Kernel methods for the detection and classification of fish schools in single-beam and multibeam acoustic data

被引：14

作者：

Buelens, Bart ^{[1
]}

Pauly, Tim ^{[1
]}

Williams, Raymond ^{[2
]}

Sale, Arthur ^{[2
]}

机构：

[1] Myriax Software Pty Ltd, Hobart, Tas 7001, Australia

[2] Univ Tasmania, Sch Comp & Informat Syst, Hobart, Tas 7001, Australia

来源：

ICES JOURNAL OF MARINE SCIENCE | 2009年 / 66卷 / 06期

关键词：

classification; detection of fish schools; kernel methods; multibeam sonar; SONAR; BEHAVIOR;

D O I：

10.1093/icesjms/fsp004

中图分类号：

S9 [水产、渔业];

学科分类号：

0908 ;

摘要：

A kernel method for clustering acoustic data from single-beam echosounder and multibeam sonar is presented. The algorithm is used to detect fish schools and to classify acoustic data into clusters of similar acoustic properties. In a preprocessing routine, data from single-beam echosounder and multibeam sonar are transformed into an abstracted representation by multidimensional nodes, which are datapoints with spatial, temporal, and acoustic features as components. Kernel methods combine these components to determine clusters based on joint spatial, temporal, and acoustic similarities. These clusters yield a classification of the data in groups of similar nodes. Including the spatial components results in clusters for each school and effectively detects fish schools. Ignoring the spatial components yields a classification according to acoustic similarities, corresponding to classes of different species or age groups. The method is described and two case studies are presented.

引用

页码：1130 / 1135

页数：6