• Home
  • UCSC journals portal
  • ANID repository
  • UCSC Thesis Repository
  • English
  • Español
  • Log In
    Have you forgotten your password?
  1. Home
  2. Productividad Científica
  3. Publicaciones Científicas
  4. A Spatial-Spectral classification method based on deep learning for controlling pelagic fish landings in Chile
 
Options
A Spatial-Spectral classification method based on deep learning for controlling pelagic fish landings in Chile
Dra. Restrepo-Medina, Silvia 
Facultad de Ingeniería 
Pezoa, Jorge
Ramírez, Diego
Godoy, Cristofher
Saavedra, María
Coelho-Caro, Pablo
Flores, Christopher
Pérez, Francisco
Torres, Sergio
Urbina, Mauricio
10.3390/s23218909
Sensors
2023
Fishing has provided mankind with a protein-rich source of food and labor, allowing for the development of an important industry, which has led to the overexploitation of most targeted fish species. The sustainable management of these natural resources requires effective control of fish landings and, therefore, an accurate calculation of fishing quotas. This work proposes a deep learning-based spatial-spectral method to classify five pelagic species of interest for the Chilean fishing industry, including the targeted Engraulis ringens, Merluccius gayi, and Strangomera bentincki and non-targeted Normanichthtys crockeri and Stromateus stellatus fish species. This proof-of-concept method is composed of two channels of a convolutional neural network (CNN) architecture that processes the Red–Green–Blue (RGB) images and the visible and near-infrared (VIS-NIR) reflectance spectra of each species. The classification results of the CNN model achieved over 94% in all performance metrics, outperforming other state-of-the-art techniques. These results support the potential use of the proposed method to automatically monitor fish landings and, therefore, ensure compliance with the established fishing quotas.
Thumbnail Image
Download
Name

A Spatial-Spectral Classification Method Based on Deep Learning for Controlling Pelagic Fish Landings in Chile.pdf

Size

11.79 MB

Format

Checksum
Deep learning
Fish
Hyperspectral imaging
Image processing
Machine learning
VIS-NIR
Historial de mejoras
Proyecto financiado por: