Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2019 Feb 7;19(3):684. doi: 10.3390/s19030684.
While ship detection using high-resolution optical satellite images plays an important role in various civilian fields-including maritime traffic survey and maritime rescue-it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea⁻land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters. In this study, Faster Region-based Convolutional Neural Network (Faster R-CNN) is applied to detect ships without the need for land masking. We propose an effective training strategy for the Faster R-CNN by incorporating a large number of images containing only terrestrial regions as negative samples without any manual marking, which is different from the selection of negative samples by targeted way in other detection methods. The experiments using Gaofen-1 satellite (GF-1), Gaofen-2 satellite (GF-2), and Jilin-1 satellite (JL-1) images as testing datasets under different ship detection conditions were carried out to evaluate the effectiveness of the proposed strategy in the avoidance of false alarms on land. The results show that the method incorporating negative sample training can largely reduce false alarms in terrestrial areas, and is superior in detection performance, algorithm complexity, and time consumption. Compared with the method based on sea⁻land segmentation, the proposed method achieves the absolute increment of 70% of the F1-measure, when the image contains large land area such as the GF-1 image, and achieves the absolute increment of 42.5% for images with complex harbors and many coastal ships, such as the JL-1 images.
虽然利用高分辨率光学卫星图像进行船舶检测在包括海上交通调查和海上救援在内的各种民用领域中起着重要作用，但由于复杂背景的影响，特别是当船舶靠近陆地时，这是一项艰巨的任务。在当前的文献中，船舶检测通常需要进行陆地掩蔽，以避免在陆地上产生许多误报。然而，海-陆分割不仅存在分割错误的风险，而且需要专业知识来调整参数。在本研究中，我们应用了基于快速区域卷积神经网络（Faster R-CNN）的方法，无需陆地掩蔽即可检测船舶。我们提出了一种有效的 Faster R-CNN 训练策略，该策略通过大量仅包含陆地区域的图像作为负样本，而无需进行任何手动标记，这与其他检测方法中通过有针对性的方式选择负样本的方式不同。我们使用高分一号卫星（GF-1）、高分二号卫星（GF-2）和吉林一号卫星（JL-1）的图像作为测试数据集，在不同的船舶检测条件下进行了实验，以评估该策略在避免陆地误报方面的有效性。结果表明，结合负样本训练的方法可以大大减少陆地误报，并且在检测性能、算法复杂度和时间消耗方面具有优势。与基于海-陆分割的方法相比，当图像包含大面积陆地（如 GF-1 图像）时，所提出的方法可将 F1 度量的绝对增量提高 70%，而对于具有复杂港口和大量沿海船舶的图像（如 JL-1 图像），则可将 F1 度量的绝对增量提高 42.5%。
