An efficient coral survey method based on a large-scale 3-D structure model obtained by Speedy Sea Scanner and U-Net segmentation

Deep-sea infauna with calcified exoskeletons imaged in situ using a new 3D acoustic coring system (A-core-2000)

The deep ocean is Earth’s largest habitable space inhabited by diverse benthic organisms. Infauna play crucial roles in shaping sedimentary structures, relocating organic matter, porewater chemistry, and hence biogeochemical cycles. However, the visualization and quantification of infauna in situ inside deep-sea sediment has been challenging, due to their sparse distribution and that deep-sea cameras do not visualize animals living below the sediment surface. Here, we newly developed a 3D acoustic “coring” system and applied it to visualize and detect burrowing bivalves in deep-sea sediments. The in situ acoustic observation was conducted at a dense colony of vesicomyid clams in a hydrocarbon seep in Sagami Bay, Japan, focusing on a patch of juvenile clams with a completely infaunal life style. We clearly observed strong backscatters from the top and lower edges of animals in our 3D acoustic data. At least 17 reflectors were identified in the survey area (625 cm²), interpreted to correspond to living clams. The estimated depths of the lower edge of clams ranged between 41 and 98 mm. The acoustic system presented here is effective for detecting and monitoring infauna with calcified exoskeletons. This novel tool will help us better assess and understand the distribution of deep-sea infauna, particularly those groups with hard exoskeletons, as well as biogeochemical cycles.

Automatic Semantic Segmentation of Benthic Habitats Using Images from Towed Underwater Camera in a Complex Shallow Water Environment

Underwater image segmentation is useful for benthic habitat mapping and monitoring; however, manual annotation is time-consuming and tedious. We propose automated segmentation of benthic habitats using unsupervised semantic algorithms. Four such algorithms––Fast and Robust Fuzzy C-Means (FR), Superpixel-Based Fast Fuzzy C-Means (FF), Otsu clustering (OS), and K-means segmentation (KM)––were tested for accuracy for segmentation. Further, YCbCr and the Commission Internationale de l’Éclairage (CIE) LAB color spaces were evaluated to correct variations in image illumination and shadow effects. Benthic habitat field data from a geo-located high-resolution towed camera were used to evaluate proposed algorithms. The Shiraho study area, located off Ishigaki Island, Japan, was used, and six benthic habitats were classified. These categories were corals (Acropora and Porites), blue corals (Heliopora coerulea), brown algae, other algae, sediments, and seagrass (Thalassia hemprichii). Analysis showed that the K-means clustering algorithm yielded the highest overall accuracy. However, the differences between the KM and OS overall accuracies were statistically insignificant at the 5% level. Findings showed the importance of eliminating underwater illumination variations and outperformance of the red difference chrominance values (Cr) in the YCbCr color space for habitat segmentation. The proposed framework enhanced the automation of benthic habitat classification processes.