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ELM-AdaBoost method of acoustic seabed sediment classification
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Jiachong Wang1, Ziyin Wu1, *, Mingwei Wang1, 2, Jieqiong Zhou1, Dineng Zhao1, Xiaowen Luo1
Haiyang Xuebao | 2021, 43(12) : 144 - 151
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Haiyang Xuebao | 2021, 43(12): 144-151
• Article •
ELM-AdaBoost method of acoustic seabed sediment classification
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Jiachong Wang1, Ziyin Wu1, *, Mingwei Wang1, 2, Jieqiong Zhou1, Dineng Zhao1, Xiaowen Luo1
Affiliations
  • 1Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
  • 2College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Published: 2021-12-30 doi: 10.12284/hyxb2021091
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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
Jiachong Wang, Ziyin Wu, Mingwei Wang, Jieqiong Zhou, Dineng Zhao, Xiaowen Luo. ELM-AdaBoost method of acoustic seabed sediment classification[J]. Haiyang Xuebao, 2021 , 43 (12) : 144 -151 . DOI: 10.12284/hyxb2021091
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Article Info
doi: 10.12284/hyxb2021091
  • Receive Date：2020-10-11
  • Online Date：2026-02-26
  • Published：2021-12-30
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  • Received：2020-10-11
  • Revised：2021-01-19
Funding
Affiliations
    1Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
    2College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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