Acta Geodaetica et Cartographica Sinica ›› 2024, Vol. 53 ›› Issue (3): 493-502.doi: 10.11947/j.AGCS.2024.20220677

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Intelligent identification of bottom layer boundaries in shallow sections of ocean waterways using improved region growth algorithm

JIANG Tingchen1,2, MENG Haofan1,3, WANG Xiao1,2, WANG Chaojin4, YANG Yi1,2, NING Yaoyao1,3, YAN Yuru5   

  1. 1. Jiangsu Ocean University School of Marine Technology and Geomatics, Lianyungang 222005, China;
    2. Jiangsu Provincial Marine Remote Sensing Engineering Research Center, Lianyungang 222005, China;
    3. Wuhan Changjiang Waterway Rescue and Salvage Bure, Wuhan 430010, China;
    4. Shanghai Da Hua Surveying&Mapping Technology Co., Ltd., Shanghai 201208, China;
    5. Key Laboratory of Coastal Salt Marsh Ecosystems and Resources, Ministry of Natural Resources, Nanjing 210007, China
  • Received:2022-11-29 Revised:2023-07-31 Published:2024-04-08
  • Supported by:
    The National Natural Science Foundation of China (No. 41004003);Jiangsu Provincial Department of Science and Technology Project (No. BE2016701); Jiangsu Provincial Marine Science and Technology Innovation Project (No. JSZRHYKJ202201);Jiangsu Provincial Water Conservancy Science and Technology Project Funding (No.2020058);Lianyungang City's “521 Project” scientific research funding approval project (No. LYG06521202131)

Abstract: It is of great significance to seabed exploration for the development and utilization of marine resources, marine engineering construction, and national defense security. As an acoustic device capable of surveying the distribution of bottom sediment in the shallow surface of the seabed, the accuracy of bottom sediment identification currently depends on the subjectivity of the operator, with poor reliability. In order to improve efficiency and interpretation accuracy, it is necessary to further study the intelligent identification model for the bottom layer boundary. In the paper, an improved region growth algorithm suitable for seabed bottom layer boundary recognition without human intervention is proposed. That is, based on the study of grayscale mapping and noise elimination, the skeleton information of the image layer boundary is extracted using an iterative maximum class difference algorithm, and then the skeleton information is used as an initial growth point and the growth direction is corrected using rheological properties. At the same time, the algorithm combines grayscale weighted mapping curves and peak valley wavelength constrained growth neighborhoods to segment layer boundaries, extract edges, and connect them into lines, thereby seabed bottom layer boundary recognition is ultimately achieved. The experimental results of shallow section survey data of Lianyungang port waterway show that the improved region growth algorithm can effectively identify the boundary of the bottom layer, and its recognition accuracy reaches centimeter level, which can meet the requirements of seabed sediment interpretation and analysis.

Key words: sub-bottom profiler, bottom layer boundary, maximum class difference algorithm, regional growth algorithm, automatic identification

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