Abstract

Multibeam Bathymetry Data Could Represent Nearly Continuous Coverage Depth Measurements of the Seafloor and Reveal Geomorphological Regions. Recent Studies Have Utilized Multibeam Bathymetry Data to Provide Geological Maps, but their Delineations Were Done Manually. Manual Classification and Delineation Are Inherently Subjective and Therefore Can Be Inaccurate. in This Paper, We Try to Develop One Strategy to Explore Seafloor Stretching in Mariana Trench Arc Via Squeeze and Excitation Network, Combining Data Clustering, Slope and Gradient. in Our Experiments, We Use the High-Resolution Multibeam Bathymetric Data Collected by Noaa Office of Ocean Exploration and Research (Oer). the Geomorphological Seabed in the Mariana Region is Automatically Classified into Different Classes. the Experimental Results Demonstrate that Geomorphological Seabed Classification Strategy Achieves a Robust, Automated Delineation Approach.

Department(s)

Engineering Management and Systems Engineering

Comments

National Natural Science Foundation of China, Grant 2014AA093410

Keywords and Phrases

Classification; Convolutional Neural Networks; Geomorphological Seabed; Multibeam Bathymetry

International Standard Book Number (ISBN)

978-172811450-7

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jun 2019

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