A deep learning solution for Posidonia oceanica seafloor habitat multiclass recognition

Authors Miguel Martín Abadal | Gabriel Oliver Codina | Yolanda González Cid
In IEEE Oceans, Marseilles, 2019.


Recent studies have shown evidence of a significant decline of the Posidonia oceanica meadows on a global scale. The monitoring and mapping of these meadows and its marine habitat are fundamental tools for measuring its status and growth opportunities. The presence of hard substrates benefits P. oceanica survival and development rates. We present an approach based on a deep neural network to automatically perform a high-precision semantic segmentation of Posidonia oceanica meadows and its seafloor habitat in sea-floor images, offering several improvements over the state of the art techniques. The presented network is able to accurately distinguish the most relevant classes: P. oceanica meadows, and rocky and sandy areas.

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