Evaluating the Robustness of Polarimetric Features: A Case Study of PolSAR Ship Detection

Polarimetric features, no matter how they are extracted, play a crucial role in synthetic aperture radar (SAR) image interpretation. Since recently developed deep neural network (DNN)-based methods are recognized as very vulnerable to some specifically designed perturbations, it raises an interesting question: are the traditional polarimetric features, calculated according to the scattering mechanism of SAR, still robust to the specially crafted perturbation? In this letter, we investigate the robustness of several traditional polarimetric features, which are widely used in the application of ship target detection, to a particularly designed perturbation, which we call polarimetric interference method (PIM). Specifically, we first calculate the gradients of traditional polarimetric features, respecting each of the polarimetric channels, and then formulate the PIM perturbation as an optimization problem, and finally, we give the PIM algorithm to perturb polarimetric features. Experiments are carried out on two polarimetric SAR (PolSAR) datasets to demonstrate the following: 1) traditional polarimetric features are vulnerable to the PIM perturbation, even if it is imperceptible in the SAR images; 2) the PIM perturbation can remarkably degrade the performance of the traditional polarimetric features in the task of ship detection, leading to more than 50% decrease in detection rate; and 3) the proposed perturbation is transferable, which means that the PIM interference regarding one type of the traditional polarimetric features also works in most cases for other types of polarimetric features.