Ship detection and extraction using visual saliency and histogram of oriented gradient
A novel unsupervised ship detection and extraction method is proposed. A combination model based on visual saliency is constructed for searching the ship target regions and suppressing the false alarms. The salient target regions are extracted and marked through segmentation. Radon transform is applied to confirm the suspected ship targets with symmetry profiles. Then, a new descriptor, improved histogram of oriented gradient (HOG), is introduced to discriminate the real ships. The experimental results on real optical remote sensing images demonstrate that plenty of ships can be extracted and located successfully, and the number of ships can be accurately acquired. Furthermore, the proposed method is superior to the contrastive methods in terms of both accuracy rate and false alarm rate.
基金项目：This work has been supported by the National Natural Science Foundation of China (No.60902067), and the Key Programs for Science and Technology Development of Jilin Province of China (No.11ZDGG001).
LIU Jing-hong：Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
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XU Fang,LIU Jing-hong. Ship detection and extraction using visual saliency and histogram of oriented gradient[J]. 光电子快报（英文版）, 2016, 12(6): 473-477
XU Fang,LIU Jing-hong. Ship detection and extraction using visual saliency and histogram of oriented gradient[J]. Optoelectronics Letters, 2016, 12(6): 473-477