DocumentCode :
2352938
Title :
Bottom classification using information in the spectral domain and time-frequency domain
Author :
Vray, D. ; Delachartre, P. ; Andrieux, N. ; Gimenez, G.
Author_Institution :
Lab. de Traitement du Signal et Ultrasons, CNRS, Villeurbanne, France
Volume :
2
fYear :
1994
fDate :
13-16 Sep 1994
Abstract :
This work deals with the response of underwater bottom insonified with wideband ultrasonic signals. The aim of the study is to investigate the feasibility to recognize the nature of sediment layer using a wideband sonar system. The processing of signals must take into account the wide frequency band of the backscattered echoes and so includes spectral analysis and time-frequency analysis. The experiments have been performed in a natural environment, in Lake Geneva. Bottom echoes have been collected on 5 different types of bottom. For each backscattered echo signal, 3 vectors of features have been extracted: directly from the amplitude of the impulse response spectrum or from the parametric modeling or from the time-frequency representation. The results of the classification by using a neural network are then analysed
Keywords :
geophysical signal processing; image classification; image recognition; sediments; seismology; sonar imaging; sonar signal processing; backscattered echo; bottom classification; explosion seismology; geophysical measurement technique; image recognition; marine sediment; neural net; neural network; seafloor imaging; sonar; spectral domain; time-frequency domain; underwater bottom insonified; wideband sonar; wideband ultrasonic signal; Feature extraction; Lakes; Neural networks; Parametric statistics; Sediments; Signal processing; Sonar; Spectral analysis; Time frequency analysis; Ultra wideband technology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
OCEANS '94. 'Oceans Engineering for Today's Technology and Tomorrow's Preservation.' Proceedings
Conference_Location :
Brest
Print_ISBN :
0-7803-2056-5
Type :
conf
DOI :
10.1109/OCEANS.1994.364124
Filename :
364124
Link To Document :
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