Automatic classification of deep benthic habitats: Detection of microbial mats and siboglinid polychaete fields from optical images on the Håkon Mosby Mud Volcano

While many seafloor surveys provide a growing number of data, only few automatic techniques are developed to analyze images. This study is interested in the automatic detection and quantification of microbial mats and fields of siboglinid polychaete (tubeworm) colonizing the seafloor of the deep-sea Hakon Mosby Mud Volcano surveyed by ROV. Three algorithms are developed to segment the high resolution optical images of the seafloor which apply a watershed transformation coupled with a region growing technique, or a similarity measure operated with a region growing technique, or a texture analysis used with the Kullback-Leibler divergence. The results are compared through the score of a classification ratio estimated with a human-made classification.