Differential Attention With Enhanced Squeeze-and-Excitation for Hyperspectral Image Classification

Hyperspectral imaging provides rich spectral-spatial information essential for fine-grained land cover classification. However, high dimensionality, spectral redundancy, and noise sensitivity significantly hinder classification accuracy. To overcome these issues, this work propose DIFF-SE, a novel differential transformer framework enhanced with a dual-path squeeze-and-excitation (E-SE) module tailored for hyperspectral image (HSI) classification (HSIC). The proposed multi-head differential attention mechanism contrasts paired attention maps to amplify discriminative spectral-spatial cues while suppressing redundancy and noise. Simultaneously, the E-SE module performs concurrent spectral and spatial recalibration, dynamically emphasizing informative bands and salient regions. Extensive experiments on three benchmark datasets, Pavia University (PU), WHU-Hi-HanChuan (HC), and OHID-1, demonstrate that DIFF-SE consistently achieves superior overall accuracies of 99.34%, 99.31%, and 94.99%, respectively, outperforming several recent state-of-the-art methods. The source code will be publicly released at https://github.com/mahmad000.