海底声呐图像智能底质分类技术研究综述  

Survey of the intelligent seabed sediment classification technology based on sonar images

作　　者：赵玉新[1] 赵廷 ZHAO Yuxin;ZHAO Ting(College of Automation,Harbin Engineering University,Harbin 150001,China)

机构地区：[1]哈尔滨工程大学自动化学院,黑龙江哈尔滨150001

出　　处：《智能系统学报》2020年第3期587-600,共14页CAAI Transactions on Intelligent Systems

基　　金：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).

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。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.

关 键 词：声学探测 声呐图像 底质类型 特征提取 图像分类 监督学习 无监督学习 深度学习 卷积神经网络 海底底质分类 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]