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

PeerJ

article

Javier Arellano-Verdejo¹  Hugo E. Lazcano-Hernandez²  Nancy Cabanillas-Terán² 

[1] Estacion para la Recepcion de Informacion Satelital ERIS-Chetumal;Catedras CONACYT-El Colegio de la Frontera Sur

关键词: Remote Sensing;    Neural Networks;    Algal blooms;    Sargassum;    MODIS;    Mexico;    Deep learning;   

DOI  :  10.7717/peerj.6842

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY