An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation
An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation
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DOI：
10.1016/j.oceaneng.2021.108803
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发表时间：
2021-03-04
影响因子：
5
通讯作者：
Lu, Feng
中科院分区：
文献类型：
--
作者：
Liang, Maohan;Liu, Ryan Wen;Lu, Feng
为了获得可靠的大规模船舶轨迹挖掘结果，最重要的挑战之一是如何有效地计算不同船舶轨迹之间的相似性。船舶航迹相似度的计算是近年来海事数据挖掘领域的研究热点。然而，传统的基于形状和翘曲的方法往往遭受几个缺点，如高计算成本和敏感性不必要的文物和不均匀的采样率等，为了消除这些缺点，我们提出了一种无监督的学习方法，自动提取低维特征，通过卷积自动编码器（CAE）。特别是，我们首先通过将原始血管轨迹重新映射到二维矩阵中来生成信息丰富的轨迹图像，同时保持时空属性。基于收集的大量血管轨迹，CAE可以以无监督的方式学习信息轨迹图像的低维表示。轨迹相似性最终相当于有效计算学习到的低维特征之间的相似性，这些特征与原始血管轨迹密切相关。在真实数据集上的综合实验表明，该方法在效率和有效性方面大大优于传统的轨迹相似性计算方法。基于CAE的轨迹相似度计算结果也可以保证高质量的轨迹聚类性能。
To achieve reliable mining results for massive vessel trajectories, one of the most important challenges is how to efficiently compute the similarities between different vessel trajectories. The computation of vessel trajectory similarity has recently attracted increasing attention in the maritime data mining research community. However, traditional shape- and warping-based methods often suffer from several drawbacks such as high computational cost and sensitivity to unwanted artifacts and non-uniform sampling rates, etc. To eliminate these drawbacks, we propose an unsupervised learning method which automatically extracts low-dimensional features through a convolutional auto-encoder (CAE). In particular, we first generate the informative trajectory images by remapping the raw vessel trajectories into two-dimensional matrices while maintaining the spatio-temporal properties. Based on the massive vessel trajectories collected, the CAE can learn the low-dimensional representations of informative trajectory images in an unsupervised manner. The trajectory similarity is finally equivalent to efficiently computing the similarities between the learned low-dimensional features, which strongly correlate with the raw vessel trajectories. Comprehensive experiments on realistic data sets have demonstrated that the proposed method largely outperforms traditional trajectory similarity computation methods in terms of efficiency and effectiveness. The high-quality trajectory clustering performance could also be guaranteed according to the CAE-based trajectory similarity computation results.