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Sensors 2018, 18(11), 3828; https://doi.org/10.3390/s18113828

Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea

1
College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2
Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Hangzhou 310012, China
3
School of Electronic Information, Wuhan University, Wuhan 430079, China
4
Sino Australian Research Centre for Coastal Management, University of New South Wales, Canberra, BC 2610, Australia
5
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Hangzhou 310012, China
*
Authors to whom correspondence should be addressed.
Received: 3 September 2018 / Revised: 30 October 2018 / Accepted: 5 November 2018 / Published: 8 November 2018
(This article belongs to the Section Remote Sensors)
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Abstract

Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about seafloor features and are important data sources representing seafloor topography and geomorphology. Currently, to classify seafloor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify seafloor types using LiDAR, waveform features are extracted from bottom returns. This paper comprehensively considers the features of both LiDAR waveforms and MBES backscatter images that include the eight feature factors of the LiDAR full waveforms (amplitude, peak location, full width half maximum (FWHM), skewness, kurtosis, area, distance, and cross-section) and the eight feature factors of MBES backscatter images (mean, standard deviation (STD), entropy, homogeneity, contrast, angular second moment (ASM), correlation, and dissimilarity). Based on a support vector machine (SVM) algorithm with different kernel functions and penalty factors, a new seafloor classification method that merges multiple features is proposed for a beneficial exploration of acousto-optic fusion. The experimental results of the seafloor classification around Yuanzhi Island in the South China Sea indicate that, when LiDAR waveform features are merged (using an Optech Aquarius system) with MBES backscatter image features (using a Sonic 2024) to classify three types of sands, reefs, and rocks, the overall accuracy is improved to 96.71%, and the kappa reaches 0.94. After merging multiple features, the classification accuracies of the SVM, genetic algorithm SVM (GA-SVM) and particle swarm optimization SVM (PSO-SVM) increase by an average of 9.06%, 3.60%, and 2.75%, respectively. View Full-Text
Keywords: LiDAR; MBES; multifeature; seafloor classification; SVM LiDAR; MBES; multifeature; seafloor classification; SVM
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, M.; Wu, Z.; Yang, F.; Ma, Y.; Wang, X.H.; Zhao, D. Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea. Sensors 2018, 18, 3828.

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