A novel vessel trajectory feature engineering for fishing vessel behavior identification

Abstract

Trajectory-based fishing vessel behavior recognition is currently a vibrant and dynamic area of investigation within transportation and maritime research. It can provide crucial technical support for combating illegal, unreported, and unregulated (IUU) fishing. Nevertheless, existing studies typically rely on extensive datasets of pre-labeled data to train machine learning models. The acquisition of these labeled datasets is frequently a time-consuming and complicated endeavor, which presents challenges in the practical application. Hence, this paper develops a semi-supervised approach for fishing vessel behavior identification using a small amount of manually labeled trajectory samples. Besides, given that the common vessel behavioral feature parameters provide limited insight into fishing activities, this paper innovatively proposes a set of descriptive trajectory feature parameters. Based on the morphological characteristics of fishing activities and domain knowledge, the proposed trajectory features excel in discerning the intricate pattern of fishing vessel trajectories. Combined with a set of Long Short-Term Memory (LSTM)-based encoding trajectory features, a composed feature engineering scheme that leverages both human intelligence and machine intelligence is proposed. The clustering experiment result confirms that our trajectory feature scheme can successfully recognize different fishing vessel behaviors while the conventional trajectory features can't. In the consequently classification tests, the 90% accuracy achieved by the composed feature scheme exhibits a 10% increase compared to solely using deep learning-based encoding features. This novel trajectory feature engineering method together with the semi-supervised machine learning structure, not reliant on extensive labeled datasets, can be tailored and applied to mobility pattern identification across various domains.