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Lidar的数据过滤是数据预处理的重要步骤,也是获取高精度数字高程模型的关键。现有很多典型的过滤算法都是基于地面种子点逐渐恢复地面的,比如自适应TIN过滤法、迭代线性预测法,分层恢复过滤法等。初始地面种子点选择的好坏将直接反映到过滤结果的精度上,尤其是对丘陵地区的地形恢复,影响更大。本文对基于种子点的TIN三角网加密法(TS过滤算法)进行了深入剖析,并在种子点选择方法上提出了一种基于移动窗口法和最小残差法的混合式种子点选择方法,有效地提高丘陵地区地形恢复的准确度。
Lidar’s data filtering is an important step in data preprocessing and the key to getting a high-precision digital elevation model. Many typical filtering algorithms are based on ground seed points to gradually restore the ground, such as adaptive TIN filtering, iterative linear prediction, hierarchical recovery filtering. The quality of initial ground seed selection will be directly reflected in the accuracy of the filtering result, especially in the hilly terrain recovery. In this paper, the TIN triangle mesh encryption algorithm based on seed points (TS filtering algorithm) is analyzed in depth, and a hybrid seed point selection method based on the moving window method and the least residual method is proposed on the seed point selection method, which is effective To improve the accuracy of terrain recovery in hilly areas.