Efficient Global Attention and Correlation-Aware Fusion for Hyperspectral Image Classification

Abstract

Hyperspectral imaging offers extensive spectral and spatial information. However, effectively utilizing this data for accurate classification remains a challenge. This study introduced the CASSX-Net, a novel framework designed to capture both short- and long-range dependencies in HSI data for land cover classification. The network combined a dual spectral-spatial feature extraction mechanism with a multi-head cross-attention module to leverage local and global feature interactions. By combining convolutional layers for short-range feature extraction with cross-attention mechanisms for long-range dependencies, the CASSX-Net addressed the intricate spectral-spatial correlations often missed by traditional CNNs. In addition, the maximal correlation fusion strategy optimally integrated the features from various pathways, improving the ability of the model to distinguish between classes with similar spectral signatures. The rigorous evaluation of four benchmark HSI datasets, including Pavia University, Pavia Centre, Salinas, and Houston 2018, demonstrated that the proposed framework consistently achieved the state-of-the-art performance, surpassing the existing methods in terms of classification accuracy and advancing the HSI land cover classification.