Target Recognition for SAR Images Enhanced by Polarimetric Information

Target recognition for synthetic aperture radar (SAR) images has been a longstanding hotspot. However, using polarimetric information to enhance recognition performance is under-researched. In this article, we develop a visualization approach to analyze and highlight the contributions of polarimetric elements and propose a simple polarimetric correlation feature for target recognition. In the visualization method, a channel-wise convolutional structure is well developed, which serves as a proxy of the polarimetric elements. By using the gradients of the target class flowing into each developed convolutional channel with normalization, we obtain activation maps indicating the contribution of each polarimetric element. Then, a comprehensive quantitative evaluation of polarimetric element contribution is also performed. We demonstrate again that polarimetric information maintains significant advantages over single-polarization intensity, and the correlations between cross- and co-polarization emerge as key components for target recognition. Exactly inspired by these insights, the straightforward feature is defined as the correlation between the target and typical scatterers, seamlessly fusing intensities and correlations. This polarimetric correlation feature aptly encapsulates the physical scattering of the target, shows a clear mapping relationship with the geometric structure, and captures the differences between categories, achieving intracategory compactness and intercategory separation. Experimental results on both synthetic and real datasets underscore the effectiveness and superiority of this proposed feature.