Ship-type identification in a maritime context can be critical to the authorities to control the activities being carried out. Although Automatic Identification Systems has been mandatory for certain vessels, if a vessel does not have them voluntarily or not, it can lead to a whole set of problems, which is why the use of tracking alternatives such as radar is fully complementary for a vessel monitoring systems. However, radars provide positions, but not what they are detecting. Having systems capable of adding categorical information to radar detections of vessels makes it possible to increase control of the activities being carried out, improve safety in maritime traffic, and optimize on-site inspection resources on the part of the authorities. This paper addresses the binary classification problem (fishing ships versus all other vessels) using unbalanced data from real vessel trajectories. It is performed from a deep learning approach comparing two of the main trends, Convolutional Neural Networks and Long Short-Term Memory. In this paper, it is proposed the weighted cross-entropy methodology and compared with classical data balancing strategies. Both networks show high performance when applying weighted cross-entropy compared with the classical machine learning approaches and classical balancing techniques. This work is shown to be a novel approach to the international problem of identifying fishing ships without context.

中文翻译：

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LSTM 与 CNN 在实船轨迹分类中的比较

海事环境中的船舶类型识别对于当局控制正在开展的活动至关重要。尽管自动识别系统对于某些船舶来说是强制性的，但如果船舶自愿或不具备这些系统，则可能会导致一系列问题，这就是为什么使用雷达等跟踪替代方案对于船舶监控来说是完全补充的系统。然而，雷达提供位置，但不提供其检测到的内容。拥有能够向船舶雷达探测添加分类信息的系统，可以加强对正在进行的活动的控制，提高海上交通安全，并优化当局的现场检查资源。本文使用来自真实船只轨迹的不平衡数据解决二元分类问题（渔船与所有其他船只）。它是通过深度学习方法进行的，比较了卷积神经网络和长短期记忆这两个主要趋势。本文提出了加权交叉熵方法，并与经典的数据平衡策略进行了比较。与经典机器学习方法和经典平衡技术相比，这两个网络在应用加权交叉熵时都表现出高性能。这项工作被证明是解决在没有背景的情况下识别渔船的国际问题的一种新颖方法。