提出双注意力网络提升高光谱图像超分辨率，兼顾局部与全局信息。
DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution
- 引入多头注意力与通道注意力，同时捕捉空间和光谱依赖关系。
- 在两个数据集上优于单一注意力机制，显著提升光谱保真度。
- 适合遥感图像处理、高光谱分析等需要精细光谱重建的场景。
二维卷积神经网络（CNN）在高光谱图像超分辨率任务中受到广泛关注。然而，其主要局限在于依赖局部邻域，缺乏全局上下文理解。此外，波段相关性与数据稀缺性持续限制性能表现。为此，本文提出一种双注意力卷积网络（DACN），用于高光谱图像超分辨率。首先，模型采用增强卷积，融合多头注意力以有效捕获局部与全局特征依赖。其次，分别生成通道与空间维度的注意力图，确定不同通道与空间位置的关注程度。此外，设计了结合L2正则化与空间-光谱梯度损失的自定义优化损失函数，以保证光谱保真度。在两个高光谱数据集上的实验结果表明，多头注意力与通道注意力的组合优于单独使用任一机制。
原文摘要 · Abstract (English)
2D convolutional neural networks (CNNs) have attracted significant attention for hyperspectral image super-resolution tasks. However, a key limitation is their reliance on local neighborhoods, which leads to a lack of global contextual understanding. Moreover, band correlation and data scarcity continue to limit their performance. To mitigate these issues, we introduce DACN, a dual-attention convolutional network for hyperspectral image super-resolution. Specifically, the model first employs augmented convolutions, integrating multi-head attention to effectively capture both local and global feature dependencies. Next, we infer separate attention maps for the channel and spatial dimensions to determine where to focus across different channels and spatial positions. Furthermore, a custom optimized loss function is proposed that combines L2 regularization with spatial-spectral gradient loss to ensure accurate spectral fidelity. Experimental results on two hyperspectral datasets demonstrate that the combination of multi-head attention and channel attention outperforms either attention mechanism used individually.
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