海底声学底质分类的ELM-AdaBoost方法

王嘉翀; 吴自银; 王明伟; 周洁琼; 赵荻能; 罗孝文

doi:10.12284/hyxb2021091

海底声学底质分类的ELM-AdaBoost方法

doi: 10.12284/hyxb2021091

王嘉翀^1,,
吴自银^1, ,,
王明伟^{1, 2},
周洁琼¹,
赵荻能¹,
罗孝文¹

1.
自然资源部第二海洋研究所自然资源部海底科学重点实验室，浙江杭州 310012
2.
山东科技大学测绘与空间信息学院，山东青岛 266590

基金项目: 国家自然科学基金（41830540，42006073，41906069）；浙江省自然科学基金（LY21D060002）；中央级公益性科研所基本科研业务费专项资金项目（JZ1902，JG2005，SZ2002）；卫星海洋环境动力学国家重点实验室自主项目（SOEDZZ2101）；全球变化与海气相互作用专项（GASI-EOGE-01）

详细信息

作者简介:
王嘉翀（1995-），男，浙江省台州市人，研究方向为地球信息与探测技术。E-mail：444684215@qq.com

通讯作者:
吴自银（1972-），男，河南省信阳市人，研究员，研究方向为多波束海底地形地貌探测与研究。E-mail：ziyinwu@163. com

中图分类号: P714⁺.6
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- 被引次数: 0
出版历程
- 收稿日期: 2020-10-11
- 修回日期: 2021-01-19
- 网络出版日期: 2021-12-09
- 刊出日期: 2021-12-30

ELM-AdaBoost method of acoustic seabed sediment classification

1.
Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
2.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

摘要

摘要: 基于自适应增强算法（AdaBoost）结合极限学习机（ELM），通过迭代、调整、优化ELM分类器之间的权值，从而构建了具有强鲁棒性、高精度的ELM-AdaBoost强分类器，增强了现有的ELM分类器的稳定性。以珠江口海区侧扫声呐图像为实验数据，对礁石、砂、泥3类典型底质进行分类识别，该方法的平均分类精度超过90%，优于单一ELM分类器的平均分类精度85.95%，也优于LVQ、BP等传统分类器，且在分类所耗时间上也远少于传统分类器。实验结果表明，本文构建的ELM-AdaBoost方法可有效应用于海底声学底质分类，可满足实时底质分类的需求。
- 极限学习机 /
- 自适应增强算法 /
- 底质分类 /
- 声呐图像 /
- 特征提取
Abstract: Based on the adaptive boosting algorithm (AdaBoost) combined with the extreme learning machine (ELM), the strong classifier of ELM-AdaBoost with strong robustness and high precision is thus constructed by iterating, adjusting, and optimizing the weights between each ELM classifier. ELM-AdaBoost method can enhance the stability of the existing ELM classifier. In this paper, the data collected by side scan sonar in the Zhujiang River Estuary was used to classify and identify three types of typical sediments as rock, sand, and mud. The average classification accuracy of new method exceeds 90%, which is better than the average classification accuracy of a single ELM classifier of 85.95%. It is also superior to other traditional classifiers (i.e. LVQ and BP) and it takes much less time to classify than traditional classifiers. The experimental result shows that the proposed ELM-AdaBoost method can be effectively applied to the classification and identification of seabed sediment and can meet the needs of real-time classification of seabed sediment.
- extreme learning machine /
- adaptive boosting algorithm /
- sediment classification /
- sonar image /
- feature extraction

HTML全文

图 1 ELM网络结构

Fig. 1 Network structure of extreme learning machine

下载: 全尺寸图片幻灯片

图 2 基于ELM-AdaBoost方法的海底底质分类流程

Fig. 2 Flow chart of seabed sediment classification based on ELM-AdaBoost method

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图 3 研究区位置示意图（a）及礁石（b）、砂（c）和泥（d）3种典型底质的声呐图像

Fig. 3 Location of study area (a) and three typical seabed sediment sonar images of rock (b), sand (c) and mud (d)

下载: 全尺寸图片幻灯片

图 4 隐含层神经元个数对ELM分类性能影响

Fig. 4 The influence of the number of hidden layer neurons on the extreme learning machine classification performance

下载: 全尺寸图片幻灯片

图 5 ELM-AdaBoost和ELM误差绝对值对比

Fig. 5 Comparison of absolute error value between extreme learning machine-adaptive boosting and extreme learning machine

下载: 全尺寸图片幻灯片

图 6 5种分类器的分类精度对比

Fig. 6 Comparison of classification accuracy of five classifiers

下载: 全尺寸图片幻灯片

表 1 礁石、砂和泥3种底质的特征向量

Tab. 1 Characteristic vectors of three types of seabed sediment of rock, sand, and mud

	均值	标准差	对比度	相关系数	能量	熵
礁石	0.765 7	0.153 4	1.621 2	0.326 1	0.121 6	0.616 4
	0.724 2	0.180 2	3.750 0	0.032 8	0.087 6	0.577 1
	0.726 6	0.142 0	1.681 8	0.358 7	0.097 6	0.603 5
	0.675 0	0.155 9	2.659 1	0.194 7	0.062 1	0.574 6
	0.562 7	0.230 5	6.053 0	0.127 9	0.040 3	0.472 6
砂	0.848 8	0.104 7	1.015 1	0.235 8	0.159 9	0.694 7
	0.812 5	0.112 1	1.659 1	0.066 4	0.103 1	0.615 4
	0.768 6	0.127 6	2.560 6	–0.089 4	0.097 7	0.570 8
	0.760 8	0.114 5	1.712 1	0.023 6	0.107 1	0.625 6
	0.798 6	0.109 1	1.825 7	–0.164 6	0.118 0	0.602 6
泥	0.535 9	0.298 0	3.257 6	0.702 6	0.033 6	0.587 0
	0.279 7	0.252 5	3.151 5	0.604 3	0.106 6	0.664 4
	0.408 7	0.302 3	4.204 5	0.572 0	0.035 2	0.563 0
	0.512 7	0.288 2	3.454 5	0.654 4	0.039 4	0.586 9
	0.630 8	0.220 0	2.598 5	0.598 7	0.045 6	0.597 9

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表 2 5种分类器的分类性能对比表

Tab. 2 Comparison of classification performance of five classifiers

分类器	训练样本平均精度/%	底质类型	测试平均精度/%	所有测试样本平均精度/%	完成分类所耗平均时间/s
BP	89.80	礁石	86.88	82.52	5
		砂	81.44
		泥	79.23
LVQ	76.83	礁石	75.66	81.09	298
		砂	84.35
		泥	83.26
PSO-SVM	93.87	礁石	85.62	88.22	447
		砂	93.85
		泥	85.18
ELM	93.68	礁石	82.70	85.95	0.11
		砂	90.41
		泥	85.19
ELM -AdaBoost	93.56	礁石	91.92	90.40	0.37
		砂	91.58
		泥	87.70

下载: 导出CSV

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