ERISNet: deep neural network for <i>Sargassum</i> detection along the coastline of the Mexican Caribbean

Abstract

Recently, Caribbean coasts have experienced atypical massive arrivals of pelagic <i>Sargassum</i> with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic <i>Sargassum</i> 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 <i>Sargassum</i> 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.