Integration of ZiYuan-3 multispectral and stereo imagery for mapping urban vegetation using the hierarchy-based classifier

被引:20
作者
Zhao, Shuai
Jiang, Xiandie
Li, Guiying
Chen, Yaoliang
Lu, Dengsheng [1 ]
机构
[1] Fujian Normal Univ, Inst Geog, Fuzhou 350007, Peoples R China
基金
国家重点研发计划;
关键词
Urban vegetation; ZiYuan-3; Hierarchy-based classifier; LAND-COVER CLASSIFICATION; TREE SPECIES CLASSIFICATION; OBJECT-BASED CLASSIFICATION; SPATIAL-RESOLUTION IKONOS; WORLDVIEW-2; IMAGERY; RANDOM FOREST; LANDSCAPE; HEIGHT; FUSION; EXTRACTION;
D O I
10.1016/j.jag.2021.102594
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Urban vegetation has important impacts on urban heat island, human living environments and even quality of life. The areal increase of urban vegetation has great contribution in achieving Sustainable Development Goals (SDGs) of United Nations. It is needed to accurately extract different urban vegetation types using high spatial resolution images, but the limitation of remotely sensed data and complexity of urban landscapes make it challenging. This research aims to explore the integration of multispectral and stereo imagery with high spatial resolution for vegetation classification in the urban landscape in East China. A hierarchy-based classifier based on optimization of selected variables in each tree node is developed to conduct urban vegetation classification through incorporation of canopy height features into spectral and textural data. The results show that use of canopy height features improved overall classification accuracy of 4.6% comparing with the dataset without use of canopy height features. The proposed hierarchy-based classifier can further improve the vegetation classification accuracy by 3% comparing with random forest. This research indicates that selection of proper variables from different source data, especially canopy height features, plays important roles in improving urban vegetation classification. This research provides a new insight for accurate urban vegetation classification using a hierarchy-based classification approach based on integration of spectral, spatial and canopy features.
引用
收藏
页数:12
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