  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.
