光电子快报(英文版), 2017, 13 (2): 151, Published Online: Sep. 13, 2018   

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

Author Affiliations
1 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2 University of Chinese Academy of Sciences, Beijing 100039, China
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]. 光电子快报(英文版), 2017, 13(2): 151.

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