Yang Haoyu, Wang Mao, Chen Zhihao, Xiao Kaiming, Li Xuan, Huang Hongbin
Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2023 Jul 27;23(15):6723. doi: 10.3390/s23156723.
Ship trajectory classification is of great significance for shipping analysis and marine security governance. However, in order to cover up their illegal fishing or espionage activities, some illicit ships will forge the ship type information in the Automatic Identification System (AIS), and this label noise will significantly impact the algorithm's classification accuracy. Sample selection is a common and effective approach in the field of learning from noisy labels. However, most of the existing methods based on sample selection need to determine the noise rate of the data through prior means. To address these issues, we propose a noise rate adaptive learning mechanism that operates without prior conditions. This mechanism is integrated with the robust training paradigm JoCoR (joint training with co-regularization), giving rise to a noise rate adaptive learning robust training paradigm called A-JoCoR. Experimental results on real-world trajectories provided by the Danish Maritime Authority verified the effectiveness of A-JoCoR. It not only realizes the adaptive learning of the data noise rate during the training process, but also significantly improves the classification performance compared with the original method.
船舶轨迹分类对于航运分析和海上安全治理具有重要意义。然而，为了掩盖其非法捕鱼或间谍活动，一些非法船舶会在自动识别系统（AIS）中伪造船舶类型信息，这种标签噪声会显著影响算法的分类准确性。样本选择是从噪声标签学习领域中一种常见且有效的方法。然而，大多数现有的基于样本选择的方法需要通过先验手段确定数据的噪声率。为了解决这些问题，我们提出了一种无需先验条件的噪声率自适应学习机制。该机制与鲁棒训练范式JoCoR（联合协同正则化训练）相结合，产生了一种称为A-JoCoR的噪声率自适应学习鲁棒训练范式。丹麦海事局提供的真实轨迹实验结果验证了A-JoCoR的有效性。它不仅在训练过程中实现了数据噪声率的自适应学习，而且与原始方法相比，显著提高了分类性能。
