Hyperspectral image super-resolution has attained widespread prominence to enhance the spatial resolution of hyperspectral images. However, convolution-based methods have encountered challenges in harnessing the global spatial-spectral information. The prevailing transformer-based methods have not adequately captured the long-range dependencies in both spectral and spatial dimensions. To alleviate this issue, we propose a novel cross-scope spatial-spectral Transformer (CST) to efficiently investigate long-range spatial and spectral similarities for single hyperspectral image super-resolution. Specifically, we devise cross-attention mechanisms in spatial and spectral dimensions to comprehensively model the long-range spatial-spectral characteristics. By integrating global information into the rectangle-window self-attention, we first design a cross-scope spatial self-attention to facilitate long-range spatial interactions. Then, by leveraging appropriately characteristic spatial-spectral features, we construct a cross-scope spectral self-attention to effectively capture the intrinsic correlations among global spectral bands. Finally, we elaborate a concise feed-forward neural network to enhance the feature representation capacity in the Transformer structure. Extensive experiments over three hyperspectral datasets demonstrate that the proposed CST is superior to other state-of-the-art methods both quantitatively and visually. The code is available at https://github.com/Tomchenshi/CST.git .

中文翻译：

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用于高光谱图像超分辨率的跨范围空间-光谱信息聚合


高光谱图像超分辨率在提高高光谱图像的空间分辨率方面得到了广泛的重视。然而，基于卷积的方法在利用全局空间光谱信息方面遇到了挑战。流行的基于 transformer 的方法没有充分捕捉频谱和空间维度的长期依赖关系。为了缓解这个问题，我们提出了一种新的跨范围空间光谱变压器 （CST） 来有效地研究单个高光谱图像超分辨率的长程空间和光谱相似性。具体来说，我们设计了空间和光谱维度的交叉注意力机制，以全面模拟长程时空光谱特性。通过将全局信息整合到矩形窗口自我注意中，我们首先设计了一个跨范围的空间自我注意，以促进远程空间交互。然后，通过利用适当特征的时空-光谱特征，构建跨范围光谱自注意力，以有效捕捉全球光谱波段之间的内在相关性。最后，我们精心设计了一个简洁的前馈神经网络，以增强 Transformer 结构中的特征表示能力。在三个高光谱数据集上的广泛实验表明，所提出的 CST 在定量和视觉上都优于其他最先进的方法。该代码可在 https://github.com/Tomchenshi/CST.git 上获得。