遥感影像融合的概述文章，2016年发表在information fusion，时间有点老，但是引用量很高。
1、pansharpening的概念
To collect more photons and maintain image Signal to Noise Ratio (SNR), the multispectral (MS) sensors (with high spectral resolution and narrow spectral bandwidth) have a larger Instantaneous Field Of View (IFOV) (i.e. larger pixel size and lower spatial resolution) compared to panchromatic (PAN) with a wide spectral band width and smaller IFOV (higher spatial resolution) sensors. With appropriate algorithms it is possible to combine these data and produce imagery with the best characteristics of both, namely high spatial and high spectral resolution. This process is known as a kind of multisensor data fusion and also called pansharpening.
2、hyperspectral pansharpening的难点
(1)Performing pansharpening with hyperspectral image is more complex than performing it with MS image. It is expected, because PAN and MS images are acquired almost in the same spectral range while the spectral range of a hyperspectral image is much wider than the one of the corresponding PAN im age.
(2)hyperspectral pansharpening 的一篇综述文章：[1] Loncan L , Almeida L , Bioucas-Dias J M , et al. Hyperspectral Pansharpening: A Review[J]. IEEE Geoscience and Remote Sensing Magazine, 2015.
3、图像融合的三个层次
（1）像素级融合
①The fusion algorithms at pixel level are generally divided into four classes: component substitution (CS), multiresolution analysis (MRA), hybrid methods (a combination of CS and MRA), and model based algorithms.
②Fusion process must satisfy three conditions : preservation of all relevant information, elimination of irrelevant information and noise, and minimization of artefacts and inconsistencies in the fused image.
③The changes of local details of images can be reflflected by the gradients.
④像素级融合方法的一些总结：
Generally, the main drawback of the pixel-level fusion rule is that the decision on whether a source image contributes to the fused image is made pixel by pixel and, this may cause spatial distortion in the merged image. In a source image, if one of its pixels contributes to the fused image, its neighbors are also likely to contribute to the fused image because the pixels in an image are spatially correlated. Therefore, the decision making during the first step of the fusion process should exploit the property of spatial correlation to improve fusion performance. The use of a window or region-based method is a straightforward approach to make use of spatial correlation.
（2）特征级融合
Feature level fusion methods deal with data at higher processing levels than pixel level methods. Normally, at fifirst, feature extraction procedures are applied. Then, the fusion process using advanced techniques takes place.
（3）决策级融合
Decision fusion (or interpretation level) is the highest processing level. It is the process of merging information from several individual data sources after each data source has undergone a preliminary classifification. In the decision level fusion, the received results from different local classififiers will be combined to determine the fifinal decision.
Some of useful decision fusion methods, applied in different applications, are voting , rank-based, Bayesian inference and Dempster-Shafer methods .
4、融合结果的评价
针对没有参考影像的融合结果评价方式：
The first approach uses the quality indexes that needs no reference image, but operates on the relationships among the original and pansharpened images. This approach directly operates on data at native scale, but, the defifinition of indexes biases the obtained results. The second approach considers the images at a spatial resolution lower than the original and considers the original MS image as a reference (Waldʼ s protocol). In the third approach, that needs no reference image, the approximations of MS and PAN images are obtained from the fused images. Then, the approximated MS is compared with the original MS and the approximated PAN is compared with the original PAN.
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