A Comparative Analysis of Attention Mechanisms in 3D CNN-Based Hyperspectral Image Super-Resolution

Hyperspectral Image Super-Resolution (HSI-SR) is a critical technique for enhancing the spatial resolution of hyperspectral images, enabling improved material identification and classification in applications such as remote sensing and environmental monitoring. While traditional methods struggle to balance spatial and spectral fidelity, recent advances in deep learning, particularly 3D Convolutional Neural Networks (3D-CNNs), have shown significant improvements. In this paper, we present a comparative analysis of four prominent attention mechanisms - Convolutional Block Attention Module (CBAM), Squeeze-and-Excitation (SE), Efficient Channel Attention (ECA), and Coordinate Attention (CA) - to evaluate their effectiveness in 3D CNN-based HSI-SR. We assess the performance of these mechanisms on the Pavia University and Botswana datasets using key metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Spectral Angle Mapper (SAM). Our results show that CA outperforms other mechanisms by providing superior spatial and spectral reconstruction while maintaining a relatively lower complexity. ECA demonstrates a good balance between performance and computational efficiency, making it suitable for real-time applications. SE and CBAM also offer significant improvements over the baseline, although they fall short of CA and ECA in overall performance. These findings provide valuable insights into selecting appropriate attention mechanisms for optimizing HSI-SR tasks.