Lake bed classification using acoustic data
Applied Mathematics and Computer Science
By: Karen K. Yin, Xing Li, John Bonde, Carl Richards, and Gary Cholwek
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Abstract
As part of our effort to identify the lake bed surficial substrates using remote sensing data, this work designs pattern classifiers by multivariate statistical methods. Probability distribution of the preprocessed acoustic signal is analyzed first. A confidence region approach is then adopted to improve the design of the existing classifier. A technique for further isolation is proposed which minimizes the expected loss from misclassification. The devices constructed are applicable for real-time lake bed categorization. A mimimax approach is suggested to treat more general cases where the a priori probability distribution of the substrate types is unknown. Comparison of the suggested methods with the traditional likelihood ratio tests is discussed.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Lake bed classification using acoustic data |
| Series title | Applied Mathematics and Computer Science |
| Volume | 8 |
| Issue | 4 |
| Year Published | 1998 |
| Language | English |
| Contributing office(s) | Great Lakes Science Center |
| Description | p. 841-864 |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Applied Mathematics and Computer Science |
| First page | 841 |
| Last page | 864 |
| Google Analytic Metrics | Metrics page |