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.
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This work has been supported by the National Natural Science Foundation of China (No.61401425).
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Wang, Hl., Zhu, M., Lin, Cb. et al. Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier. Optoelectron. Lett. 13, 151–155 (2017). https://doi.org/10.1007/s11801-017-7014-9
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DOI: https://doi.org/10.1007/s11801-017-7014-9