Identifying and recognizing vehicles in Synthetic Aperture Radar (SAR) images are key for military application. This paper
presents a thorough exploratory work on SAR image target classification utilizing feature fusion strategy. The combination of
features is examined with respect to their classification accuracy. The test SAR image is processed by a SAR-BM3D filter to remove
speckle noise. Then the salient region of the image is extracted using context aware saliency detection model to detect the potential
regions of interest (ROI) which reduces the search space. The different texture characteristic values of GLCM are computed and twenty
geometrical features such as centroid, area are calculated for ROI. The features are cascaded and applied to a popular classifier such as
Support Vector Machines (SVM) and K-Nearest Neighbor (KNN). Experimental results shown on a MSTAR SAR imagery dataset for
three classes exhibit the superior performance of the proposed methods.
Published In:IJCSN Journal Volume 6, Issue 6
Date of Publication : December 2017
Pages : 689-693
Figures :05
Tables : 01
Sivaranjani Rajamanickam : Dept of ECE,Sethu Institute of Technology
Virudhunagar, India
S.Mohamed Mansoor Roomi : Dept of ECE, Thiagarjar College of Engineering
Madurai, India.
In this work, a feature fusion based classification
framework on MSTAR dataset for classifying military
target in SAR image is proposed. The proposed method
applies cascaded features such as GLCM texture feature
and shape features. These features are trained by a SVM
and KNN classifier. From the experimentation it is clearly
evident that fused feature based classification provides
better results compared to their individual performance.
At the same time SVM based classification provides better
result than KNN interms of classification accuracy and
true positive rate.
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