CFAR Detection of Targets in Fully Polarimetric SAR Images
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(Also cross-referenced as CAR-TR-696) Traditional constant false alarm rate (CFAR) detection algorithms produce a lot of false targets when applied to single-look, high-resolution, fully polarimetric synthetic aperture radar (SAR) images, due to the presence of speckle. We propose a two stag e CFAR detector followed by conditional dilation for detecting point and extended targets in polarimetric SAR images. In the first stage, possible targets are detected and false targets due to the speckle are removed by using global statistical parameters . In the second stage, the local statistical parameters are used to detect targets in regions adjacent to targets detected in the first stage. Conditional dilation is then performed to recover target pixels lost in second stage CFAR detection. The performance of a CFAR detector will be degraded if an incorrect statistical model is adopted and the data are correlated. A goodness-of-fit test is performed to decide the appropriate distribution and the effects of decorrelation of the data are cons idered. Good experimental results are obtained when our method is applied to single-look, highresolution, fully polarimetric SAR images acquired from MIT Lincoln Laboratory.