Classifying Sonar Images: Can a Computer-Driven Process Identify Eels?

  • Mueller A
  • Mulligan T
  • Withler P
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Abstract

The purpose of this study was to explore the extent to which a computer-driven process can be used to classify sonar images. The data we present come front a feasibility study for a hydroacoustic monitoring system aimed at the automatic detection of downstream-migrating adult American eels Anguilla rostrata in the intake canal of a small hydroelectric station. The images were collected by a dual-frequency identification sonar with sufficient resolution to show the distinct shape and swimming motion of eels, and thus to allow confident visual identification. The goal was to find a set of image processing, tracking, and patient recognition techniques that would reproduce the results of the visual classification. Of the three classification methods that we tested with our example data set, neural network analysis had the lowest misclassification rate for eels (7% of the eels being misclassified as debris) and the second-lowest misclassification rate for debris (5% of the debris being misclassified as eels). Discriminant function analysis misclassified 12% of the eels as debris and 4% of the debris as eels. A K-nearest-neighbor analysis initially provided the poorest results (17% misclassified eels and 12% misclassified debris). However, after applying an algebraic correction, K-nearest-neighbor analysis yielded an accurate estimate of the number of eels in the data set. We discuss the value of flagging cases of uncertain classification, how image processing and feature selection can affect the results, and how the numeric ratio of the targets present determines what error rates are acceptable. We conclude that, depending on the application, different degrees of automation may be achieved, ranging front a relatively high degree of human supervision in the classification of all potential targets to a fully automated process that requires only periodic quality control and adjustments of the classification model.

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Authors

  • Anna-Maria Mueller

  • Tim Mulligan

  • Peter K. Withler

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