Cobalt crust recognition based on kernel Fisher discriminant analysis and genetic algorithm in reverberation environment

Recognition of substrates in cobalt crust mining areas can improve mining efficiency. Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area, a method based on multiple-feature sets is proposed. Features of the tar...

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Bibliographic Details
Published in:Journal of Central South University 2021, Vol.28 (1), p.179-193
Main Authors: Zhao, Hai-ming, Zhao, Xiang, Han, Feng-lin, Wang, Yan-li
Format: Article
Language:English
Subjects:
Cobalt
Discriminant analysis
Engineering
Feature extraction
Feature recognition
Genetic algorithms
Kernels
Linear prediction
Mathematical analysis
Matrix methods
Maxima
Metallic Materials
Ocean floor
Substrates
Wavelet analysis
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Summary:Recognition of substrates in cobalt crust mining areas can improve mining efficiency. Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area, a method based on multiple-feature sets is proposed. Features of the target echoes are extracted by linear prediction method and wavelet analysis methods, and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted. Meanwhile, the characteristic matrices of modulus maxima, sub-band energy and multi-resolution singular spectrum entropy are obtained, respectively. The resulting features are subsequently compressed by kernel Fisher discriminant analysis (KFDA), the output features are selected using genetic algorithm (GA) to obtain optimal feature subsets, and recognition results of classifier are chosen as genetic fitness function. The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent. The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.
ISSN:2095-2899
2227-5223
DOI:10.1007/s11771-021-4595-z