Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach

doi:10.1016/j.isprsjprs.2015.03.011

CORC > 北京大学 > 地球与空间科学学院

	Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach
	Du, Shihong ; Zhang, Fangli ; Zhang, Xiuyuan
刊名	isprs journal of photogrammetry and remote sensing
	2015
DOI	10.1016/j.isprsjprs.2015.03.011
英文摘要	While most existing studies have focused on extracting geometric information on buildings, only a few have concentrated on semantic information. The lack of semantic information cannot satisfy many demands on resolving environmental and social issues. This study presents an approach to semantically classify buildings into much finer categories than those of existing studies by learning random forest (RF) classifier from a large number of imbalanced samples with high-dimensional features. First, a two-level segmentation mechanism combining GIS and VHR image produces single image objects at a large scale and intra-object components at a small scale. Second, a semi-supervised method chooses a large number of unbiased samples by considering the spatial proximity and intra-cluster similarity of buildings. Third, two important improvements in RF classifier are made: a voting-distribution ranked rule for reducing the influences of imbalanced samples on classification accuracy and a feature importance measurement for evaluating each feature's contribution to the recognition of each category. Fourth, the semantic classification of urban buildings is practically conducted in Beijing city, and the results demonstrate that the proposed approach is effective and accurate. The seven categories used in the study are finer than those in existing work and more helpful to studying many environmental and social problems. ? 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).; SCI(E); EI; 0; ARTICLE; dshgis@hotmail.com; 107-119; 105
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/156252]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Du, Shihong,Zhang, Fangli,Zhang, Xiuyuan. Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach[J]. isprs journal of photogrammetry and remote sensing,2015.
APA	Du, Shihong,Zhang, Fangli,&Zhang, Xiuyuan.(2015).Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach.isprs journal of photogrammetry and remote sensing.
MLA	Du, Shihong,et al."Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach".isprs journal of photogrammetry and remote sensing (2015).