Acoustic Seabed Classification Based on Multibeam Echosounder Backscatter Data Using the PSO-BP-AdaBoost Algorithm: A Case Study From Jiaozhou Bay, China

When backpropagation neural network (BPNN) is often applied to supervised classification, problems arise, including a slow convergence rate, local extremum, and difficulty in determining the number of hidden layers and hidden nodes that affect the classification accuracy and efficiency. These proble...

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Published in:	IEEE journal of oceanic engineering Vol. 46; no. 2; pp. 509 - 519
Main Authors:	Ji, Xue, Yang, Bisheng, Tang, Qiuhua
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Acoustic seabed classification Acoustics AdaBoost algorithm Algorithms Artificial neural networks Back propagation Back propagation networks back-propagation neural network (BPNN) Backscatter Backscattering Classification Classifiers (sedimentation) Data Decision trees Discrete wavelet transforms Echo sounding Echosounders Machine learning Microscopy Model accuracy multibeam echosounder Neural networks Ocean floor Particle swarm optimization particle swarm optimization (PSO) Sediments Sonar Support vector machines Jiaozhou Bay
ISSN:	0364-9059, 1558-1691
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Abstract	When backpropagation neural network (BPNN) is often applied to supervised classification, problems arise, including a slow convergence rate, local extremum, and difficulty in determining the number of hidden layers and hidden nodes that affect the classification accuracy and efficiency. These problems can be overcome by using smarter network designs. Adaptive boosting (AdaBoost), which combines multiple weak classifiers to create a strong classifier, has a strong classification advantage. In this article, we propose an acoustic seabed classification method that combines AdaBoost with the particle swarm optimization (PSO). The PSO-BP-AdaBoost algorithm uses multibeam echosounder backscatter data to solve the multiclassification problem of diverse seafloor sediment types with small differences between types. We optimize a BPNN using the PSO algorithm to obtain the optimal initial weight and threshold and combine these to form an AdaBoost strong classifier. The input data is obtained from the sonar mosaic from multibeam echosounder backscatter data collected in Jiaozhou Bay using a series of fine processing techniques. These processing techniques result in 34-dimensional (34-D) features using ReliefF analysis. The most advantageous 8-D features are used as input into the AdaBoost algorithm based on one-level decision tree, PSO-BP algorithm, support vector machine (SVM), and PSO-BP-AdaBoost algorithm. The PSO-BP-AdaBoost classification model has better classification accuracy. The overall accuracy is improved by 12.68%, 6.78%, and 3.56%, respectively, which demonstrates that the PSO-BP-AdaBoost algorithm can be effectively applied to acoustic seabed classification and identification and achieves high precision.
AbstractList	When backpropagation neural network (BPNN) is often applied to supervised classification, problems arise, including a slow convergence rate, local extremum, and difficulty in determining the number of hidden layers and hidden nodes that affect the classification accuracy and efficiency. These problems can be overcome by using smarter network designs. Adaptive boosting (AdaBoost), which combines multiple weak classifiers to create a strong classifier, has a strong classification advantage. In this article, we propose an acoustic seabed classification method that combines AdaBoost with the particle swarm optimization (PSO). The PSO-BP-AdaBoost algorithm uses multibeam echosounder backscatter data to solve the multiclassification problem of diverse seafloor sediment types with small differences between types. We optimize a BPNN using the PSO algorithm to obtain the optimal initial weight and threshold and combine these to form an AdaBoost strong classifier. The input data is obtained from the sonar mosaic from multibeam echosounder backscatter data collected in Jiaozhou Bay using a series of fine processing techniques. These processing techniques result in 34-dimensional (34-D) features using ReliefF analysis. The most advantageous 8-D features are used as input into the AdaBoost algorithm based on one-level decision tree, PSO-BP algorithm, support vector machine (SVM), and PSO-BP-AdaBoost algorithm. The PSO-BP-AdaBoost classification model has better classification accuracy. The overall accuracy is improved by 12.68%, 6.78%, and 3.56%, respectively, which demonstrates that the PSO-BP-AdaBoost algorithm can be effectively applied to acoustic seabed classification and identification and achieves high precision.
Author	Ji, Xue Yang, Bisheng Tang, Qiuhua
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SubjectTerms	Accuracy Acoustic seabed classification Acoustics AdaBoost algorithm Algorithms Artificial neural networks Back propagation Back propagation networks back-propagation neural network (BPNN) Backscatter Backscattering Classification Classifiers (sedimentation) Data Decision trees Discrete wavelet transforms Echo sounding Echosounders Machine learning Microscopy Model accuracy multibeam echosounder Neural networks Ocean floor Particle swarm optimization particle swarm optimization (PSO) Sediments Sonar Support vector machines
Title	Acoustic Seabed Classification Based on Multibeam Echosounder Backscatter Data Using the PSO-BP-AdaBoost Algorithm: A Case Study From Jiaozhou Bay, China
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