Super resolution of multispectral images using locally adaptive models
Super resolution of multispectral images using locally adaptive models
复制标题
DOI：
10.1093/comjnl/bxn031
复制
发表时间：
2007-12
期刊：
影响因子：
--
通讯作者：
M. Vega;J. Mateos;R. Molina;A. Katsaggelos
中科院分区：
文献类型：
--
作者：
M. Vega;J. Mateos;R. Molina;A. Katsaggelos
在本文中，我们提出了一种用于多光谱图像全色锐化的局部自适应超分辨率贝叶斯方法。该方法结合了多光谱图像预期特征的先验局部知识，使用传感器特征对全色和多光谱图像的观测过程进行建模，并以超先验分布的形式包含模型中未知参数的信息。使用真实和合成数据，将全色锐化多光谱图像与其他部分锐化方法获得的图像进行比较，并定性和定量评估其质量。
In this paper we present a locally adaptive super resolution Bayesian methodology for pansharpening of multispectral images. The proposed method incorporates prior local knowledge on the expected characteristics of the multispectral images, uses the sensor characteristics to model the observation process of both panchromatic and multispectral images, and includes information on the unknown parameters in the model in the form of hyperprior distributions. Using real and synthetic data, the pansharpened multispectral images are compared with the images obtained by other parsharpening methods and their quality is assessed both qualitatively and quantitatively.