利用合成算法从LiDAR数据提取屋顶面

Automatic Extraction of Building Roofs from LiDAR Data Using a Hybridized Method

区域增长法和随机抽样一致性(RANSAC)算法是从LiDAR数据提取屋顶面时常用的两类方法,但这两种方法都存在某些缺陷,使它们的应用受到了一定限制。针对LiDAR数据中建筑物脚点的特点,提出了一种融合以上两种方法优点于一体的合成算法。1根据脚点的法向量和粗糙度特征进行屋顶面粗提取;2在屋顶面粗提取结果的基础上,利用基于先验知识的局部采样策略和区域增长方式对传统随机抽样一致性算法进行扩展,实现屋顶面自动提取;3采用投票法解决屋顶面竞争问题,提高屋顶面的提取精度。实验结果表明,本文设计的合成算法能够有效地提取建筑物屋顶面。

Two types of approach called region-growing and random sample consensus have been proposed for automatic building roof extraction. They both, however, have drawbacks. In this paper, an hybridized method is proposed to take advantage of both algorithms＇ strengths so that building roofs can be extracted more precisely and efficiently. First, we calculate the normal and rough features from LiDAR data for the coarse extraction of building roofs. Second, precise roof-extraction is performed using an extended RANSAC method which takes the coarse extraction results as the prioriknowledge, and integrates a region growing method. Finally, a poll strategy is adopted to solve the competition problem. The experimental results show that our method can extract intact building roofs in a highly automated manner.