A Center-Focused Transformer for hyperspectral image classification

In recent years, transformer-based methods have achieved remarkable progress in hyperspectral image classification (HSIC). However, they often rely heavily on extensive training samples to achieve optimal performance. Moreover, these methods frequently fail to adequately capture diverse local spectral–spatial correlations and multi-granular features inherent in hyperspectral images (HSIs). Crucially, existing approaches often overlook the pivotal role of the target center pixel. Their attention mechanisms tend to focus on irrelevant background regions, thereby reducing feature discriminability and degrading classification accuracy. To address these challenges, we propose a novel Center-Focused Transformer (CFT) framework that seamlessly integrates multi-scale spectral–spatial fusion for HSIC. Our framework comprises three key components. First, the Spectral–Spatial Fusion (SSF) mechanism integrates local and global dependencies by employing PCA alongside a Superpixel Graph Feature Extraction (SGFE) block. Second, the Multi-Granular Feature Enhancement (MGFE) approach strengthens spectral–spatial interactions through patch augmentation, a HybridConv block, and a Multi-Scale CBAM (MS-CBAM) block. Finally, the Focus Center Transformer (FCT) strategy explicitly emphasizes the importance of the central pixel for precise classification by incorporating Gaussian Positional Embedding (GPE) and cross-layer aggregation. Extensive experiments on four public datasets demonstrate that the proposed CFT consistently outperforms state-of-the-art methods, highlighting its potential for practical engineering applications.