ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean.

ABSTRACT: 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 it

SUBMITTER: Arellano-Verdejo J 

PROVIDER: S-EPMC6500371 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature