Abstract
This paper presents a study results of the Artificial Neural Network (ANN) architectures [Self Organizing Map (SOM) and Multi-Layer Perceptron (MLP)] using single beam echosounding data. The single beam echosounder, operable at 12 kHz, has been used for backscatter data acquisitions from three distinctly different seafloor’s from the the Arabian Sea. With some preprocessing of the snapshots, the performance of the SOM network is observed to be quite good. For unsupervised SOM network, only single snapshot is used for the training, and number of snapshots for subsequent testing of the network. Feature selection from ASCII data is an important component for an supervised MLP based network. Four selected features are used for training the the network. The test results of the MLP based network are also discussed in the text.
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References
Stanton, T.K.: Sonar Estimates of Seafloor Microroughness. J. Acoust. Soc. Am. 75 (1984) 809–818
Chakraborty, B.: Effects of Scattering due to Seafloor Microrelief on a Multifrequency-Sonar Seabed Profiler. J. Acoust. Soc. Am. 85 (1989) 1478–1481
Chakraborty, B., Schenke, H.W., Kodagali, V., Hagen, R.: Seabottom Characterization using Multibeam Echosounder Angular Backscatter: An Application of the Composite Roughness Theory. IEEE TGARS. 38 (2000) 2419–2422.
Chakraborty B., Pathak, D.: Sea Bottom Backscatter Studies in the Western Continental Shelf of India. Jour. Sound Vib. 219 (1999) 51–62
Nair, R.R., Hashimi, N.H., Rao, V.P.: Distribution and Dispersal of Clay Minerals on the Western Continental Shelf of India. Mar. Geol. 50 (1982) 1–9
Chakraborty, B., Kaustubha, R., Hegde, A., Pereira, A.: Acoustic Seafloor Sediment Classification using SOMs (IEEE TGARS-In-Press).
Masters, T.: Practical Neural Network Recipes in C++, Academic Press, San Diego (1993)
Alexandrou, D., de Moustier, C., Haralabaus, G.,: Evaluation and Verification of Bottom Acoustic Reverberation Statistics Predicted by the Point Scattering Model. J. Acous. Soc. Am. 91(1992) 1403–1413
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© 2002 Springer-Verlag Berlin Heidelberg
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Chakraborty, B. (2002). A Neural Network Based Seafloor Classification Using Acoustic Backscatter. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_33
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DOI: https://doi.org/10.1007/3-540-45631-7_33
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