Acoustic seafloor sediment classification using self-organizing feature maps

被引：23

作者：

Chakraborty, B ^{[1
]}

Kaustubha, R ^{[1
]}

Hegde, A ^{[1
]}

Pereira, A ^{[1
]}

机构：

[1] Natl Inst Oceanog, Panaji 403004, Goa, India

来源：

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2001年 / 39卷 / 12期

关键词：

artificial neural network (ANN); echosounder; seafloor sediment classifications; self-organizing feature map (SOFM);

D O I：

10.1109/36.975006

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

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 (similar to 100%) for an ideal output neuron grid size of 15 X 1 is obtained for a moving average of 35 input snapshots.

引用

页码：2722 / 2725

页数：4

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