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Bayesian Data Fusion for Adaptable Image Pansharpening

机译：贝叶斯数据融合用于自适应图像全景锐化

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摘要

Currently, most optical Earth observation satellites carry both a panchromatic sensor and a set of lower spatialresolution multispectral sensors. In order to benefit from both sources of information, several pansharpening methods have been developed to produce a multispectral image at the spatial resolution of the panchromatic band. The aim of this paper is to suggest a novel approach to the pansharpening problem within a Bayesian framework. This Bayesian data fusion (BDF) method relies on statistical relationships between the various spectral bands and the panchromatic band without suffering from restricting modeling hypotheses. Furthermore, it allows the user to weight the spectral and panchromatic information with respect to either visual or quantitative criteria, which leads to adaptable results according to users'' needs and study areas. The performance of this approach was compared to existing methods based on markedly different subset images from very high spatial resolution IKONOS images. Results showed that BDF yielded the highest spectral consistency. Furthermore, small details were adequately added to the pansharpened images with little artifact as compared to those created using wavelet-based methods. Finally, the method was fast and easy to implement owing to its straightforward formulation. As it does not have any intrinsic limitations on the type of data to be processed or the number of bands to be merged, it also appears to be very promising for optical/SAR or hyperspectral image fusion.
机译：当前，大多数光学地球观测卫星都装有全色传感器和一组较低空间分辨率的多光谱传感器。为了从这两种信息源中受益，已经开发了几种全锐化方法以在全色波段的空间分辨率下产生多光谱图像。本文的目的是提出一种在贝叶斯框架内解决泛锐化问题的新颖方法。这种贝叶斯数据融合（BDF）方法依靠各种光谱带和全色带之间的统计关系，而不会受到建模假设的限制。此外，它允许用户根据视觉或定量标准对光谱和全色信息进行加权，从而根据用户的需求和研究领域得出适应性强的结果。将这种方法的性能与基于非常高的空间分辨率IKONOS图像明显不同的子集图像的现有方法进行了比较。结果表明，BDF产生了最高的光谱一致性。此外，与使用基于小波的方法创建的图像相比，这些图像中几乎没有任何伪影的细节就被适当地添加到了锐化图像中。最后，由于该方法的直接表达，因此该方法快速且易于实施。由于它对要处理的数据类型或要合并的波段数没有任何固有的限制，因此对于光学/ SAR或高光谱图像融合似乎也很有希望。

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