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

• Main Authors: Javier Arellano-Verdejo, Hugo E. Lazcano-Hernandez, Nancy Cabanillas-Terán
• Format: Article
• Language: English
• Published: PeerJ Inc. 2019-05-01
• Series: PeerJ
• Subjects: Remote Sensing; Neural Networks; Algal blooms; Sargassum; MODIS; Mexico
• Online Access: https://peerj.com/articles/6842.pdf

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.