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 segmentatio |
