Using three dimensional convolutional neural networks for denoising echosounder point cloud data-Reference-Cited by-同舟云学术

Using three dimensional convolutional neural networks for denoising echosounder point cloud data

Published:2020-03 Issue: Volume:5 Page:100016
ISSN:2590-1974
Container-title:Applied Computing and Geosciences
language:en
Short-container-title:Applied Computing and Geosciences

Author:

Stephens David,Smith Andrew,Redfern Thomas,Talbot Andrew,Lessnoff Andrew,Dempsey Kari

Publisher

Elsevier BV

Reference43 articles.

1. Cleaning massive sonar point clouds;Arge;Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS ’10,2010

2. Automatic processing of high-rate, high-density multibeam echosounder data;Calder;Geochem. Geophys. Geosyst.,2003

3. Computationally efficient variable resolution depth estimation;Calder;Comput. Geosci.,2017

4. Deep big simple neural nets excel on hand- written digit recognition;Ciresan;arXiv,2010

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