• Optoelectronics Letters
  • Vol. 13, Issue 2, 151 (2017)
Hui-li WANG1、2, Ming ZHU1、*, Chun-bo LIN1, and Dian-bing CHEN1、2
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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    DOI: 10.1007/s11801-017-7014-9 Cite this Article
    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 Copy Citation Text show less

    Abstract

    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.
    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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