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Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier
Wang, H.-l.; M. Zhu; C.-b. Lin and D.-b. Chen
2017
Source PublicationOptoelectronics Letters
Volume13Issue:2
AbstractIn this paper, firstly, target candidate regions are extracted by combining maximum symmetric surround saliency detection algorithm with a cellular automata dynamic evolution model. Secondly, an eigenvector independent of the ship target size is constructed by combining the shape feature with ship histogram of oriented gradient (S-HOG) feature, and the target can be recognized by AdaBoost classifier. As demonstrated in our experiments, the proposed method with the detection accuracy of over 96% outperforms the state-of-the-art method. 2017, Tianjin University of Technology and Springer-Verlag Berlin Heidelberg.
Indexed Bysci ; ei
Language英语
Document Type期刊论文
Identifierhttp://ir.ciomp.ac.cn/handle/181722/59252
Collection中科院长春光机所知识产出
Recommended Citation
GB/T 7714
Wang, H.-l.,M. Zhu,C.-b. Lin and D.-b. Chen. Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier[J]. Optoelectronics Letters,2017,13(2).
APA Wang, H.-l.,M. Zhu,&C.-b. Lin and D.-b. Chen.(2017).Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier.Optoelectronics Letters,13(2).
MLA Wang, H.-l.,et al."Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier".Optoelectronics Letters 13.2(2017).
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