多尺度特征和马尔可夫随机场模型的电力线场景点云分类法

及时、准确地监测电力线安全可以预防危险情况的发生。本文以机载点云为研究对象，提出了一种基于随机森林后验概率的马尔可夫随机场模型，用于电力线场景的点云分类。首先结合空间金字塔理论构建多尺度视觉分类特征以此描述空间点及其邻域的几何形状信息；接着利用随机森林分类器描述观测数据的概率分布，基于马尔可夫随机场模型建立顾及上下文信息的先验概率，从而构建一个多标记能量函数；最后利用多标记图割技术最小化能量函数完成分类标签优化。利用直升机巡线系统和小型无人机巡线系统获取的LiDAR点云数据来验证本文提出的模型。试验结果表明，该模型能够有效地分类场景中的电塔、电力线和植被且总分类正确率得到98%以上。与其他分类方法相比，本文提出的模型总体精度更高，尤其是电塔的分类优势明显。

Multi-scale Features and Markov Random Field Model for Powerline Scene Classification

Timely and accurate monitoring the safety of power line can prevent dangerous situations effectively.It is proposed that a Markov random field(MRF) model,into which a random forest classifier being integrated,to classify airborne LiDAR point cloud for power line scene.First,it is extracted that multi-scale visual features according to spatial pyramid theory to represent geometry information of the point and its neighborhood.And then a random forest classifier is used to describe the probability distribution of observed data.Meanwhile,contextual prior probability is established using MRF model,which is formulated as a multi-label energy function.Finally,the multi-label graph-cut technique is used to minimize energy function for optimizing the labels.It is validated the proposed method with LiDAR point cloud acquired by helicopter and mini-UAV power line inspection system.Experimental results demonstrate that the model can effectively classify pylon,power line and vegetation,with the overall accuracy of over 98％.Moreover,compared with other methods,the proposed model shows higher classification accuracy,particularly for the classification of the pylon.