Enhanced target recognition using optimum polarimetric SAR signatures

Abstract

We present a new method for automatic target/object classification by using the optimum polarimetric radar signatures of the targets/objects of interest. The use of optimum polarimetric signatures for enhancing target recognition using synthetic aperture radar is explored. The polarization scattering matrix is used for the derivation of target signatures at arbitrary transmit and receive polarizations (arbitrary polarization inclination angles and ellipticity angles). Then an optimization criterion that minimizes the within class distance and maximizes the between class metrics is used for the derivation of optimum sets of polarimetric signatures. Then from sets of real fully polarimetric SAR imagery arbitrary polarization attributes are extracted. The performance of the automatic target detection and recognition algorithms using optimum sets of polarimetric signatures are derived and compared with those associated with the non-optimum signatures. The results show that noticeable improvements can be achieved by using the SAR signatures obtained via optimum transmits and receives over non-optimum signatures. This work indicates that by optimally adjusting the radar polarization-by using polarization filters-the target classification performance can be improved and targets that may not be easily separable can be separated.