[1]赵玉新,赵廷.海底声呐图像智能底质分类技术研究综述[J].智能系统学报,2020,15(3):587-600.[doi:10.11992/tis.202004026]
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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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]
海底声呐图像智能底质分类技术研究综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第3期
页码:
587-600
栏目:
人工智能院长论坛
出版日期:
2020-05-05
- Title:
- Survey of the intelligent seabed sediment classification technology based on sonar images
- Keywords:
- acoustic detection; sonar image; sediment type; feature extraction; image classification; supervized learning; unsupervized learning; deep learning; convolutional neural network; seabed sediment classification
- 分类号:
- TP753
- 摘要:
- 海底声呐图像底质分类技术是指利用多波束、侧扫声呐等设备获取海底图像进行浅表层地质属性信息的反演和预测。综合运用水声学、图像处理以及机器学习的相关理论，声学海底底质分类技术能够快速、准确地识别海底底质类型。通过回顾国内外发展历程，对利用声学图像进行海底底质分类的关键技术进行了总结，从声学海底底质分类的关系模型、海底声呐图像的特征表达和分类模型构建三个方面介绍了领域内的研究进展和主要方法，重点分析了不同模型和方法的原理、技术特点和适用场合，并结合卷积神经网络对深度学习方法在海底底质分类中的应用进行了讨论。最后，对海底声呐图像底质分类技术的研究方向和发展趋势进行了归纳和展望。
- 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.
- 参考文献/References:
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备注/Memo
收稿日期:2020-04-24。
基金项目:国家重点基础研究发展计划(613317)；国家自然科学基金面上项目(41676088)
作者简介:赵玉新，教授，博士生导师，工信部高技术船舶通信导航与智能系统专业组秘书长，中国航海学会理事，中国运筹学会决策科学分会常务理事，IET(英国工程技术学会)Fellow，IEEE高级会员，主要研究方向为水下导航技术及应用、业务化海洋学、智能航海技术。主持国防973课题、国家重大专项课题、国家自然科学基金等多个科研项目。发表学术论文100余篇。出版学术著作4部;赵廷，博士研究生，主要研究方向为海底探测、海洋遥感、图像处理、机器学习
通讯作者:赵廷.E-mail:zhaoting@hrbeu.edu.cn
更新日期/Last Update:
1900-01-01