Performance and Progress Report - John Doe FIX: put in the authors ...

IMPROVED SIDESCAN SONAR AND BACKSCATTER PROCESSING:
Geocoder:
While our initial data processing efforts focused on improving bathymetric processing, it
has become increasingly clear that there is also a great need for improved processing of
backscatter data (both from multibeam sonars and sidescan sonars). With this in mind,
we began, in 2005, a new effort aimed at improving the suite of backscatter processing
tools available to us and NOAA. Our aim was two-fold: 1- to develop easy to use tools
that will generate “pretty” images of sidescan sonar or multibeam backscatter that will be
suitable for small object detection as well as geologic and habitat interpretation, and; 2-
to develop tools that allow for the quantitative analysis of backscatter data in support of
seafloor characterization and small object identification.
In an effort to meet these two objectives, we started a lab-wide effort to develop a new
suite of backscatter processing tools. This effort is being led by Luciano Fonseca with
input from many others. The goal is to create an integrated suite of tools that will allow
us to import backscatter or sidescan data from a number of sensors (in various formats,
including Reson snippet data), convert these data to an internal GFS format, correct these
data (where possible) for source levels, beam patterns, gains, area ensonified, attenuation,
and local slope, and then either analyze and/or display these data in a georeferenced
mosaic. A number of modules have now been developed including GEOCODER, a C++
mosaicking tool that now reads directly from GSF or XTF files (including Reson snippet
data), corrects data for gains and removes speckle. Special attention has been given to
correcting for beam patterns. Normally, the empirical beam pattern correction is
calculated as the residual necessary to flatten the angular response registered by the sonar
system, i.e. to normalize the backscatter at 45 degrees, (sometimes adding a Lambertian
correction). The new approach now used by Geocoder calculates the beam pattern as the
residual to the modeled angular response of the ensonified seafloor which then reveals
the actual non-linearity of the transducer angular response.
Data is then geocoded in a projected coordinate system using an interpolation scheme
that emulates the acquisition geometry.
Along with the full time series backscatter data from both Reson and Simrad systems,
GEOCODER also fully supports average beam backscatter data from these systems. A
feathering algorithm to smooth the transition between overlapping lines has been added
along with an anti-aliasing algorithm that makes it possible to produce a lower resolution
mosaic that is not degraded by aliasing, a process for slant-range correction based on
actual bathymetry, and a trend-adaptive angle-varying gain that helps remove artifacts
that appear when different bottom types are found along a single swath. Lines can be
removed or remosaicked, and the overlap area between parallel lines can be controlled by
filter parameters. GEOCODER also supports a statistical package that identifies patterns
in the backscatter response that can be used in support of seafloor characterization (see
below). Statistics calculated for backscatter bins include: mean, mode, range, minimum,
maximum, standard deviation, variance, percentiles, quartile range, skewness, kurtosis,
moments of any order, and also parameters extracted from a gray level co-occurrence
matrix (contrast, homogeneity, dissimilarity, entropy and energy). Taking advantage of
JHC Performance Report 31 30 January 2007