期刊论文详细信息
Remote Sensing
Automatic Semantic Segmentation of Benthic Habitats Using Images from Towed Underwater Camera in a Complex Shallow Water Environment
Hassan Mohamed1  Takashi Nakamura2  Kazuo Nadaoka2 
[1] Department of Geomatics Engineering, Shoubra Faculty of Engineering, Benha University, Cairo 11672, Egypt;School of Environment and Society, Tokyo Institute of Technology, Tokyo 152-8552, Japan;
关键词: unsupervised semantic segmentation;    underwater images;    benthic habitats mapping;    shallow-water ecosystems;   
DOI  :  10.3390/rs14081818
来源: DOAJ
【 摘 要 】

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.

【 授权许可】

Unknown   

  文献评价指标  
  下载次数:0次 浏览次数:0次