DDF-Net: A Dual-Branch Deep Feature Fusion Network for Few-Shot Hyperspectral Image Classification

In recent years, various deep learning frameworks have been introduced for hyperspectral image HSI classification. However, the proposed network models often exhibit high model complexity and fail to provide high classification accuracy when applied in few-shot learning scenarios. In this paper, we propose a Dual-Branch Deep Feature Fusion Network DDF-Net for few-shot hyperspectral image classification. DDF-Net extracts multi-layer features from hyperspectral images using a pre-trained CNN model and applies Principal Component Analysis PCA for dimensionality reduction. Subsequently, non-overlapping image patches are extracted from the reduced-dimensional features, and processed through two parallel streams: a 3D-CNN stream for spatial feature extraction and a CV-CNN stream for spectral feature extraction. Additionally, to enhance model performance, the Squeeze-and-Excitation SE mechanism is incorporated. Finally, the features from the two branches are effectively integrated through concatenation fusion and enhancement by the SE module, and then input into an SVM for classification. Experiments conducted on multiple datasets demonstrate the effectiveness and efficiency of DDF-Net in hyperspectral image classification, outperforming state-of-the-art methods.