A Fast and Efficient Clutter Rejection Algorithm for SAR Imagery

A clutter rejection scheme is proposed for synthetic aperture radar (SAR) imagery based on two-stage two-dimensional principal component analysis (two-stage 2DPCA) followed by a bipolar eigenspace separation transformation (BEST) and a multilayer perceptron (MLP). For this, we have examined and analyzed four different algorithms. They are based on principal component analysis (PCA) both in one dimension (conventional PCA) and in two dimension with three different forms (2DPCA, alternative 2DPCA and two-stage 2DPCA), followed by BEST and MLP in each case. Feature extraction in different cases is carried out using respective PCA scheme. Each algorithm uses the BEST to further reduce dimensionality and enhance the generalization capability of the classifier. Classification between target chips and clutter chips is finally made through an MLP classifier. Experimental results on MSTAR public release database of SAR imagery are presented. Comparison of all the 2DPCA algorithms with an existing technique shows improvement both in performance and time. Moreover, all the 2DPCA algorithms compute eigenvectors and eigenvalues of image covariance matrix accurately and very efficiently with respect to time, and further reduces the effect of noise better than that in the case of PCA. Comparison reveals the fact that two-stage 2DPCA-based algorithm is the best (both in performance and time) between all algorithms.