Optoelectronics Letters, Volume. 13, Issue 2, 151(2017)

Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier

Hui-li WANG1...2, Ming ZHU1,*, Chun-bo LIN1, and Dian-bing CHEN12 |Show fewer author(s)
Author Affiliations
  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
  • 2University of Chinese Academy of Sciences, Beijing 100039, China
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    In 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.1 efficiency switch and modulation.

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    WANG Hui-li, ZHU Ming, LIN Chun-bo, CHEN Dian-bing. Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier[J]. Optoelectronics Letters, 2017, 13(2): 151

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    Paper Information

    Received: Jan. 17, 2017

    Accepted: Feb. 10, 2017

    Published Online: Sep. 13, 2018

    The Author Email: Ming ZHU (zhu_mingca@163.com)

    DOI:10.1007/s11801-017-7014-9

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