Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier
- 120 Downloads
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.
Unable to display preview. Download preview PDF.
- Y. Wang, L. Ma and Y. Tian, Acta Automatica Sinaca 37, 1029 (2011).(in Chinese)Google Scholar
- Y. Zhao, X. Wu, L. Wen and S. Xu, Opto-Electronic Engineering 35, 102 (2008).Google Scholar
- 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
- 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
- R. Achanta and S. Süsstrunk, Saliency Detection Using Maximum Symmetric Surround, IEEE International Conference on Image Processing, Hong Kong, 2653 (2010).Google Scholar
- 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
- R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua and S. Süsstrunk, Slic Superpixels, EPFL Technical Report, 149300 (2010).Google Scholar
- 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