计算机科学
点云
人工智能
特征选择
特征(语言学)
选择(遗传算法)
水准点(测量)
最佳显著性理论
模式识别(心理学)
机器学习
特征提取
组分(热力学)
多样性(控制论)
数据挖掘
大地测量学
心理治疗师
地理
心理学
语言学
哲学
物理
热力学
作者
Martin Weinmann,Boris Jutzi,Stefan Hinz,Clément Mallet
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing
日期:2015-07-01
卷期号:105: 286-304
被引量:454
标识
DOI:10.1016/j.isprsjprs.2015.01.016
摘要
3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.
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