Feature Fusion Network Model Based on Dual Attention Mechanism for Hyperspectral Image Classification

Hyperspectral images (HSIs) have been playing an important role in the field of ground object classification because of their rich spatial and spectral information. Aiming at how to extract complex feature information from HSIs, we propose a new feature fusion network based on dual attention (DAFFN) with dual attention mechanism, which is mainly used to capture more accurate global–local context attention features. The model extracts global context attention features using self-attention (SA) mechanism and local context attention features using cross-attention mechanism. Considering the problem that position information is easily lost during the conversion of attention mechanism, we propose a position self-calibration (PSC) module that can be flexibly embedded into two attention modules. In addition, to better integrate global and local features, we also designed a multiscale global and local feature fusion module (MSGL), which preserves more representative features with less communication costs by aggregating global and local attention features. We have carried out experiments on three commonly used hyperspectral datasets, and the classification results show that our model can achieve high classification accuracy even in the case of a limited number of samples.