Fourier-Based Spectral–Spatial Generator for Cross-Scene Hyperspectral Image Classification

Domain generalization (DG) has shown significant potential for cross-scene hyperspectral image (HSI) classification, wherein a model is trained exclusively on the source domain (SD) and can be directly transferred to an unseen target domain (TD). Current DG-based methods focus only on expanding the distribution of source domains by randomizing the style of the entire HSI cube. They fail to account for the domain shift problem caused by the variance of spatial land-cover distribution (context semantics), which results in SD-specific patterns being overly emphasized during training and, consequently, limiting the generalizability. Moreover, such randomization on cubes may introduce undesirable artifacts, such as blurring or distortion, leading to semantically compromised samples. In this paper, a Fourier-based spectral-spatial generator (FSSG) is proposed to generate diversified and robust generative domain (GD). Specifically, a Fourier disentanglement is developed to construct spectral expansion (SpeE) and spatial expansion (SpaE) from pixel-wise and region-wise levels, respectively. In SpeE, the style information is transmitted across pixels in a privacy-protecting way, i.e., SD shares the semantic information with the GD. In SpaE, an effective continuous frequency space interpolation is employed to transmit the styles and semantics information across cubes, which enables GD to bridge inter-domain gaps in both styles and context semantics. To further alleviate the over-emphasis on SD-specific patterns, a relaxation procedure is integrated within an adversarial training based on a coarse-to-fine paradigm, which facilitates the HSI cubes to gain more robust context semantics. Extensive experiments and analyses, conducted with two baseline methods across three public datasets, demonstrate the superiority of the proposed approach.