ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean
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 coastl...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
PeerJ Inc.
2019-05-01
|
Series: | PeerJ |
Subjects: | |
Online Access: | https://peerj.com/articles/6842.pdf |
id |
doaj-bb496957f0f144cb8cc8bb4c8060968a |
---|---|
record_format |
Article |
spelling |
doaj-bb496957f0f144cb8cc8bb4c8060968a2020-11-24T23:49:11ZengPeerJ Inc.PeerJ2167-83592019-05-017e684210.7717/peerj.6842ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican CaribbeanJavier Arellano-Verdejo0Hugo E. Lazcano-Hernandez1Nancy Cabanillas-Terán2Estacion para la Recepcion de Informacion Satelital ERIS-Chetumal, El Colegio de la Frontera Sur, Chetumal, Quintana Roo, MéxicoCatedras CONACYT-El Colegio de la Frontera Sur, Chetumal, Quintana Roo, MéxicoCatedras CONACYT-El Colegio de la Frontera Sur, Chetumal, Quintana Roo, MéxicoRecently, 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.https://peerj.com/articles/6842.pdfRemote SensingNeural NetworksAlgal bloomsSargassumMODISMexico |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Javier Arellano-Verdejo Hugo E. Lazcano-Hernandez Nancy Cabanillas-Terán |
spellingShingle |
Javier Arellano-Verdejo Hugo E. Lazcano-Hernandez Nancy Cabanillas-Terán ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean PeerJ Remote Sensing Neural Networks Algal blooms Sargassum MODIS Mexico |
author_facet |
Javier Arellano-Verdejo Hugo E. Lazcano-Hernandez Nancy Cabanillas-Terán |
author_sort |
Javier Arellano-Verdejo |
title |
ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean |
title_short |
ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean |
title_full |
ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean |
title_fullStr |
ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean |
title_full_unstemmed |
ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean |
title_sort |
erisnet: deep neural network for sargassum detection along the coastline of the mexican caribbean |
publisher |
PeerJ Inc. |
series |
PeerJ |
issn |
2167-8359 |
publishDate |
2019-05-01 |
description |
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. |
topic |
Remote Sensing Neural Networks Algal blooms Sargassum MODIS Mexico |
url |
https://peerj.com/articles/6842.pdf |
work_keys_str_mv |
AT javierarellanoverdejo erisnetdeepneuralnetworkforsargassumdetectionalongthecoastlineofthemexicancaribbean AT hugoelazcanohernandez erisnetdeepneuralnetworkforsargassumdetectionalongthecoastlineofthemexicancaribbean AT nancycabanillasteran erisnetdeepneuralnetworkforsargassumdetectionalongthecoastlineofthemexicancaribbean |
_version_ |
1725483560309096448 |