[1]ZHAO Yuxin,ZHAO Ting.Survey of the intelligent seabed sediment classification technology based on sonar images[J].CAAI Transactions on Intelligent Systems,2020,15(3):587-600.[doi:10.11992/tis.202004026]
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Survey of the intelligent seabed sediment classification technology based on sonar images

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 3 Page number: 587-600 Column: 人工智能院长论坛 Public date: 2020-05-05
Title:
Survey of the intelligent seabed sediment classification technology based on sonar images
Author(s):
ZHAO Yuxin;  ZHAO Ting
College of Automation, Harbin Engineering University, Harbin 150001, China
Keywords:
acoustic detection; sonar image; sediment type; feature extraction; image classification; supervized learning; unsupervized learning; deep learning; convolutional neural network; seabed sediment classification
CLC:
TP753
DOI:
10.11992/tis.202004026
Abstract:
Image-based acoustic seabed sediment classification refers to the technology of inversion and prediction of the marine geological attributes of the shallow strata using seabed sonar image obtained using a multi-beam, side-scan sonar. As the multidisciplinary branch of oceanology, this technology is able to quickly identify a sediment type based on the relevant knowledge of underwater acoustics, image processing, and machine learning. Based on the review on the history and development of the technology at home and abroad, this article summarizes the key techniques in the framework of seabed sediment classification using sonar image and makes an introduction to the progress in research and main algorithms used in the domain, including the geoacoustic relationship model, the feature expression of the seabed sonar image, and the building of classification model. The emphasis is put on the analysis of the principles, technical features, and applications for various models and algorithms. Deep learning is also discussed for exploring proper application in the acoustic seabed classification with the case of convolutional neural network. The deep learning algorithms are applied on the sonar images and analyzed . Finally, acoustic image-based seabed sediment classification is completed and forecast is done.
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