Man-made object classification in SAR images using 2-D cepstrum
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2009-05
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Abstract
In this paper, a novel descriptive feature parameter extraction method from Synthetic Aperture Radar (SAR) images is proposed. The new method is based on the two-dimensional (2-D) real cepstrum. This novel 2-D cepstrum method is compared with principal component analysis (PCA) method by testing over the MSTAR image database. The extracted features are classified using Support Vector Machine (SVM). We demonstrate that discrimination of natural background (clutter) and man-made objects (metal objects) in SAR imagery is possible using the 2-D cepstrum feature parameters. In addition, the computational cost of the cepstrum method is lower than the PCA method. Experimental results are presented. ©2009 IEEE.
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IEEE National Radar Conference - Proceedings
Publisher
IEEE
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Cepstrum, Cepstrum method, Computational costs, Feature parameters, Image database, Man made objects, Natural backgrounds, PCA method, SAR imagery, SAR Images, Synthetic aperture radar images, Feature extraction, Image retrieval, Imaging systems, Metal recovery, Object recognition, Parameter extraction, Principal component analysis, Smelting, Support vector machines, Synthetic apertures, Synthetic aperture radar
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English