Machine learning and deep learning strategies to identify Posidonia meadows in underwater images

This paper describes how to automatically identify Posidonea Oceanica (P.O.) from seabed images gathered by a bottom-looking camera. Different methods based on machine learning and deep learning algorithms are presented and compared. On the one hand, texture descriptors and co-occurrence matrices are used to characterize the images and classify the P.O. regions by means of Support Vector Machine and Artificial Neural Networks. On the other hand, Convolutional Neural Networks are used in the Deep Learning approach. The experimental results obtained demonstrate the effectiveness of the algorithms proposed to automatically identify P.O. meadows in underwater images.