Nowadays, most of the hyperspectral image (HSI) fusion experiments are based on simulated datasets to compare different fusion methods. However, most of the spectral response functions and spatial downsampling functions used to create the simulated datasets are not entirely accurate, resulting in deviations in spatial and spectral features between the generated images for fusion and the real images for fusion. This reduces the credibility of the fusion algorithm, causing unfairness in the comparison between different algorithms and hindering the development of the field of hyperspectral image fusion. Therefore, we release a real HSI/MSI/PAN image dataset to promote the development of the field of hyperspectral image fusion. These three images are spatially registered, meaning fusion can be performed between HSI and MSI, HSI and PAN image, MSI and PAN image, as well as among HSI, MSI, and PAN image. This real dataset could be available at https://aistudio.baidu.com/datasetdetail/281612. The related code to process the data could be available at https://github.com/rs-lsl/CSSNet.

翻译：当前，大多数高光谱图像（HSI）融合实验基于模拟数据集来比较不同融合方法。然而，用于构建模拟数据集的光谱响应函数与空间降采样函数大多不够精确，导致生成的待融合图像与真实待融合图像在空间和光谱特征上存在偏差。这降低了融合算法的可信度，造成不同算法间比较的不公平性，阻碍了高光谱图像融合领域的发展。为此，我们发布了一个真实的HSI/MSI/PAN图像数据集以推动高光谱图像融合领域的进步。这三幅图像已完成空间配准，可在HSI与MSI、HSI与全色图像、MSI与全色图像之间进行融合，亦可实现HSI、MSI与全色图像三者间的融合。该真实数据集可通过 https://aistudio.baidu.com/datasetdetail/281612 获取。相关数据处理代码可通过 https://github.com/rs-lsl/CSSNet 获取。