Improving classification performance of sonar targets by applying general regression neural network with PCA - Erkmen, Burcu; Yıldırım, Tülay (Expert systems with applications, 2008)
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.