The purpose of the paper is to extract the region of interest (ROI) from the coarse detected synthetic
aperture radar (SAR) images and discriminate if the ROI contains a target or not, so as to eliminate the
false alarm, and prepare for the target recognition. The automatic target clustering is one of the most
difficult tasks in the SAR-image automatic target recognition system. The density-based spatial
clustering of applications with noise (DBSCAN) relies on a density-based notion of clusters which is
designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing,
which has many excellent features: only two insensitivity parameters (radius of neighborhood and
minimum number of points) are needed; clusters of arbitrary shapes which fit in with the coarse
detected SAR images can be discovered; and the calculation time and memory can be reduced. In the
multi-feature ROI discrimination scheme, we extract several target features which contain the geometry
features such as the area discriminator and Radon-transform based target profile discriminator, the
distribution characteristics such as the EFF discriminator, and the EM scattering property such as the PPR discriminator. The synthesized judgment effectively eliminates the false alarms.
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