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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Bibliographic Details
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
Online Access:	Get full text
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