分类准确性验证; 结果显示, 在剔除饱和波形影响后, 分类总体精度OA大于95%, Kappa系数接近0.89, 具有非常好的分类效果。论文将使得激光测高仪地物类型分类方法由基于机器学习为依据向半理论解析模型为依据的分类方向延伸, 为后续基于激光回波数据的地物分类方法提供重要的参考思路。

Current terrain classification methods using waveforms of a laser altimeter are mainly based on machine learning, which is an empirical model. Starting from the theoretical model of the laser waveform, by deriving the analytic model of laser waveforms of sea surface and sea-ice surface, the sum of the amplitude differences between the sea waveform and sea-ice waveform was calculated according to one by one sampling point based on the temporal-distance weighting; and the sea or sea-ice waveform was classified by the calculated difference value. The terrain types corresponding to GLAS laser footprints in the north sea-ice area of Greenland were obtained by analyzing the point clouds captured by an airborne LiDAR, and in this area, the GLAS measurement waveforms were used to validate based on the classification method proposed in the paper. The results indicate that after eliminating the effect of saturated waveforms, the overall accuracy OA is better than 95% and the Kappa coefficient is approximately 0.89, which achieves a high classification accuracy. The new method extends the terrain type classification from taking the machine learning as basis to taking the semi-theory analytic model as basis for a laser altimeter, which can be an important reference to the terrain type classification based on laser waveform successively.