3D Colored Point Cloud Classification of a Deep-Sea Cold-Water Coral and Sponge Habitat Using Geometric Features and Machine Learning Algorithms

Salem Morsy^1,2*Ana Belén Yánez-Suárez^1,3Katleen Robert¹

• ¹School of Ocean Technology, Memorial University of Newfoundland Fisheries and Marine Institute, St. John's, Canada
• ²Public Works Department, Cairo University Faculty of Engineering, Cairo, Egypt
• ³Fisheries and Marine Institute, School of Fisheries, Memorial University of Newfoundland, St. John's, Canada

Classification of benthic habitats in the deep sea is instrumental in managing and monitoring marine ecosystems as it provides distinct units for which changes can be quantified over time. These applications require automatic classification approaches with reasonable accuracy to ensure efficiency and robustness. The use of 3D point clouds is currently emerging in deep-sea benthic classification as it allows for high-resolution representation of the 3D structure (i.e., geometry), texture, and composition of complex benthic habitats such as those created by structure-forming cold-water corals. Point clouds were derived from remotely operated vehicle video surveys of three vertical walls (depth range 1400-1900 m) along the Charlie-Gibbs Fracture Zone, North Atlantic. In addition to RGB values, this research incorporated nine geometric features derived from structure-from-motion 3D point clouds to classify coral and sponge colonies. Three unsupervised (k-means (KM), fuzzy c-means (FCM), and Gaussian mixture model (GMM)) and three supervised (decision tree (DT), random forest (RF), and linear discriminant analysis (LDA)) machine learning (ML) algorithms were compared and assessed for accuracy and reliability. The ML classifiers were used to build full-coverage seafloor predictions for three classes, namely seabed, sponges, and corals. The KM, GMM, and FCM achieved an average overall accuracy of 74.87%, 71.94 %, and 70.77%, respectively, while the RF, LDA, and DT achieved 84.50%, 84.01%, and 79.90%, respectively. Overall, the supervised ML classifiers outperformed the unsupervised ML classifiers. In particular, the RF classifier demonstrated the highest overall classification accuracy and F1-score for individual classes, with an average of 89.09%, 67.12%, and 41.60% for the seabed, sponges, and corals, respectively. In addition, the spatial coherence of the point clouds was considered and improved the results’ overall accuracy and F1-score by up to 9% and 12%, respectively. Results showed that incorporating geometric features, traditionally employed in terrestrial and shallow-water LiDAR surveys, in combination with RGB values is suitable for high-resolution deep-sea benthic 3D point clouds classification.