SAR Image Target Classification: A Feature Fusion Approach

  • Rajamanickam S
  • Mohamed S
  • Roomi M
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Abstract

-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.

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APA

Rajamanickam, S., Mohamed, S., & Roomi, M. (2017). SAR Image Target Classification: A Feature Fusion Approach. IJCSN -International Journal of Computer Science and Network, 6(6), 689–693. Retrieved from http://ijcsn.org/IJCSN-2017/6-6/SAR-Image-Target-Classification-A-Feature-Fusion-Approach.pdf

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