Neural Network Normal Estimation and Bathymetry Reconstruction from Sidescan Sonar

TL;DR

This paper presents a deep learning-based inverse model to estimate seabed surface normals from sidescan sonar data, enabling high-quality bathymetry reconstruction through an optimization framework that fuses multiple observations and altimeter constraints.

Contribution

It introduces a novel deep learning approach to estimate seabed normals from sidescan data and integrates it with neural implicit representations for accurate bathymetry reconstruction.

Findings

High-quality bathymetry reconstruction demonstrated.

Fusion of multiple observations improves results.

Deep learning model outperforms traditional forward models.

Abstract

Sidescan sonar intensity encodes information about the changes of surface normal of the seabed. However, other factors such as seabed geometry as well as its material composition also affect the return intensity. One can model these intensity changes in a forward direction from the surface normals from bathymetric map and physical properties to the measured intensity or alternatively one can use an inverse model which starts from the intensities and models the surface normals. Here we use an inverse model which leverages deep learning's ability to learn from data; a convolutional neural network is used to estimate the surface normal from the sidescan. Thus the internal properties of the seabed are only implicitly learned. Once this information is estimated, a bathymetric map can be reconstructed through an optimization framework that also includes altimeter readings to provide a sparse depth profile as a constraint. Implicit neural representation learning was recently proposed to represent the bathymetric map in such an optimization framework. In this article, we use a neural network to represent the map and optimize it under constraints of altimeter points and estimated surface normal from sidescan. By fusing multiple observations from different angles from several sidescan lines, the estimated results are improved through optimization. We demonstrate the efficiency and scalability of the approach by reconstructing a high-quality bathymetry using sidescan data from a large sidescan survey. We compare the proposed data-driven inverse model approach of modeling a sidescan with a forward Lambertian model. We assess the quality of each reconstruction by comparing it with data constructed from a multibeam sensor.