With the purpose of better adapt to different types of complex terrain, this paper proposes an experimental scheme for ground image and LiDAR point cloud decision-level fusion block filtering and DEM generation. Firstly, the ground image and point cloud are matched through homologous feature points linear transformation to obtain more spectral texture information from the image. Then, based on decision-level fusion, the original point cloud is segmented into several independent blocks. The IPTD filtering algorithm is improved and optimized by using a search method according to the different multi-dimensional detail features of each block region. Finally, the filtered total ground points are interpolated to obtain a DEM model. The results show that compared with other overall filtering schemes, the DEM generated by this algorithm has the highest accuracy with an average absolute error and root mean square error of 0.100 meters and 0.157 meters respectively, greatly optimizing the generation effect of DEM.
针对地面激光扫描及无人机航摄技术在实际外业测量中受视场角限制或遮挡等因素的影响而难以获取待测区域完整的点云数据的问题,本文在经典ICP算法的基础上,提出了一种顾及高程差异和点云密度的激光点云与影像点云融合方法.通过差分数字高程模型对点云进行分块,并基于点云密度选取融合范围,将分块后的影像点云配准到激光点云的孔洞和稀疏区域.本文方法能够提高激光点云与影像点云的融合效果,保持激光点云的精度并保留更多的细节特征,实现激光点云与影像点云的高质量融合.
针对现有LiDAR地面点滤波算法对复杂地形地物适应性不强的问题,本文提出了一种融合点云与地面影像分块滤波的方法.首先,将地面影像与点云匹配,使点云从影像中获取更多的光谱纹理信息.然后,分析地物光谱、林地相对密度、点云高程特征、地面DSM模型及其坡度,并基于决策级融合将原始点云切割成若干独立的区块.最后,根据每块区域不同的多元细节特征,对IPTD滤波算法进行改进并利用搜索法优化参数,得到最优且稳健的结果.利用滤波后的总地面点通过插值算法得到的DEM模型和相关试验验证了本文算法的优越性.
