Title :
BP nets applied to ISAR object recognition
Author :
Xingbin Gao ; Yongtan Liu
Author_Institution :
Dept. of Radio Eng., Harbin Inst. of Technol., China
Abstract :
The performance of backpropagation (BP) neural classifiers for inverse synthetic aperture radar (ISAR) object recognition problems has been compared to that of a linear classifier and a nearest-neighbor classifier trained with the same data. The experimental results show that the error (misclassification) rate of the linear classifier is about twice that of the BP classifier, and the error rate of the BP classifier is about twice that of the nearest-neighbor classifier.<>
Keywords :
backpropagation; errors; image recognition; neural nets; pattern recognition; synthetic aperture radar; telecommunications computing; ISAR object recognition; backpropagation neural net classifiers; error rate; inverse synthetic aperture radar; linear classifier; misclassification rate; nearest-neighbor classifier; training; Aircraft; Arthritis; Continuous wavelet transforms; Decision support systems; Feature extraction; Filters; Frequency; Object recognition; Strontium; Testing;
Conference_Titel :
TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on
Conference_Location :
Beijing, China
Print_ISBN :
0-7803-1233-3
DOI :
10.1109/TENCON.1993.320139
