Complex-valued cross-domain few-shot learning network for PolSAR image classification
Yice Cao, Dayi Zhu, Zhenhua Wu, Jie Chen, Zhixiang Huang, Lixia Yang · IET conference proceedings. · 2024
Gathering reliable labeled samples for polarimetric synthetic aperture (PolSAR) image classification is laborious. Moreover, applying a trained classifier to new domains often leads to noticeable performance degradation due to domain disparities. Therefore, this paper proposes the novel complex-valued cross-domain (CD) few-shot learning classification (CCFSLC) method for PolSAR images to address these issues. Firstly, the transferrable knowledge learning module (TKLM) with a complex-valued feature encoder (CVFE) is trained using source data with sufficient labeled samples. Then, the deep few-shot learning module (DFSLM), constructed using the pre-trained CVFE, is trained by episodes in both source and target domains, with only minimal target labeled samples. Meanwhile, the adversarial domain adaptation module (ADAM) is employed to eliminate domain shift. The proposed CCFSLC mainly focuses on exploring discriminative information from raw PolSAR data, while reducing the domain gap to recognize novel categories in unseen domains with only a few annotated samples. Experiments on typical PolSAR datasets validate the effectiveness of the proposed method.