ASA 130th Meeting - St. Louis, MO - 1995 Nov 27 .. Dec 01

4pUW13. Classification of lake bottom sediments by neural networks using wideband echo signals.

Yuanliang Ma

College of Marine Eng., Northwestern Polytechnical Univ., Xi'an 710072, People's Republic of China

Z. Y. Wang

Northwestern Polytechnical Univ.

G. Gimenez

D. Vray

CREATIS, INSA, Lyon, France

The paper presents a method of lake bottom sediment classification by artificial neural networks (ANN) using wideband echo signals. The samples to be classified were acquired by experiments at Lake Geneva. There are five types of sediments, namely, silt, rocks, pebbles, sand, and a mixture of sand and gravel. The pattern features are extracted from spectra of echo signals in subband energy expression. Different subband divisions for frequency-domain feature extraction are compared and it is shown that the contant Q method provides better results in comparison with the constant bandwidth method. Using the constant Q method in association with a BP-type locally connected neural network, 85.1% correct classification in average has been achieved for a testing data set. Wideband echo signals have outstanding superiority for classification in comparison with narrow-band signals. It contains more information representing the physical and architectural features of targets. The neural network ultilizes the information through careful optimization and provides a performance improvement up to 10%.