Department of Computer Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada.
Sensors (Basel). 2022 Jul 26;22(15):5588. doi: 10.3390/s22155588.
The classification of ships based on their trajectory descriptors is a common practice that is helpful in various contexts, such as maritime security and traffic management. For the most part, the descriptors are either geometric, which capture the shape of a ship's trajectory, or kinematic, which capture the motion properties of a ship's movement. Understanding the implications of the type of descriptor that is used in classification is important for feature engineering and model interpretation. However, this matter has not yet been deeply studied. This article contributes to feature engineering within this field by introducing proper similarity measures between the descriptors and defining sound benchmark classifiers, based on which we compared the predictive performance of geometric and kinematic descriptors. The performance profiles of geometric and kinematic descriptors, along with several standard tools in interpretable machine learning, helped us to provide an account of how different ships differ in movement. Our results indicated that the predictive performance of geometric and kinematic descriptors varied greatly, depending on the classification problem at hand. We also showed that the movement of certain ship classes solely differed geometrically while some other classes differed kinematically and that this difference could be formulated in simple terms. On the other hand, the movement characteristics of some other ship classes could not be delineated along these lines and were more complicated to express. Finally, this study verified the conjecture that the geometric-kinematic taxonomy could be further developed as a tool for more accessible feature selection.
基于轨迹描述符对船舶进行分类是一种常见的做法，在海上安全和交通管理等各种情境中都很有用。在大多数情况下，描述符要么是几何的，捕捉船舶轨迹的形状，要么是运动学的，捕捉船舶运动的运动特性。了解在分类中使用的描述符类型的含义对于特征工程和模型解释很重要。然而，这个问题尚未得到深入研究。本文通过引入描述符之间的适当相似性度量，并基于此定义合理的基准分类器，为该领域的特征工程做出了贡献，我们比较了几何描述符和运动学描述符的预测性能。几何描述符和运动学描述符的性能特征，以及可解释机器学习中的几个标准工具，帮助我们解释了不同船舶在运动方面的差异。我们的结果表明，几何描述符和运动学描述符的预测性能因手头的分类问题而有很大差异。我们还表明，某些船舶类别的运动仅在几何上有所不同，而其他一些类别的运动则在运动学上有所不同，并且这种差异可以用简单的术语来表述。另一方面，某些其他船舶类别的运动特征无法按照这些标准进行划分，表达起来更为复杂。最后，这项研究验证了这样一个假设，即几何-运动分类法可以进一步发展成为一种更易于使用的特征选择工具。
