Physics-Guided Time-Interactive-Frequency Network for Cross-Domain Few-Shot Hyperspectral Image Classification.

Recently, domain alignment and metric-based few-shot learning (FSL) have been introduced into hyperspectral image classification (HSIC) to solve the issues of uneven data distribution and scarcity of annotated data faced in practical applications. However, existing cross-domain few-shot methods ignore pivotal frequency priors of the complex field, which contribute to better category discrimination and knowledge transfer. To address this issue, we propose a novel physics-guided time-interactive-frequency network (PTFNet) for cross-domain few-shot HSIC, enabling the extraction of both frequency priors and spatial features (termed "time domain" following Fourier convention) simultaneously through a lightweight time-interactive-frequency module (TiF-Module) as a pioneering effort. Meanwhile, a spectral Fourier-based augmentation module (SFA-Module) is designed to decouple the frequency priors and enhance the diversity of distribution of physical attributes to imitate the domain shift. Then, the physics consistency loss is introduced to regularize the diverse embeddings to approximate the center of each category's physical attributes, guiding the network to excavate more transferable knowledge of source domain (SD). Furthermore, to fully exploit the discriminant time-frequency information and further improve the accuracy of boundary pixels, a set of multiorientation homogeneous prototypes is adopted to represent each class comprehensively, and an intuitive and flexible uncertainty-rectified bidirectional random walk strategy is applied to replace the Euclidean metric for more reliable classification. The experimental results on four public datasets demonstrate the prominent performance of the proposed PTFNet.