利用深层卷积神经网络实现地形辅助的多波束海底底质分类

doi:10.11947/j.AGCS.2021.20200065

测绘学报 ›› 2021, Vol. 50 ›› Issue (1): 71-84.doi: 10.11947/j.AGCS.2021.20200065

利用深层卷积神经网络实现地形辅助的多波束海底底质分类

阳凡林^1,2, 朱正任¹, 李家彪³, 冯成凯¹, 邢喆⁴, 吴自银³

1. 山东科技大学测绘科学与工程学院, 山东青岛 266590;
2. 自然资源部海洋测绘重点实验室, 山东青岛 266590;
3. 自然资源部第二海洋研究所, 浙江杭州 310012;
4. 国家海洋信息中心, 天津 300171

收稿日期:2020-02-25 修回日期:2020-11-15 发布日期:2021-01-15
通讯作者: 朱正任 E-mail:Zhengren_zhu@163.com
作者简介:阳凡林(1974-),男,博士,教授,研究方向为海底地形测量和海洋定位导航。E-mail:yang723@163.com
基金资助:
国家自然科学基金（41930535；41830540）；国家重点研发计划（2018YFF0212203；2018YFC1405900；2017YFC1405006；2016YFC1401210）；山东科技大学科研创新团队支持计划（2019TDJH103）

Seafloor classification based on combined multibeam bathymetry and backscatter using deep convolution neural network

YANG Fanlin^1,2, ZHU Zhengren¹, LI Jiabiao³, FENG Chengkai¹, XING Zhe⁴, WU Ziyin³

1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China;
2. Key Laboratory of Oceanic Surveying and Mapping, Ministry of Natural Resources, Qingdao 266590, China;
3. Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China;
4. National Marine Data Information Center, Tianjin 300171, China

Received:2020-02-25 Revised:2020-11-15 Published:2021-01-15
Supported by:
The National Natural Science Foundation of China (Nos. 41930535;41830540);The National Key Research and Development Program of China (Nos. 2018YFF0212203;2018YFC1405900;2017YFC1405006;2016YFC1401210);The SDUST Research Fund (No. 2019TDJH103)

摘要/Abstract

摘要： 海底底质分类对于海洋资源开发与利用、海洋科学研究等多方面具有重要意义。目前，多波束探测是实现大范围海底底质分类的有效手段之一，通常基于多波束反向散射强度提取角度响应（AR）特征及反向散射图像特征进行底质分类。由于特征来源较单一，分类器结构简单，往往分类精度不高。为此，本文提出了一种基于深层卷积神经网络（CNN）的多波束海底底质分类方法。除反向散射强度特征外，还利用地形特征，将特征向量转换为波形图，再输入卷积神经网络进行训练和分类。试验对比不同特征组合以及BP网络、支持向量机（SVM）、K近邻（KNN）、随机森林（RF）4种常规分类器，本文模型算法总体分类精度达到94.86%，Kappa系数为0.93，精度具有明显优势，效率也比较高。表明该方法有效利用两种数据类型所蕴含的海底底质信息，充分发挥卷积神经网络权值共享、高效率等特点，实现高分辨率海底底质分类，可对海底底质分类研究提供参考。

关键词: 多波束, 反向散射图像, 角度响应, 底质分类, 卷积神经网络

Abstract: Seafloor classification is of great significance for the development and utilization of marine resources and marine scientific research. At present, multibeam detection is one of the effective methods to achieve large-scale seafloor classification. Seafloor classification is usually based on the angular response (AR) features and backscatter image features extracted by using multibeam backscatter. Because the feature source is relatively single and classifier structure is simple, the classification accuracy is often not high. This paper proposes a seafloor classification method based on convolutional neural networks (CNN). In addition to backscatter features, bathymetry features are also used to classify. The feature vectors are converted into waveform maps, and then input to the convolutional neural network for training and classification. The experiment compares different feature combination models and four conventional classifiers: BP network, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). The overall classification accuracy of the experiment reaches 94.86%, the kappa coefficient up to 0.93, and it takes 1 min 25 s. The accuracy has obvious advantages and the efficiency is relatively high. This method can effectively obtain the seafloor information in two different data types, give full play to the characteristics of convolutional neural network weight sharing, high efficiency, and achieve high-resolution seafloor classification. This paper provides a reference for the seafloor classification based on multibeam.

Key words: multibeam, backscatter image, angular response, seafloor classification, convolutional neural network

中图分类号:

P229.1

阳凡林, 朱正任, 李家彪, 冯成凯, 邢喆, 吴自银. 利用深层卷积神经网络实现地形辅助的多波束海底底质分类[J]. 测绘学报, 2021, 50(1): 71-84.

YANG Fanlin, ZHU Zhengren, LI Jiabiao, FENG Chengkai, XING Zhe, WU Ziyin. Seafloor classification based on combined multibeam bathymetry and backscatter using deep convolution neural network[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(1): 71-84.

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利用深层卷积神经网络实现地形辅助的多波束海底底质分类

Seafloor classification based on combined multibeam bathymetry and backscatter using deep convolution neural network

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