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Journal of Applied Remote Sensing

Automatic target classification of man-made objects in synthetic aperture radar images using Gabor wavelet and neural network
Author(s): Perumal Vasuki; S. Mohamed Mansoor Roomi
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Paper 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.

Paper Details

Date Published: 23 January 2013
PDF: 13 pages
J. Appl. Remote Sens. 7(1) 073592 doi: 10.1117/1.JRS.7.073592
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Perumal Vasuki, Thiagarajar College of Engineering (India)
S. Mohamed Mansoor Roomi, Thiagarajar College of Engineering (India)


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