DocumentCode
1558272
Title
Acoustic seafloor sediment classification using self-organizing feature maps
Author
Chakraborty, Bishwajit ; Kaustubha, R. ; Hegde, Amey ; Pereira, Ashley
Author_Institution
Nat. Inst. of Oceanogr., Goa, India
Volume
39
Issue
12
fYear
2001
fDate
12/1/2001 12:00:00 AM
Firstpage
2722
Lastpage
2725
Abstract
A self-organizing feature map (SOFM), a kind of artificial neural network (ANN) architecture, is used in this work for continental shelf seafloor sediment classification. Echo data are acquired using an echosounding system from three types of seafloor sediment areas. Excellent classification (~100%) for an ideal output neuron grid size of 15×1 is obtained for a moving average of 35 input snapshots
Keywords
geophysical signal processing; geophysical techniques; pattern classification; sediments; self-organising feature maps; sonar signal processing; ANN architecture; SOFM; acoustic seafloor sediment classification; artificial neural network architecture; continental shelf seafloor; echo data; echosounding system; self-organizing feature maps; Acoustic scattering; Artificial neural networks; Backscatter; Circuits; Data acquisition; Delay; Grain size; Oceanographic techniques; Sea floor; Sediments;
fLanguage
English
Journal_Title
Geoscience and Remote Sensing, IEEE Transactions on
Publisher
ieee
ISSN
0196-2892
Type
jour
DOI
10.1109/36.975006
Filename
975006
Link To Document