Extraction of main urban roads from high resolution satellite images by machine learning

CASIA OpenIR > 09年以前成果

	Extraction of main urban roads from high resolution satellite images by machine learning
	Wang, YQ; Tian, Y; Tai, XQ; Shu, LX; Narayanan, PJ; Nayar, SK; Shum, HY
发表期刊	COMPUTER VISION - ACCV 2006, PT I
	2006
卷号	3851 页码:236-245
文章类型	Article
摘要	This paper focuses on automatic road extraction in urban areas from high resolution satellite images. We propose a new approach based on machine learning. First, many features reflecting road characteristics are extracted, which consist of the ratio of bright regions, the direction consistency of edges and local binary patterns. Then these features are input into a learning container, and AdaBoost is adopted to train classifiers and select most effective features. Finally, roads are detected with a sliding window by using the learning results and validated by combining the road connectivity. Experimental results on real Quick-bird images demonstrate the effectiveness and robustness of the proposed method.
关键词	Adaboost Local Binary Pattern Machine Learning Road Extraction
WOS标题词	Science & Technology ; Technology
收录类别	ISTP ; SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:000235772300025
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/9197
专题	09年以前成果
作者单位	Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Wang, YQ,Tian, Y,Tai, XQ,et al. Extraction of main urban roads from high resolution satellite images by machine learning[J]. COMPUTER VISION - ACCV 2006, PT I,2006,3851:236-245.
APA	Wang, YQ.,Tian, Y.,Tai, XQ.,Shu, LX.,Narayanan, PJ.,...&Shum, HY.(2006).Extraction of main urban roads from high resolution satellite images by machine learning.COMPUTER VISION - ACCV 2006, PT I,3851,236-245.
MLA	Wang, YQ,et al."Extraction of main urban roads from high resolution satellite images by machine learning".COMPUTER VISION - ACCV 2006, PT I 3851(2006):236-245.