Scattering mechanism-guided zero-shot PolSAR target recognition

Abstract

In response to the challenges posed by the difficulty in obtaining polarimetric synthetic aperture radar (PolSAR) data for certain specific categories of targets, we present a zero-shot target recognition method for PolSAR images. Based on a generative model, the method leverages the unique characteristics of polarimetric SAR images and incorporates two key modules: the scattering characteristics-guided semantic embedding generation module (SE) and the polarization characteristics-guided distributional correction module (DC). The former ensures the stability of synthetic features for unseen classes by controlling scattering characteristics. At the same time, the latter enhances the quality of synthetic features by utilizing polarimetric features, thereby improving the accuracy of zero-shot recognition. The proposed method is evaluated on the GOTCHA dataset to assess its performance in recognizing unseen classes. The experiment results demonstrate that the proposed method achieves SOTA performance in zero-shot PolSAR target recognition (e.g., improving the recognition accuracy of unseen categories by nearly 20%). Our codes are available at https://github.com/chuyihuan/Zero-shot-PolSAR-target-recognition.