An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation

TL;DR

This paper introduces an unsupervised convolutional auto-encoder approach to efficiently compute vessel trajectory similarities by transforming raw data into informative images and learning low-dimensional features, outperforming traditional methods.

Contribution

The novel use of a convolutional auto-encoder to automatically extract low-dimensional features from trajectory images for vessel similarity computation.

Findings

Outperforms traditional methods in efficiency and effectiveness

Enables high-quality vessel trajectory clustering

Reduces sensitivity to artifacts and sampling issues

Abstract

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.