TARGET PRESCREENING BASED ON A QUADRATIC GAMMA-DISCRIMINATOR

This work presents the development, analysis and validation of a new t arget discrimination module for synthetic aperture radar (SAR) imagery based on an extension of gamma functions to 2-D. Using the two parame ter constant false-alarm rate (CFAR) stencil as a prototype, a new ste ncil based on 2-D gamma functions is used to estimate the intensity of the pixel under test and its surroundings. A quadratic discriminant f unction is created from these estimates, which is optimally adapted wi th least squares in a training set of representative clutter and targe t chips. This discriminator is called the quadratic gamma discriminato r (QGD). The combination of the CFAR and the QGD was tested in realist ic SAR environments and the results show a large improvement of the fa lse alarm rate with respect to the two-parameter CFAR, both with high resolution (1 ft) fully polarimetric: SAR and with one polarization, 1 In SAR data.