Mou Fangli, Fan Zide, Ge Yunping, Wang Lei, Li Xinming
Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel). 2024 Oct 18;24(20):6708. doi: 10.3390/s24206708.
In this study, we propose a practical and efficient scheme for ship detection in remote sensing imagery. Our method is developed using both ship body detection and ship wake detection and combines deep learning and feature-based image processing. A deep convolutional neural network is used to achieve ship body detection, and a feature-based processing method is proposed to detect ship wakes. For better analysis, we model the sea region and evaluate the quality of the image. Generally, the wake detection result is used to assist ship detection and obtain the sailing direction. Conventional methods cannot detect ships that are covered by clouds or outside the image boundary. The method proposed in this paper uses the wake to detect such ships, with a certain level of confidence and low false alarm probability in detection. Practical aspects such as the method's applicability and time efficiency are considered in our method for real applications. We demonstrate the effectiveness of our method in a real remote sensing dataset. The results show that over 93.5% of ships and over 70% of targets with no visible ship body can be successfully detected. This illustrates that the proposed detection framework can fill the gap regarding the detection of sailing ships in a remote sensing image.
在本研究中，我们提出了一种实用且高效的遥感影像船舶检测方案。我们的方法是通过船体检测和船尾检测相结合，并融合深度学习和基于特征的图像处理技术来开发的。使用深度卷积神经网络实现船体检测，并提出一种基于特征的处理方法来检测船尾。为了进行更好的分析，我们对海域进行建模并评估图像质量。一般来说，船尾检测结果用于辅助船舶检测并获取航行方向。传统方法无法检测被云层覆盖或位于图像边界之外的船舶。本文提出的方法利用船尾来检测此类船舶，在检测中具有一定的置信度且误报概率较低。我们的方法在实际应用中考虑了该方法的适用性和时间效率等实际方面。我们在真实的遥感数据集中证明了我们方法的有效性。结果表明，超过93.5%的船舶以及超过70%没有可见船体的目标能够被成功检测。这表明所提出的检测框架可以填补遥感影像中航行船舶检测方面的空白。
