### ASA 125th Meeting Ottawa 1993 May

## 5aAO13. Application of neural and statistical classifiers to the problem
of seafloor characterization.

**Zoi-Heleni Michalopoulou
**

**
Dimitri Alexandrou
**

**
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*Duke Univ., Dept. of Elec. Eng., Box 90291, Durham, NC 27708-0291
*

*
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Simulated seafloor backscatter is obtained by employing the Kirchhoff
approximation and the statistical properties of bottom reverberation. Such data
are presented to multilayer perceptrons for training and testing, aiming at the
development of a neural processor that discriminates among signal returns
coming from seafloors with different roughness parameters. Experiments on the
same data sets are performed with optimum Bayesian classifiers as well; a
comparison of the results indicates suboptimum performance for the perceptrons.
The same procedure is followed with real data collected by the bathymetric
system Sea Beam over Horizon Guyot and Magellan Rise. In this case, the
perceptron performance is comparable to that of the Bayesian classifier, which
is no longer optimum, since no prior knowledge of the probability distribution
parameters is available. In addition, self-organizing maps have been applied to
both synthetic and real data sets and resulted in a successful separation of
the output space into distinct regions corresponding to the existing classes.
[Work supported by the Office of Naval Research, Code 1125GG, through Contract
No. N00014-93-I-0049.]