J. Arellano-verdejo, H. E. Lazcano-hernandez, and N. Cabanillas-terán, ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean, PeerJ, vol.7, p.6842, 2019.

D. Bernard, E. Biabiany, N. Sekkat, R. Chery, and R. Cécé, Massive stranding of pelagic sargassum seaweeds onthe french Antilles coasts : Analysis of observed situations with Operational Mercator global oceananalysis and forecast system, p.24

C. Francais-de-mécanique, , 2019.

M. Ester, H. P. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, In: Kdd, vol.96, pp.226-231, 1996.

J. Gower, S. King, G. Borstad, and L. Brown, Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer, International Journal of Remote Sensing -INT J REMOTE SENS, vol.26, 2005.

C. Hu, A novel ocean color index to detect floating algae in the global oceans, Remote Sensing of Environment, vol.113, issue.10, pp.2118-2129, 2009.

C. Hu and M. X. He, Origin and Offshore Extent of Floating Algae in Olympic Sailing Area, Transactions American Geophysical Union, vol.89, issue.33, pp.302-303, 2008.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.

A. Mehnert and P. Jackway, An improved seeded region growing algorithm, Pattern Recognition Letters, vol.18, issue.10, pp.1065-1071, 1997.

M. Wang and C. Hu, Mapping and quantifying Sargassum distribution and coverage in the Central West Atlantic using MODIS observations, Remote Sensing of Environment, vol.183, pp.350-367, 2016.