Tran Thi Huong Giang, Ressl Camillo, Pfeifer Norbert
Department of Geodesy and Geoinformation, Technische Universität Wien, Gußhausstraße 27-29, 1040 Vienna, Austria.
Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Hanoi 10000, Vietnam.
Sensors (Basel). 2018 Feb 3;18(2):448. doi: 10.3390/s18020448.
This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground.
本文提出了一种用于三维点云变化检测（CD）的新方法。它通过机器学习在一个步骤中结合了分类和变化检测。两个时期的点云数据被合并以计算四种类型的特征：描述点分布的特征、与相对地形高程相关的特征、激光扫描多目标能力特有的特征以及结合两个时期的点云以识别变化的特征。所有这些特征在点中合并，然后获取训练样本以创建用于监督分类的模型，随后将其应用于整个研究区域。对于八个类别的两个时期（丢失的树木、新树木、丢失的建筑物、新建筑物、变化的地面、未变化的建筑物、未变化的树木和未变化的地面），最终结果的总体准确率均超过90%。
