基于AIS轨迹数据，用轻量特征区分渔船与非渔船
Architecture for Trajectory-Based Fishing Ship Classification with AIS Data
- 从AIS轨迹中提取时空特征，构建渔船分类模型
- 在数据噪声和类别不平衡下仍达良好分类效果
- 方法通用性强，适合无上下文信息的船舶行为分析
本文提出一种针对真实世界运动数据的预处理流程，用于检测渔船。该方法为二分类任务，将船舶轨迹分为渔船或非渔船两类。所用数据存在典型的真实数据问题，如噪声和不一致性，且两类样本严重不平衡，通过重采样算法解决。特征从自动识别系统（AIS）报告序列中提取，表征船舶的时空轨迹行为。这些特征虽不含上下文信息，但可推广至其他场景。实验表明，所提数据预处理流程对分类任务有效，且仅需少量信息即可获得正面结果。
原文摘要 · Abstract (English)
This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information.
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