Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Nearshore Bathymetry Estimation Based on Image Feature of X-Band Marine Radar
Nugrahinggil Subasita,1,2, Tri Widya Laksana Putra1, Henokh Lugo Hariyanto1,2, Mashury Wahab3, Akmal Brilly Ashidiqi3, Yazid
Hilman Maulana3, Topik Teguh Estu3, Andonowati1,2, Andreas Parama Wijaya1,41Bahari Berjaya Indonesia, Bandung, West Java, Indonesia
2Labmath Indonesia, Bandung, West Java, Indonesia
3Radar Telekomunikasi Indonesia, Bandung, West Java, Indonesia
4Department of Mathematics, Parahyangan Catholic University, Bandung, West Java, Indonesia

Holman et al., (2013) introduced an algorithm which is so-called“cBathy” to estimate bathymetry from recorded images of waves. Thealgorithm was divided into three phases: 1) estimation of wavenumber
for each frequency samplings using cross-spectral correlation at a small
rectangular patch, 2) eliminating frequency-dependency for each depth
to have a single depth estimation, and 3) using Kalman filter to get thebest estimate of depths for long-term analysis. The algorithm wasimproved by Holman and Stanley (2013) such that it was able toestimate the bathymetry near an estuary. This case was much moredifficult because of the presence of strong inlet current near the coast.
Honegger et al. (2019) applied the cBathy algorithm to X-band radar
images and the result was comparable to the depth estimation using theoptical video imagery.

ABSTRACTThis paper presents a depth inversion approach that uses a directestimation of the wave period and wavelength locally in space fromradar images. The wave periods are retrieved by time-signals analysis
at several points. The wavelengths are directly measured from thedistance of successive wave rays. The method is tested on synthetic andreal image datasets in Cilacap, Pangandaran, and Glagah Beach,Indonesia. The mean average error (MAE) for the measured depth is
0.99 m up to a radius of 2.5 km at Pangandaran beach and MAE of 0.86
m up to the depth of 30 m at Glagah beach.
KEYWORDS: X-Band Marine Radar, Remote Sensing, Coastal Depth
Inversion.

Unlike optical imaging, X-band marine radars produce sea-clutter
images from Bragg scattering of the incident radar energy that
resonated over surface water waves (see Fig. 1). This signature of thesea surface is commonly visible on a radius under 3 nautical miles orabout 5.6 km (Holman et al., 2013). Attempts to estimate the pairs ofwavenumber and wave frequency (or wave period and wave celerity)from radar images have been carried out (e.g., Bell, 1999; Hessner etal., 1999). The resulting parameters were used to estimate the water
depth via the dispersion relation from the linear wave theory. The mostcommon method to derive water depth from radar images is based on
3D-FFT analysis. Performing a 3D-FFT to radar images yields thewavenumber-frequency image spectrum. A curve fitting process thatminimizes the difference of the wavenumber-frequency pairs from thedispersion function can be used to estimate the depth (e.g., Trizna,2001; Friedman, 2014). Serafino et al., (2010) and Ludeno et al., (2015)
used the resulting image spectrum and a characteristic function, basedon the dispersion relation, to form a Normalized Scalar Product (NSP).
A number that maximizes the NSP is chosen as the estimated depth. A
2D-FFT approach was proposed by Abileah and Trizna (2010). The 2D
Fourier components of image pairs were compared by applying a
propagation kernel on the preceding image. The estimated depth wasobtained by minimizing the difference of the Fourier components.
Wijaya and Jabbar (2020) proposed a bathymetry inversion that
included an image reconstruction process called Dynamic Averagingand Evolution Scenario (DAES).

INTRODUCTION
Ocean wave parameters detection is one of the ubiquitous ways forunderstanding and controlling the wave phenomena. Numerous
physically derived wave parameters are very useful for practical
purposes such as significant wave height, peak period, wavelength, andwave spectrum. The other important information is bathymetry,especially for coastal activities to maintain the shore fromsedimentation or erosion. For harbours, bathymetry needs to bemonitored regularly to prevent ships from damage due tosedimentation. The usual way to measure the water depth is to attach an
echo sounding device on ships that sails over a target area. Although
this technique is able to generate accurate point measurements, its
drawbacks are expensive and time-consuming.
An alternative method is to use remote sensing, which provides more
flexibility and cost-effectiveness. The most common remote sensing
technique is based on the imaging of the sea surface. The bathymetry
can be estimated since there exists a relation between the wave
parameters (that are extracted from the images) with the water depth.
Stockdon and Holman (2000) estimated nearshore bathymetry fromvideo imagery. They used cross-spectral correlation to estimate
separately the alongshore and cross-shore wavenumbers. Then using
the dispersion relation from the linear theory of wave propagation, theestimated depth for a 30 days period of measurement was obtained.

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