激光与光电子学进展, 2016, 53 (8): 082802, 网络出版: 2016-08-11
一种机载LiDAR点云分类的自适应特征选择方法 下载: 531次
A Method of Adaptive Feature Selection for Airborne LiDAR Point Cloud Classification
遥感 机载LiDAR 自适应特征选择 点云分类 随机森林 支持向量机 remote sensing airborne LiDAR adaptive features selection point cloud classification random forest support vector machine
摘要
不同地形条件下,不同的特征组合、特征维数对点云的分类效率及分类结果有不同的影响。提出了一种机载LiDAR点云分类的自适应特征选择方法,该方法依据地形起伏情况对整体点云数据进行区域划分,自适应选择适宜该区域LiDAR点云分类的特征集合。为了验证这种特征选择方法的有效性,利用优选后的特征集合,分别采用随机森林和支持向量机算法进行分类实验验证,实验结果表明,在不同地形条件的区域里,适合LiDAR点云分类的特征集合不同。该方法可以有效地降低特征维数,缩短运算时间,且分类精度较高。
Abstract
In different terrain conditions, different feature combinations and dimensions have different influences on the effectiveness and accuracy of classification. A method is proposed to select the airborne LiDAR point cloud classification with adaptively feature selection. The whole point cloud is divided into different regions in accordance with the terrain conditions, and the suitable feature set is selected adaptively for classification. In order to evaluate the effective of this method, the random forest method and support vector machine classification method are used to classify the experimental data with the feature set after optimization. Experimental result shows that the suitable feature set for classification in different areas are different. The proposed method can reduce the feature dimensions effectively, shorten time consumption, and achieve high classification accuracy.
张爱武, 肖涛, 段乙好. 一种机载LiDAR点云分类的自适应特征选择方法[J]. 激光与光电子学进展, 2016, 53(8): 082802. Zhang Aiwu, Xiao Tao, Duan Yihao. A Method of Adaptive Feature Selection for Airborne LiDAR Point Cloud Classification[J]. Laser & Optoelectronics Progress, 2016, 53(8): 082802.