DOI: 10.1016/j.jag.2017.04.003
Scopus记录号: 2-s2.0-85030846251
论文题名: Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification
作者: Dong W ; , Lan J ; , Liang S ; , Yao W ; , Zhan Z
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 60 起始页码: 99
结束页码: 110
语种: 英语
英文关键词: Geometric feature
; Land cover classification
; LiDAR data
; Neighborhood
Scopus关键词: geometry
; land cover
; lidar
; neighborhood
; size
; urban area
英文摘要: LiDAR has been an effective technology for acquiring urban land cover data in recent decades. Previous studies indicate that geometric features have a strong impact on land cover classification. Here, we analyzed an urban LiDAR dataset to explore the optimal feature subset from 25 geometric features incorporating 25 scales under 6 definitions for urban land cover classification. We performed a feature selection strategy to remove irrelevant or redundant features based on the correlation coefficient between features and classification accuracy of each features. The neighborhood scales were divided into small (0.5–1.5 m), medium (1.5–6 m) and large (>6 m) scale. Combining features with lower correlation coefficient and better classification performance would improve classification accuracy. The feature depicting homogeneity or heterogeneity of points would be calculated at a small scale, and the features to smooth points at a medium scale and the features of height different at large scale. As to the neighborhood definition, cuboid and cylinder were recommended. This study can guide the selection of optimal geometric features with adaptive neighborhood scale for urban land cover classification. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79999
Appears in Collections: 气候变化事实与影响
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作者单位: State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities & Faculty of Geography, Beijing Normal University, China; Department of Geographical Sciences, University of Maryland, College ParkCollege Park, MD, United States; Photogrammetry and Remote sensing, Technische Universitaet Muenchen, Munich, Germany
Recommended Citation:
Dong W,, Lan J,, Liang S,et al. Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,60