Dynamic gated fusion network with hierarchical multi-scale attention for hyperspectral image classification.

Hyperspectral image classification is a critical task in remote sensing, but existing methods often employ fixed feature fusion strategies, making it difficult to adapt to the data characteristics of different scenarios. Additionally, there is a lack of effective synergy between multi-scale feature extraction and attention mechanisms. To address this issue, this paper proposes a dynamic gated fusion network with hierarchical multi-scale attention (DGFNet). This method comprises three core modules: the multi-scale feature aggregator (MSFA), which uses a pyramid expansion convolution structure to concurrently extract spatial features with different receptive fields, achieving comprehensive scale coverage from local texture to global context; the enhanced channel-spatial attention (ECSA) module, which employs multi-pooling strategies and a cascaded structure to achieve deep interaction between channel and spatial attention, thereby adaptively enhancing discriminative features; and the dynamic gated fusion module, which learns input-related fusion weights to adaptively adjust the contribution ratios of multi-scale features and attention features based on data characteristics. Experimental results on four benchmark datasets-Pavia University, Houston, Indian Pines, and WHU-HongHu-show that DGFNet achieves overall accuracy rates of 96.91, 97.12, 94.05, and 94.46%, respectively, representing significant improvements over existing state-of-the-art methods. Ablation experiments thoroughly validate the effectiveness and necessity of each module. Additionally, this paper systematically compares five different fusion strategies (cross-attention, hierarchical fusion, parallel fusion, recurrent fusion, and sequential fusion). Experimental results demonstrate that dynamic gated fusion outperforms other fusion methods in terms of classification accuracy, computational efficiency, and model stability. This method provides an efficient, accurate, and robust solution for hyperspectral image classification. The code will be published on https://github.com/willianbilledu-alt/DGFNet .