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<sub>1</sub> norm distance metric. Support vector machines which are trained using these matrices
are also used in decision making. Simulation results are presented.
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.