Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2020 Apr 29;20(9):2536. doi: 10.3390/s20092536.
Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds.
高效的船舶检测对于商业和军事策略至关重要。然而，由于海面条件的不确定性，如大气、光照、云层和岛屿，传统的船舶检测方法存在检测效率低和可靠性差的问题。因此，本研究提出了一种基于改进的超复数傅里叶变换（MHFT）显著模型的新型船舶目标自动检测系统，用于空间分辨率的遥感图像。该方法首先利用视觉显著理论有效地抑制海面干扰。然后我们使用 OTSU 方法提取感兴趣区域。在获得候选船舶目标区域后，我们使用基于 ResNet 框架的船舶目标识别方法获得候选目标。与其他方法相比，该方法在船舶目标识别方面具有更高的准确性和更好的性能。实验结果表明，该方法不仅可以准确有效地识别船舶目标，而且还适用于具有复杂背景的空间分辨率的遥感图像。
