%0 Journal Article
%T ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean
%A Hugo E. Lazcano-Hernandez
%A Javier Arellano-Verdejo
%A Nancy Cabanillas-Ter¨¢n
%J Archive of "PeerJ".
%D 2019
%R 10.7717/peerj.6842
%X 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
%K Remote Sensing
%K Neural Networks
%K Algal blooms
%K Sargassum
%K MODIS
%K Mexico
%K Deep learning
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6500371/