强度体元基元下的机载LiDAR 3D滤波

针对现有基于二值体元基元的机载LiDAR三维（3 Dimensional, 3D）滤波算法仅利用了数据的高程特征、无法区分相连的地面和非地面目标的问题,提出了一种基于强度体元基元的机载LiDAR 3D滤波算法。首先,基于计算几何理论,将机载LiDAR数据规则化为强度（体元内激光点的量化平均反射强度值）体元结构。然后,基于3D连通区域构建理论,选取局部高程最低的非0值体元为地面种子进而搜寻并标记与地面种子,空间连通、反射强度及坡度值均接近的连通区域内体元为地面体元。算法综合利用LiDAR数据的高程、反射强度及坡度特征,支持相连但强度不同的地面和非地面目标的区分,为相连的地面和非地面目标的精确区分提供更有效的信息。算法有助于提高滤波精度,并扩展基于体元基元的3D滤波算法适用于更复杂的场景。实验基于ISPRS提供的专门用于滤波算法测试的LiDAR点云数据测试了“空间邻域尺度”参数的敏感性及提出算法的精度。定量评价的结果表明：51邻域为最佳邻域尺度;提出算法的平均Kappa系数在相对平坦、陡坡及不连续地形分别为0.9380、0.7749和0.6866;从总误差测度来看,提出算法对比经典的Axelsson算法改进了15个样本中的7个样本精度,且其对比其他二值体元基元下的滤波算法平均总误差最低。

Airborne LiDAR 3D Filtering based on Intensity Voxel Primitive

The existing binary voxel primitive based 3-Dimensional (3D) filtering algorithms for airborne Light Detection And Ranging (LiDAR) data, which use only elevation features, cannot distinguish between connected ground and non-ground objects. As a result, an airborne LiDAR 3D filtering algorithm based on intensity voxel primitive was proposed in the present study. First, airborne LiDAR data were regularized into intensity voxel structure based on computational geometry theory, in which intensity value of the voxel corresponds to the quantized intensity of the LiDAR point(s) within the voxel. Second, based on the theory of 3D connected region construction, the non-zero voxels with the lowest local elevation was selected as ground seeds, and then ground seeds and their connected regions where the voxels are 3D connected and have similar grayscales and slope with seeds were labelled as ground voxels. The proposed algorithm makes comprehensive use of the features of elevation, reflection intensity, and slope, supports 3D filtering in areas where the ground are adjacent to non-ground objects but with different intensities, and provides more effective information for the accurate distinction between the connected ground and non-ground objects. The proposed algorithm is helpful to improve the filtering accuracy and extend voxel primitive based 3D filtering algorithm for more complex scenes. The International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark dataset, which contains a variety of features that is expected to be difficult for automatic filtering, were used to analyze the sensitivity of “spatial adjacency size” parameter in the proposed algorithm and to assess the accuracy of the proposed algorithm quantitatively. Results show: （1） The 51-adjacency was the optimal spatial adjacent size. （2） The average Kappa coefficient of the proposed algorithm was 0.9380, 0.7749, and 0.6866 in relatively flat, steep slope, and discontinuous terrain areas, respectively. （3） In terms of total error, the proposed algorithm improved the accuracy of 7 out of 15 samples and had a higher accuracy than all other binary voxel primitive based 3D filtering algorith-ms on average.