Riverbed sediment classification using multi-beam echo-sounder backscatter data.
A method has recently been developed that employs multi-beam echo-sounder backscatter data to both obtain the number of sediment classes and discriminate between them by applying the Bayes decision rule to multiple hypotheses [Simons and Snellen, Appl. Acoust. 70, 1258-1268 (2009)]. In deep water, the number of scatter pixels within the beam footprint is large enough to ensure Gaussian distributions for the backscatter strengths and to increase the discriminative power between acoustic classes. In very shallow water (<10 m), however, this number is too small. This paper presents an extension of this high-frequency methodology for these environments, together with a demonstration of its performance using backscatter data from the river Waal, The Netherlands. The objective of this work is threefold. (i) Increasing the discriminating power of the classification method: high-resolution bathymetry data allow precise bottom slope corrections for obtaining the true incident angle, and the high-resolution backscatter data reduce the statistical fluctuations via an averaging procedure. (ii) Performing a correlation analysis: the dependence of acoustic backscatter classification on sediment physical properties is verified by observing a significant correlation of 0.75 (and a disattenuated correlation of 0.90) between the classification results and sediment mean grain size. (iii) Enhancing the statistical description of the backscatter intensities: angular evolution of the K-distribution shape parameter indicates that the riverbed is a rough surface, in agreement with the results of the core analysis.
Amiri-Simkooei
AliReza
A
Acoustic Remote Sensing Group, Delft Institute of Earth Observation and Space Systems (DEOS), Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands. a.amirisimkooei@tudelft.nl
Snellen
Mirjam
M
Simons
Dick G
DG