Bathymetry reconstruction from sidescan sonar images using neural networks

Authors

  • Julian Christopher Maypa ⋅ PH National Institute of Physics, University of the Philippines Diliman
  • Maricor N. Soriano ⋅ PH National Institute of Physics, University of the Philippines Diliman

Abstract

We demonstrate a method to estimate bathymetry from sidescan sonar images using machine learning. The mapping of sonar image intensity values to bathymetry maps is learned by a convolutional neural network autoencoder. This network is trained with simulated images derived from the Lambertian model, and it is tested with never-before-seen simulated images and actual sidescan sonar images. After training, the network was able to estimate bathymetry maps from actual sidescan images with an error in the sub-meter level. This degree of error is useful for the navigation of ships, construction of submarine pipelines, and management of marine protected areas.

Downloads

Issue

Brewing waves of innovation and discovery in Physics
3-6 July 2024, Batangas State University, Pablo Borbon Campus

Please visit the SPP2024 activity webpage for more information on this year's Physics Congress.

Article ID

SPP-2024-3E-05

Section

Complex Systems and Data Analytics

Published

2024-06-26

How to Cite

[1]
JC Maypa and MN Soriano, Bathymetry reconstruction from sidescan sonar images using neural networks, Proceedings of the Samahang Pisika ng Pilipinas 42, SPP-2024-3E-05 (2024). URL: https://proceedings.spp-online.org/article/view/SPP-2024-3E-05.