LSTM vs CNN in Real Ship Trajectory Classification
| Autor: | Jesús García, José M. Molina, Juan Pedro Llerena |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) ISBN: 9783030878689 SOCO |
| DOI: | 10.1007/978-3-030-87869-6_6 |
| Popis: | Ship type identification in a maritime context can be critical to the authorities to control the activities being carried out. Although Automatic Identification Systems (AIS) has been mandatory for certain vessels if a vessel does not have them voluntarily or not, it can lead to a whole set of problems, so the use of tracking alternatives such as radar is fully complementary. However, radars provide positions, but not what they are detecting. Having systems capable of adding categorical information to radar detections of vessels makes it possible to increase control of the activities being carried out, improve safety in maritime traffic, and optimize on-site inspection resources on the part of the authorities. This paper addresses the binary classification problem (fishing ships versus all other vessels) using unbalanced data from real vessel trajectories. It is performed from a Deep Learning (DL) approach comparing two of the main trends, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). In this paper it is proposed the weighted Cross-Entropy (WCE) methodology and compared with classical data balancing strategies. Both networks show high performance when applying WCE compared to the classical machine learning approaches and classical balancing techniques. This work is shown to be a novel approach to the international problem of identifying fishing ships without contexts. |
| Databáze: | OpenAIRE |
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