Hyperspectral Image Classification Based on Multibranch Adaptive Feature Fusion Network

Convolutional neural networks (CNNs) are widely used in hyperspectral image classification (HSIC) due to their exceptional performance. However, current methods for multiscale feature extraction typically rely on single-branch CNNs, potentially causing interference among features of varying scales. To mitigate this issue, we present a multibranch adaptive feature fusion network (MBAFFN) classification method. MBAFFN enhances feature uniqueness and improves the accuracy and reliability of classification results by extracting information at multiple scales through three parallel branches. Furthermore, to address the challenge of capturing global features within CNNs, we introduce a global detail attention (GDA) mechanism aimed at bolstering the network's capability to capture comprehensive information. In addition, we mitigate the issue of neglecting center-pixel importance in convolution operations through a distance suppression attention (DSA) design. To effectively integrate outcomes from multiple branches, we propose a pixel-based adaptive feature fusion strategy, thereby increasing the proportion of features conducive to improved classification results. Lastly, auxiliary loss functions are employed to train the multibranch network. Experimental results on four benchmark datasets demonstrate the superiority of our approach over several state-of-the-art methods, particularly in managing imbalanced small samples. Furthermore, ablation studies validate the effectiveness of the proposed modules.