Three-Dimensional Frequency-Domain Transform Network for Cross-Scene Hyperspectral Image Classification

Reducing interdomain discrepancies effectively enhances the performance of hyperspectral cross-scene classification tasks. However, hyperspectral single-source domain (SD) generalization methods based on mining visual representation information are significantly influenced by interdomain discrepancies. Recent research has demonstrated that frequency-domain information exhibits robust stability. Therefore, this article proposes a three-dimensional frequency domain transform network (TFTnet) for achieving hyperspectral single-SD cross-scene classification tasks. To leverage the advantageous 3-D characteristics of hyperspectral images (HSIs), all frequency domain transforms are implemented within a 3-D framework. The model consists of a generator and a discriminator. The generator incorporates a frequency domain enhancement (FDE) module and a multisource information fusion (MIF) module; the discriminator incorporates a set of weight-sharing adaptive frequency domain transform (AFT) modules. The FDE module generates the extended domain (ED) with a certain domain shift by doing linear interpolation in the amplitude interval of a single SD itself. The MIF module integrates multisource information through interdomain attention, ensuring a balanced approach between the SD and ED, thus generating the effective balance domain (BD). The AFT module empowers the discriminator to selectively acquire HSI frequency domain features, facilitating synergistic collaboration of spatial-spectral features and frequency domain features for enhanced image comprehension. Extensive experiments on three public hyperspectral datasets show the superiority of the method compared with state-of-the-art techniques.