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Fishing Gear Pattern Recognition by Including Supervised Autoencoder Dimensional Reduction
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DOI：10.1109/LGRS.2021.3084183.png)
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Abstract
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Fishing is a crucial worldwide activity as it provides a source of food and economic income. A challenge in ecology and conservation is decreasing overfishing and illegal, unreported, and unregulated fishing (IUUF). One strategy to decrease those issues is to track vessels for detecting fishing behaviors through monitory systems. In this letter, we present an approach to classify fishing behaviors, specifically, for four fishing gear types (trawl, purse seine, fixed gear, and longline) using automatic identification systems (AISs) data from the Global Fishing Watch platform. Thus, our main contribution is how we propose data processing by including a supervised autoencoder dimensional reduction (SA-DR) processing data step. This step allows removing redundant features and noise, avoiding overfitting, decreasing data complexity, and preserving the differences between classes. Specifically, we propose to use IVIS and centroid encoder (CE) methods. The experimental results show how our approach applying SA-DR over the vessel trajectory feature representation reduces the variation results among different classifiers and achieves a high classification accuracy of up to 95%. This result could help prevent IUUF, overfishing, and improve fishery management strategies.
Keywords:
Gears
Trajectory
Artificial intelligence
Marine vehicles
Monitoring
Support vector machines
Proposals
Dimension reduction
fishing gear
supervised autoencoder
vessel behavior
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