Improving classification performance of sonar targets by applying general regression neural network with PCA
The remote detection of undersea mines in shallow waters using active sonar is a crucial subject required to maintain the security of important harbors and cost line areas. The discrimination sonar returns from mines and returns from rocks on the sea floor by human experts is usually difficult and v...
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| Published in | Expert systems with applications Vol. 35; no. 1; pp. 472 - 475 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.07.2008
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2007.07.021 |
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| Abstract | The remote detection of undersea mines in shallow waters using active sonar is a crucial subject required to maintain the security of important harbors and cost line areas. The discrimination sonar returns from mines and returns from rocks on the sea floor by human experts is usually difficult and very heavy workload. Neural network classifiers have been widely used in classification of complex sonar signals due to its adaptive and parallel processing ability. In this paper, due to the advantages on fast learning and convergence to the optimal regression surface as the number of samples becomes very large, general regression neural network (GRNN) has been used to solve the problem of classification underwater targets. Principal component analysis (PCA) has been established as a feature extraction method to improve classification performance. Receiver operating characteristic (ROC) analysis has been applied to the neural classifier to evaluate the sensitivity and specificity of diagnostic procedures. |
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| AbstractList | The remote detection of undersea mines in shallow waters using active sonar is a crucial subject required to maintain the security of important harbors and cost line areas. The discrimination sonar returns from mines and returns from rocks on the sea floor by human experts is usually difficult and very heavy workload. Neural network classifiers have been widely used in classification of complex sonar signals due to its adaptive and parallel processing ability. In this paper, due to the advantages on fast learning and convergence to the optimal regression surface as the number of samples becomes very large, general regression neural network (GRNN) has been used to solve the problem of classification underwater targets. Principal component analysis (PCA) has been established as a feature extraction method to improve classification performance. Receiver operating characteristic (ROC) analysis has been applied to the neural classifier to evaluate the sensitivity and specificity of diagnostic procedures. |
| Author | Yıldırım, Tülay Erkmen, Burcu |
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| Keywords | General regression neural networks Sonar target classification Receiver operating characteristic Principal component analysis |
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| References_xml | – reference: Polat, O., & Yildirim, T. (2006). Hand geometry identification without feature extraction by general regression neural network. Expert systems with applications. Article in press. Available online ( – reference: (pp. 316–320). Istanbul, Turkey, – volume: 1 start-page: 75 year: 1988 end-page: 89 ident: bib4 article-title: Analysis of hidden units in a layered network trained to classify sonar targets publication-title: Neural Networks – reference: Kusy, M., & Zajdel, R. (2005). Comparison of two radial function based models: support vector machine and neural network in classification of ovarian cancer data. – reference: 2 (pp.785–788). – reference: Chen, C. H. (1992). Neural networks for active sonar classification. Pattern recognition, vol. II. conference B: Pattern recognition methodology and systems. – reference: (pp. 201–206). – reference: (Vol. 3) (pp. 592–595). – reference: Song, W., & Shaowei, X. (1997). Robust PCA based on neural networks. – reference: Jing, Y., & El-Hawary, F. (1994). A multilayered ANN architecture for underwater target tracking. – reference: , (pp. 438 – 440). – volume: 6 start-page: 568 year: 1991 end-page: 576 ident: bib13 article-title: A general regression neural network publication-title: IEEE Transactions on Neural Networks – volume: 36 start-page: 1135 year: 1988 end-page: 1140 ident: bib3 article-title: Learned classification of sonar targets using a massively parallel network publication-title: IEEE Transactions on Acoustics, Speech, and Signal Processing – reference: (pp. 57–68). – reference: Soares-Filho, W., Manoel de Seixas, J.,& Pereira Caloba, L. (2001). Principal component analysis for classifying passive sonar signals. Circuits and systems, ISCAS 2001. – reference: Larkin, M. J. (1997). Optimal feature extraction techniques to improve classification performance, with application to sonar signals. Neural networks for signal processing VII. – reference: (pp.503–508). San Diego, California USA. – volume: vol. 345 start-page: 779 year: 2006 end-page: 784 ident: bib2 article-title: Conic section function neural networks for sonar target classification and performance evaluation using ROC analysis publication-title: Lecture notes in control and information sciences – reference: & – reference: Woods, K. S., & Bowyer, K. W. (1994). Generating ROC curves for artificial neural networks. Computer-based medical systems. – reference: (pp. 223–225). North Cyprus, Nicosia. – reference: Kapanoğlu, B., & Yıldırım T., (2004). Generalized regression neural networks for underwater target classification. – reference: (pp. 64–71). – reference: Shazeer, D. J., & Bello, M. G. (1991). Minehunting with multi-layer perceptrons. Neural networks for ocean engineering, 1991, – volume: 1 start-page: 395 year: 1992 end-page: 399 ident: bib15 article-title: Sonar target recognition using radial basis function networks. Singapore ICCS/ISITA ’92 publication-title: Communications on the Move – reference: ). |
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| Title | Improving classification performance of sonar targets by applying general regression neural network with PCA |
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