无人机DIM点云滤波处理是地物分类、地物单体化提取和地形特征分析的关键步骤,为解决高原山区因地形复杂而导致DIM点云滤波处理难度大和精度低等问题。选择以滇中高原恐龙谷南缘山区为试验区,首先利用DJI Phantom4 RTK采集影像数据,解算密集影像获取DIM点云；其次,考虑山体点与地面点有较大高程差,选择经典PTD滤波算法对实验区密集匹配点云进行滤波处理；最后,综合考虑实验区山顶和山脚存在较大高程差且山体两侧沟壑丛生,山体两侧地面点易被识别为非地面点,提出以脊-谷交汇地形特征点为PTD滤波算法的种子点,在山体两侧精细化构建不规则三角网的改进PTD滤波算法。结果表明:1)PTD滤波算法得到地面点较为完整保留整个实验区,但明显的地物如山体两侧低矮植被和山脚蔬菜大棚基本未被剔除,且山体部分的地面点易被识别为非地面点而在出现山体K1、K2、K3区域的空洞现象。2)针对恐龙谷南缘山区复杂地形,提出以脊-谷交汇地形特征点为PTD滤波算法的种子点,在山体两侧精细化构网,山体低矮植被部分清除,相对于PTD滤波算法蔬菜大棚大面积被清除。并且山体两侧地面点得到较为完成保留,未出现明显点云空洞的现象。
DIM point cloud filtering is a key step in feature classification,feature monolithic extraction and terrain feature analysis.In order to solve the difficulty and low accuracy of DIM point filtering due to complex terrain in highland mountains,an improved PTD filtering algorithm is proposed.The mountainous areas on the southern edge of the dinosaur valley in the Central Yunnan Plateau are chosen as the experimental area.Firstly,DJI Phantom 4 RTK is used to collect the image data and solve the dense image to obtain the DIM point cloud.Then,considering the large difference in elevation between the summit and the foot of the mountain,and the ground points on both sides of the mountain are easily identified as non groundpoints,the topographic feature point of ridge valley intersection is the seed point of PTD filtering algorithm,and the improved PTD filtering algorithm refine the irregular triangular network on both sides of the mountain.The results show that:1) the ground points obtained by the PTD filtering algorithm are more completely preserved throughout the experimental area,but obvious features such as low vegetation on both sides of the mountain and vegetable trellises at the foot of the mountain are basically not eliminated,and the ground points on parts of the mountain are easily identified as non ground points and hollow phenomena in the K1,K2 and K3 areas of the mountain.2) For the complex terrain of the mountainous area on the southern edge of the dinosaur valley,the ridge valley intersection terrain feature points are proposed as the seed points of the PTD filtering algorithm.On both sides of the mountain refinement network,the mountain low vegetation partly cleared.Vegetable shed large area is cleared compared to the PTD filtering algorithm.The ground points on both sides of the mountain are preserved,and no obvious point cloud voids are observed.
LUO Wei-dong, GAN Shu, YUAN Xi-ping, GAO Sha, BI Rui, YUAN Xin-yue. Experimental study on the improvement of PTD filtering for densepoint clouds of UAV in the southern margin of dinosaur valley[J]. LASER & INFRARED,2022,52(6):938~944