Seafloor classification using echo-waveforms: a method employing hybrid neural network architecture

Author

Chakraborty, Bishwajit ; Mahale, Vasudev ; De Sousa, Carlyle ; Das, Pranab

Author_Institution

Nat. Inst. of Oceanogr., Goa, India

Volume

1

Issue

3

fYear

2004

fDate

7/1/2004 12:00:00 AM

Firstpage

196

Lastpage

200

Abstract

This letter presents seafloor classification study results of a hybrid artificial neural network architecture known as learning vector quantization. Single beam echo-sounding backscatter waveform data from three different seafloors of the western continental shelf of India are utilized. In this letter, an analysis is presented to establish the hybrid network as an efficient alternative for real-time seafloor classification of the acoustic backscatter data.

Keywords

acoustic signal detection; acoustic transducers; geophysics computing; neural net architecture; oceanographic regions; oceanographic techniques; remote sensing; seafloor phenomena; self-organising feature maps; underwater sound; India; acoustic backscatter data; hybrid neural network architecture; learning vector quantization; remote sensing; seafloor classification; self organizing feature map; single beam echo-sounding backscatter waveform; western continental shelf; Acoustic beams; Acoustic scattering; Artificial neural networks; Backscatter; Grain size; Neural networks; Oceanographic techniques; Sea floor; Sediments; Vector quantization; Learning vector quantization; SOFM; neural network architecture; seafloor classification; self-organizing feature map;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing Letters, IEEE

Publisher

ieee

ISSN

1545-598X

Type

jour

DOI

10.1109/LGRS.2004.831206

Filename

1315631