Abstract

Hyperspectral image (HSI) clustering assigns unlabeled pixels to land-cover groups by jointly exploiting spectral and spatial observations. Existing Vision Transformer-based deep clustering captures global dependencies through self-attention. However, the quadratic computational complexity of self-attention restricts practical applications in large HSI scenes. Furthermore, illumination variation and topographic shading shift spectral amplitude of co-class pixels toward divergent directions in feature space, enlarging intra-class distances and reducing inter-class separability in learned embeddings. To address the above limitations, we propose a self-supervised Spatial–Frequency Interaction and Amplitude–Phase Decoupling framework, termed SFI-APD, which integrates a High-Order Spatial–Frequency Interaction Module (HSFIM), a Frequency Feature Attention Block (FFAB), and a Frequency-Domain Vision Transformer (FreqViT) into a unified architecture. Specifically, HSFIM couples local convolutions with Fourier filtering to extract enriched spectral–spatial representations. FFAB then decouples amplitude and phase components to suppress brightness variations, yielding illumination-robust embeddings. Finally, FreqViT performs attention modulation across spectral channels, reducing token aggregation complexity from O(N2D) to O(NDlogN). On the Indian Pines, Salinas, Pavia University, and Yangzhou datasets, SFI-APD achieves OAs of 57.47%, 79.38%, 54.57%, and 64.11%, respectively, outperforming state-of-the-art self-supervised methods for large HSIs.

IPC Classification

Keywords