Bridging 3-D and 2-D Convolution for Hyperspectral Images With Cross-Dimensional Spectral Attention

Hyperspectral imaging captures reflectance characteristics across hundreds of narrow spectral bands, enabling the joint acquisition of fine-grained spatial details and continuous spectral information. However, the inherently high dimensionality of hyperspectral data presents substantial challenges for feature extraction, necessitating effective modeling of complex spatial–spectral interactions while alleviating overfitting and computational inefficiency. To address these challenges, we propose the hybrid-dimensional hyperspectral network (HDHN), a novel framework that achieves a balance between efficiency and performance. HDHN employs a 3-D encoder with SSIR blocks to capture spatial–spectral representations and a 2-D decoder with SIR blocks to abstract semantic features and reconstruct spatial resolution. This design not only delivers strong performance but also reduces computational overhead and mitigates overfitting, offering a practical solution for hyperspectral image analysis. Moreover, we introduce the cross-dimensional spectral attention (CDSA) mechanism, which adaptively refines spectral representations at the pixel level. By dynamically allocating attention weights, CDSA emphasizes informative spectral bands, thereby enhancing spatial–spectral feature interactions and facilitating efficient encoder–decoder fusion. This leads to notable improvements in both feature representation and overall model performance. In addition, we present HDHN-e, a streamlined variant optimized for computational efficiency. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches on hyperspectral image classification and salient object detection tasks, while maintaining superior efficiency.