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
Source PublicationCOMPUTER VISION - ACCV 2006, PT I
2006
Volume3851Pages:236-245
SubtypeArticle
AbstractThis 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.
KeywordAdaboost Local Binary Pattern Machine Learning Road Extraction
WOS HeadingsScience & Technology ; Technology
Indexed ByISTP ; SCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS IDWOS:000235772300025
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9197
Collection09年以前成果
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
Recommended Citation
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.
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