Simulation of Bathymetry Pattern from Polarised TOPSAR
Maged Marghany , Mohd. Lokman Hussien and K.B. Yuins
Institute of Oceanography (INOS)
University College Science and Technology Malaysia
21030 Mengabang Telipot, Kuala Terenggaun
Malaysia
Introduction
Mapping of shallow water bathymetry by air or space borne Synthetic Aperture Radar (SAR) has not been yet received any attention by coastal engineering and hydrographical services. However, shallow bathymetry information plays a vital role for navigation safety, various economics proposes and environmental protection. The extraction of shallow waters bathymetry from SAR images is much operational compared to the traditional methods such as advanced ship-borne echo sounders and multi-beam system. This is because of the fact that SAR images can cover area of 100 km x 100 km. In addition, the traditional methods are rather expensive and time consuming and cannot be operated in very shallow waters such the coastal waters of Kuala Terengganu, Malaysia.
SAR Imaging Mechanisms
According to Alpers and Hennings (1984), the under bottom topography can be shown in SAR images with moderate winds of 3 to 5 m/s and tidal current speed of about 0.5 m/s. This imaging mechanism is consisted of three stages. These stages are based on the effect of tidal current on the radar backscatter cross section. First, interaction between tidal current and bottom topography results in modulations in the surface current velocity. Second, the modulations in surface current speed generate variations in the wave spectra intensity. Third, the variations in the wave spectra intensity induce modulations in the level of radar backscatter (Hesselmans et al., 2000).
These techniques can only be applied in shallow waters with high current speed and requires certain geometry between bathymetry gradient, current and radar look direction.
Methodology
Radar Image
TOPSAR image was acquired at December 6, 1996 has been used to simulate the under water bottom topography. C band with vv polarization has been chosen for this propose.
Gaussian Kernal Model
TOPSAR data are dominated by speckle noises. This problem could be effect the accuracy of water depth estimation. The convolving the TOPSAR raw data with a Gaussian kernal will produce smooth image. The change of the grey level can be detected easier. In this case, the image contours will be generated. The change of the image contours will be function of the grey level variation. The linear model assumed that the change of image signature would be followed by the change of the sea state pattern along the image. This method is named by anisortropic diffusion. This relation can be given as follows:
I(t)=I0(x,y)*G(x,y,t) (1)