Fishing Gear Pattern Recognition by Including Supervised Autoencoder Dimensional Reduction

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 beha...

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Veröffentlicht in:IEEE geoscience and remote sensing letters Jg. 19; S. 1 - 5
Hauptverfasser: Carlos, Hugo, Aranda, Ramon, Velasco, Mariana Rivera-De, Rodriguez-Gonzalez, Ansel Y., Mendez-Lopez, Maria Elena
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Artificial intelligence
Centroids
Coders
Conservation
Data analysis
Data processing
Dimension reduction
Ecology
Economics
Fisheries
Fisheries management
Fishery management
Fishing
Fishing equipment
Fishing gear
Food sources
Gears
Illegal fishing
Longline fishing
Marine vehicles
Monitoring
Overfishing
Pattern recognition
Proposals
Purse seines
supervised autoencoder
Support vector machines
Trajectory
vessel behavior
Vessels
ISSN:1545-598X, 1558-0571
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Zusammenfassung: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.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3084183