Target detection and classification in SAR images using region covariance and co-difference
Çetin, A. Enis
Proceedings of SPIE - Algorithms for Synthetic Aperture Radar Imagery XVI
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In this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix whose entries are used in target detection and classification. In addition the region co-difference matrix is also introduced. Experimental results of object detection in MSTAR (moving and stationary target recognition) database are presented. The RC and region co-difference method delivers high detection accuracy and low false alarm rates. It is also experimentally observed that these methods produce better results than the commonly used principal component analysis (PCA) method when they are used with different distance metrics introduced. © 2009 SPIE.
KeywordsAutomatic target recognition (ATR) and classification
Principal component analysis (PCA)
Region co-difference matrix
Region covariance (RC)
Synthetic aperture radar (SAR) images
False alarm rate
Synthetic aperture radar images
Automatic target recognition
Principal component analysis
Synthetic aperture radar
Radar target recognition
Published Version (Please cite this version)http://dx.doi.org/10.1117/12.818725
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