Effectiveness of Training Sample and Features for Random?Forest?on Road Extraction from?Unmanned?Aerial?Vehicle-Based Point Cloud

编号 030033803

推送时间 20220411

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Effectiveness of Training Sample and Features for Random?Forest?on Road Extraction from?Unmanned?Aerial?Vehicle-Based Point Cloud

来源期刊 TRANSPORTATION RESEARCH RECORD

期 第338期

发表时间 20210819

关键词 LAND-COVER CLASSIFICATION;  MOBILE LIDAR;  IMAGE CLASSIFICATION;  SELECTION;  SURFACE;  ACCURACY;  IMPACT; 

摘要 The accuracy of random forest (RF) classification depends on several inputs. In this study, two primary inputs-training sample and features-are evaluated for road classification from an unmanned aerial vehicle-based point cloud. Training sample selection is a challenging step since the machine learning stage of the RF classification depends greatly on it. That is, an imbalanced training sample might dramatically decrease classification accuracy. Various criteria are defined to generate different types of training samples to evaluate the effectiveness of the training sample. There are several point features that can be used in RF classification under different circumstances. More features might increase the classification accuracy, however, in that case, the processing time is also increased. Point features such as RGB (red/green/blue), surface normals, curvature, omnivariance, planarity, linearity, surface variance, anisotropy, verticality, and ground/non-ground class are investigated in this study. Different training samples and sets of features are used in the RF to extract the road surface. The experiment is conducted on a local road without a raised curb located on a relatively steep hill. The accuracy assessment is conducted by comparing the model classification results with the manually extracted road surface point cloud. It is found that the accuracy increases up to around 4%-13%, and 95% overall accuracy was obtained when using convenient training samples and features.

服务人员 付贺龙

服务院士 唐守正

PDF文件 浏览全文