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
fDate :
12/1/2001 12:00:00 AM
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;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
