ELM-AdaBoost method of acoustic seabed sediment classification

Wang Jiachong; Wu Ziyin; Wang Mingwei; Zhou Jieqiong; Zhao Dineng; Luo Xiaowen

doi:10.12284/hyxb2021091

Volume 43 Issue 12

Dec. 2021

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Wang Jiachong，Wu Ziyin，Wang Mingwei, et al. ELM-AdaBoost method of acoustic seabed sediment classification[J]. Haiyang Xuebao，2021, 43(12)：144–151 doi: 10.12284/hyxb2021091

Citation:

Wang Jiachong，Wu Ziyin，Wang Mingwei, et al. ELM-AdaBoost method of acoustic seabed sediment classification[J]. Haiyang Xuebao，2021, 43(12)：144–151 doi: 10.12284/hyxb2021091

Citation:

Wang Jiachong，Wu Ziyin，Wang Mingwei, et al. ELM-AdaBoost method of acoustic seabed sediment classification[J]. Haiyang Xuebao，2021, 43(12)：144–151 doi: 10.12284/hyxb2021091

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ELM-AdaBoost method of acoustic seabed sediment classification

doi: 10.12284/hyxb2021091

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

Received Date: 2020-10-11
Rev Recd Date: 2021-01-19

Available Online: 2021-12-09

Publish Date: 2021-12-30

Abstract

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

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References(31)

References

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