基于遗传算法的星载激光全波形分解

全波形激光雷达的回波中包含着地物目标的垂直结构特征信息，传统的全波形数据处理方法在提取这些信息时过于依赖初始参数，导致地形复杂地区的数据可利用率低、准确率低。针对这一问题，提出了一种基于遗传算法的波形分解方法。改进后的处理算法无需提供精确的初始参数，用概率性传递规则代替确定性规则，具有全局寻优特点。并以高分七号卫星激光全波形数据进行试验。结果证明，基于改进后的波形处理方法拟合的回波波形与预处理后波形的相关系数在99%以上。文中对森林地区最大树高的反演与ICESat-2的ATL08产品中的森林冠层高度参数进行对比，两者相关系数为0.85，中误差为1.1 m，表明该方法可以较准确地提取复杂波形的特征信息。

Full waveform decomposition of spaceborne laser based on genetic algorithm

The echo of the all waveform lidar contains the vertical structure information of the ground object. The traditional all waveform data processing methods rely too much on the initial parameters when extracting these information, resulting in the low availability and accuracy of the data in the terrain complex area. To solve this problem, a waveform decomposition method based on genetic algorithm was proposed. The improved algorithm did not need to provide accurate initial parameters, and used probabilistic transfer rules instead of deterministic rules, which had the characteristics of global optimization. The experiment was carried out with the full waveform data of GF-7 satellite laser. The results show that the correlation coefficient between the echo waveform fitted by the improved waveform processing method and the preprocessed waveform is more than 99%. The inversion of the maximum tree height in the forest area was compared with the forest canopy height parameter in the ATL08 data of ICESat-2. The correlation coefficient is 0.85 and the mean square error is 1.1 m, which shows that the method can extract the feature information of complex waveform more accurately.