ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean.
PeerJ
; 7: e6842, 2019.
Article
en En
| MEDLINE
| ID: mdl-31106059
Recently, Caribbean coasts have experienced atypical massive arrivals of pelagic Sargassum with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic Sargassum detection along the coastline of Quintana Roo, Mexico. Using convolutional and recurrent neural networks architectures, a deep neural network (named ERISNet) was designed specifically to detect these macroalgae along the coastline through remote sensing support. A new dataset which includes pixel values with and without Sargassum was built to train and test ERISNet. Aqua-MODIS imagery was used to build the dataset. After the learning process, the designed algorithm achieves a 90% of probability in its classification skills. ERISNet provides a novel insight to detect accurately algal blooms arrivals.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
País/Región como asunto:
Mexico
Idioma:
En
Revista:
PeerJ
Año:
2019
Tipo del documento:
Article
Pais de publicación:
Estados Unidos