Wu Lei, Jiang Xunyan, Zhu Weihua, Huang Yulong, Liu Kai
College of Mathematics and Computer, Xinyu University, Xinyu, 338004, China.
School of Economics and Management, Xinyu University, Xinyu, 338004, China.
Sci Rep. 2024 May 21;14(1):11558. doi: 10.1038/s41598-024-60394-y.
Remote sensing image fusion is dedicated to obtain a high-resolution multispectral (HRMS) image without spatial or spectral distortion compared to the single source image. In this paper, a novel fusion algorithm based on Bayesian estimation for remote sensing images is proposed from the new perspective of risk decisions. In this study, an observation model based on Bayesian estimation for remote sensing image fusion is constructed. Three categories of probabilities including prior, conditional and posterior probabilities are calculated after an intensity-hue-saturation (IHS) transformation is applied to the original low-resolution MS image. To obtain the desired HRMS image, with the corrected posterior probability, a fusion rule based on Bayesian decisions is designed to estimate which pixels to select from the panchromatic (PAN) image and the intensity component of the MS image. The selected pixels constitute a new component that will participate in an IHS inverse transformation to yield the fused image. Extensive experiments were performed on the Pleiades, WorldView-3, and IKONOS datasets, and the results demonstrate the effectiveness of the proposed method.
遥感图像融合致力于获得一幅高分辨率多光谱（HRMS）图像，与单源图像相比，该图像不存在空间或光谱失真。本文从风险决策的新视角出发，提出了一种基于贝叶斯估计的新型遥感图像融合算法。在本研究中，构建了一种基于贝叶斯估计的遥感图像融合观测模型。在对原始低分辨率多光谱（MS）图像进行强度-色调-饱和度（IHS）变换后，计算包括先验概率、条件概率和后验概率在内的三类概率。为了获得所需的高分辨率多光谱图像，利用校正后的后验概率，设计了一种基于贝叶斯决策的融合规则，以估计从全色（PAN）图像和多光谱图像的强度分量中选择哪些像素。所选像素构成一个新的分量，该分量将参与IHS逆变换以生成融合图像。在昴宿星、WorldView-3和IKONOS数据集上进行了大量实验，结果证明了所提方法的有效性。
