PolSAR Target Detection via Reflection Symmetry and a Wishart Classifier

Detection of man-made targets using polarimetric synthetic aperture radar (PolSAR) data has become a promising research area. The reflection symmetry is gradually being applied to man-made target detection algorithms as a physical property that can distinguish between man-made targets and natural clutter. However, the two terms related to the reflection symmetry property in the polarimetric coherency matrix, namely, the C
₁₂
and C
₂₃
terms, are not fully exploited by the traditional methods. To fully exploit the polarization information of the two terms, an image fusion strategy based on the position and scale information of the scale-invariant feature transform (SIFT) key points is proposed in this paper. Then, a new Wishart classifier based on the patch-level Wishart distance is used to realize automatic target detection of the fused image. The experimental results on measured data show that the proposed method can enhance the contrast between targets and clutter. In addition, the detection performance of the proposed method under different target-to-clutter ratios (TCRs) are verified on the synthetic data and measured data.