A Stochastic Geometry Method for Pylon Reconstruction from Airborne LiDAR Data
A Stochastic Geometry Method for Pylon Reconstruction from Airborne LiDAR Data
复制标题
机载激光雷达数据重建塔架的随机几何方法
DOI：
10.3390/rs8030243
复制
发表时间：
2016-03-01
期刊：
影响因子：
5
通讯作者：
Wang, Chisheng
中科院分区：
文献类型：
--
作者：
Guo, Bo;Huang, Xianfeng;Wang, Chisheng
从遥感数据中检测和重建目标是摄影测量和遥感领域的研究热点。电力工程设备监测是对电力安全的重要保障。在本文中，我们介绍了一种新的方法重建自立塔广泛使用的高压电力线系统从机载激光雷达数据。我们的工作是从一个3D参数化模型库中构建塔架，这些模型使用基于随机几何的多面体表示。首先，采用自动分类的方法从数据集中提取塔架的激光点。然后定义由两项组成的能量函数：第一项测量对象相对于数据的充分性，第二项具有基于先验知识支持或惩罚某些配置的能力。最后，估计进行最小化的能量，使用模拟退火。我们使用马尔可夫链蒙特卡罗采样器，导致对象的最佳配置。本文的两个主要贡献是：（1）建立了一个框架，自动塔架重建;（2）高效的全局优化。通过能量优化，可以精确地对塔架进行改造。实验产生令人信服的结果验证了所提出的方法使用复杂结构的数据集。
Object detection and reconstruction from remotely sensed data are active research topic in photogrammetric and remote sensing communities. Power engineering device monitoring by detecting key objects is important for power safety. In this paper, we introduce a novel method for the reconstruction of self-supporting pylons widely used in high voltage power-line systems from airborne LiDAR data. Our work constructs pylons from a library of 3D parametric models, which are represented using polyhedrons based on stochastic geometry. Firstly, laser points of pylons are extracted from the dataset using an automatic classification method. An energy function made up of two terms is then defined: the first term measures the adequacy of the objects with respect to the data, and the second term has the ability to favor or penalize certain configurations based on prior knowledge. Finally, estimation is undertaken by minimizing the energy using simulated annealing. We use a Markov Chain Monte Carlo sampler, leading to an optimal configuration of objects. Two main contributions of this paper are: (1) building a framework for automatic pylon reconstruction; and (2) efficient global optimization. The pylons can be precisely reconstructed through energy optimization. Experiments producing convincing results validated the proposed method using a dataset of complex structure.