Acoustical methods provide rapid, non-invasive, and synoptic tools for studying marine ecosystems. Despite the dramatic advances in this technology during the past three decades, there is presently a large disparity between the demand for quantitative information about marine animals and the capability of acoustic systems to deliver this information. A primary reason for this disparity is the strong dependence of acoustic scatter from marine animals on their size, shape, in situ orientation, and taxa. In a typical setting, these parameters are unknown, and are difficult to determine using existing acoustic methods. To mitigate this problem, a multi-view, broadband approach to marine animal classification and size estimation is investigated in this thesis.
Initially, zooplankton classification was investigated for two ecologically important taxa: copepods and euphausiids. Numerical simulations compared physics-based feature transformations, Nearest Neighbor (NN), and Multi-Layer Perceptron (MLP) classifiers. Results indicate that combining frequency-correlation features with a MLP yields an accurate (> 90 % correct) classification algorithm. Based on these promising results, a laboratory system was developed to recorded multi-view, broadband scatter from live, individual copepods and mysids. Results using frequency correlation features indicate that these features yield very good separation between classes with non-overlapping standard deviations computed from eight individuals per class.
Next, sound scatter data from live, individual fish were used to develop several kernel-machine-based multi-view fusion algorithms. Performance was quantitatively compared as a function of the number of available views, feature spaces, and classification problem type. A collaborative fusion algorithm performs better than the others without requiring any assumption about view geometry, the number of views, or the type of features.
Finally, multi-view fish size and orientation estimation was investigated under three different approaches. Results indicate that classification-based size estimation can be effective with a limited aperture and limited number of views. Model-based and image-reconstruction-based estimation show very good performance with full aperture data.
This thesis demonstrates that the multi-view, broadband approach offers significant advantages for marine animal classification, sizing, and orientation estimation.
|Advisor:||Jaffe, Jules S., Trivedi, Mohan M.|
|Commitee:||Hodgkiss, William S., Kreutz-Delgado, Kenneth, Kuperman, William A.|
|School:||University of California, San Diego|
|Department:||Electrical Engineering (Applied Ocean Science)|
|School Location:||United States -- California|
|Source:||DAI-B 70/05, Dissertation Abstracts International|
|Subjects:||Biological oceanography, Electrical engineering|
|Keywords:||Acoustic classification, Fish, Frequency correlation, Marine animals, Scattering, Zooplankton|
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