A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery
AbstractMaritime target detection from optical remote sensing images plays an important role in related military and civil applications and its weakness lies in its compromised performance under complex uncertain conditions. In this paper, a novel hierarchical ship detection method is proposed to overcome this issue. In the ship detection stage, based on Entropy information, we construct a combined saliency model with self-adaptive weights to prescreen ship candidates from across the entire maritime domain. To characterize ship targets and further reduce the false alarms, we introduce a novel and practical descriptor based on gradient features, and this descriptor is robust against clutter introduced by heavy clouds, islands, ship wakes as well as variation in target size. Furthermore, the proposed method is effective for not only color images but also gray images. The experimental results obtained using real optical remote sensing images have demonstrated that the locations and the number of ships can be determined accurately and that the false alarm rate is greatly decreased. A comprehensive comparison is performed between the proposed method and the state-of-the-art methods, which shows that the proposed method achieves higher accuracy and outperforms all the competing methods. Furthermore, the proposed method is robust under various backgrounds of maritime images and has great potential for providing more accurate target detection in engineering applications. View Full-Text
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Xu, F.; Liu, J.; Sun, M.; Zeng, D.; Wang, X. A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery. Remote Sens. 2017, 9, 280.
Xu F, Liu J, Sun M, Zeng D, Wang X. A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery. Remote Sensing. 2017; 9(3):280.Chicago/Turabian Style
Xu, Fang; Liu, Jinghong; Sun, Mingchao; Zeng, Dongdong; Wang, Xuan. 2017. "A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery." Remote Sens. 9, no. 3: 280.
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