Browsing by Subject "Synthetic aperture radar (SAR) images"

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    Target detection and classification in SAR images using region covariance and co-difference
    (SPIE, 2009-04) Duman, Kaan; Eryıldırım, Abdulkadir; Çetin, A. Enis
    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.
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    Target detection in SAR images using codifference and directional filters
    (SPIE, 2010) Duman, Kaan; Çetin, A. Enis
    Target detection in SAR images using region covariance (RC) and codifference methods is shown to be accurate despite the high computational cost. The proposed method uses directional filters in order to decrease the search space. As a result the computational cost of the RC based algorithm significantly decreases. Images in MSTAR SAR database are first classified into several categories using directional filters (DFs). Target and clutter image features are extracted using RC and codifference methods in each class. The RC and codifference matrix features are compared using l 1 norm distance metric. Support vector machines which are trained using these matrices are also used in decision making. Simulation results are presented. © 2010 Copyright SPIE - The International Society for Optical Engineering.