Title :
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
fDate :
7/1/2004 12:00:00 AM
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;
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
DOI :
10.1109/LGRS.2004.831206
