Optoelectronics Letters

, Volume 13, Issue 2, pp 151–155 | Cite as

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

  • Hui-li Wang (王慧利)
  • Ming Zhu (朱明)
  • Chun-bo Lin (蔺春波)
  • Dian-bing Chen (陈典兵)


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.

Document code


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Y. Wang, L. Ma and Y. Tian, Acta Automatica Sinaca 37, 1029 (2011).(in Chinese)Google Scholar
  2. [2]
    Y. Wang and H. Liu, IEEE Transactions on Geoscience and Remote Sensing 50, 4173 (2012).ADSCrossRefGoogle Scholar
  3. [3]
    Y. Zhao, X. Wu, L. Wen and S. Xu, Opto-Electronic Engineering 35, 102 (2008).Google Scholar
  4. [4]
    C. Zhu, H. Zhou, R. Wang and J. Guo, IEEE Transactions on Geoscience and Remote Sensing 48, 3446 (2010).ADSCrossRefGoogle Scholar
  5. [5]
    Z. Li, D. Yang and Z. Chen, Multi-Layer Sparse Coding Based Ship Detection for Remote Sensing Images, IEEE International Conference on Information Reuse and Integration, San Francisco, 122 (2015).CrossRefGoogle Scholar
  6. [6]
    G. Yang, B. Li, S. Ji, F. Gao and Q. Xu, IEEE Geoscience and Remote Sensing Letters 11, 641 (2014).ADSCrossRefGoogle Scholar
  7. [7]
    Z. Song, H. Sui and Y. Wang, Automatic Ship Detection for Optical Satellite Images Based on Visual Attention Model and LBP, IEEE Workshop on Electronics, Computer and Applications, Ottawa, 722 (2014).Google Scholar
  8. [8]
    F. Yang, Q. Xu, F. Gao and L. Hu, Ship Detection from Optical Satellite Images Based on Visual Search Mechanism, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, 3679 (2015).Google Scholar
  9. [9]
    S. Qi, J. Ma, J. Lin, Y. Li and J. Tian, IEEE Geoscience and Remote Sensing Letters 12, 1451 (2015).ADSCrossRefGoogle Scholar
  10. [10]
    R. Achanta and S. Süsstrunk, Saliency Detection Using Maximum Symmetric Surround, IEEE International Conference on Image Processing, Hong Kong, 2653 (2010).Google Scholar
  11. [11]
    Y. Qin, H. Lu, Y. Xu and H. Wang, Saliency detection via Cellular Automata, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 110 (2015).Google Scholar
  12. [12]
    R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua and S. Süsstrunk, Slic Superpixels, EPFL Technical Report, 149300 (2010).Google Scholar
  13. [13]
    R. E. Schapire and Y. Singer, Machine Learning 37, 297 (1999).CrossRefGoogle Scholar
  14. [14]
    J. Sochman and J. Malas, AdaBoost with Totally Corrective Updates for Fast Face Detection, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 445 (2004).Google Scholar
  15. [15]
    P. Wang, C. Shen, N. Barnes and H. Zheng, IEEE Transactions on Neural Networks and Learning Systems 23, 33 (2012).CrossRefGoogle Scholar

Copyright information

© Tianjin University of Technology and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Hui-li Wang (王慧利)
    • 1
    • 2
  • Ming Zhu (朱明)
    • 1
  • Chun-bo Lin (蔺春波)
    • 1
  • Dian-bing Chen (陈典兵)
    • 1
    • 2
  1. 1.Changchun Institute of Optics, Fine Mechanics and PhysicsChinese Academy of SciencesChangchunChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations