23 January 2013 Automatic target classification of man-made objects in synthetic aperture radar images using Gabor wavelet and neural network
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Abstract
Processing of synthetic aperture radar (SAR) images has led to the development of automatic target classification approaches. These approaches help to classify individual and mass military ground vehicles. This work aims to develop an automatic target classification technique to classify military targets like truck/tank/armored car/cannon/bulldozer. The proposed method consists of three stages via preprocessing, feature extraction, and neural network (NN). The first stage removes speckle noise in a SAR image by the identified frost filter and enhances the image by histogram equalization. The second stage uses a Gabor wavelet to extract the image features. The third stage classifies the target by an NN classifier using image features. The proposed work performs better than its counterparts, like K-nearest neighbor (KNN). The proposed work performs better on databases like moving and stationary target acquisition and recognition against the earlier methods by KNN.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Perumal Vasuki, Perumal Vasuki, S. Mohamed Mansoor Roomi, S. Mohamed Mansoor Roomi, } "Automatic target classification of man-made objects in synthetic aperture radar images using Gabor wavelet and neural network," Journal of Applied Remote Sensing 7(1), 073592 (23 January 2013). https://doi.org/10.1117/1.JRS.7.073592 . Submission:
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