ABSTRACT Pansharpening refers to the fusion of panchromatic (Pan) image and multispectral (MS) image, which are acquired from the same scene. The output of the process is a high-spatial-resolution MS image. However, in most cases, spectral or spatial distortion occurs in the local region. To solve this problem, this paper introduces a detailed injection model, where estimates the model parameters recursively at the full-scale. The proposed method estimates detailed injection coefficients by finding minimum variance-unbiased (MVU) estimator. The superiority of the proposed method is verified via conducting several experiments on the five satellite datasets and comparing the results quantitatively and visually with several existing methods.

中文翻译：

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基于递归全尺度估计的光谱和空间信息改进多光谱图像

摘要 全色锐化是指从同一场景中获取的全色 (Pan) 图像和多光谱 (MS) 图像的融合。该过程的输出是高空间分辨率的 MS 图像。然而，在大多数情况下，局部区域会发生光谱或空间失真。为了解决这个问题，本文引入了一个详细的注入模型，其中在全尺度递归地估计模型参数。所提出的方法通过寻找最小方差无偏 (MVU) 估计量来估计详细的注入系数。通过对五个卫星数据集进行多次实验并将结果与​​几种现有方法进行定量和可视化比较，验证了所提出方法的优越性。