A New Look at Image Fusion Methods from a Bayesian Perspective

Remote Sensing

Hankui K. Zhang¹  Bo Huang³  Gonzalo Pajares Martinsanz² 

[1] Geospatial Science Center of Excellence, South Dakota State University, Brookings, SD 57007, USA; E-Mail:;Geospatial Science Center of Excellence, South Dakota State University, Brookings, SD 57007, USA; E-Mail;Shenzhen Research Institute, The Chinese University of Hong Kong, Nanshan District, Shenzhen 518172, China; E-Mail:

关键词: pansharpening;    Bayesian data fusion;    quality tradeoff;    point spread function;    spectral consistency;   

DOI  :  10.3390/rs70606828

来源: mdpi

【 摘 要 】

Component substitution (CS) and multi-resolution analysis (MRA) are the two basic categories in the extended general image fusion (EGIF) framework for fusing panchromatic (Pan) and multispectral (MS) images. Despite of the method diversity, there are some unaddressed questions and contradictory conclusions about fusion. For example, is the spatial enhancement of CS methods better than MRA methods? Is spatial enhancement and spectral preservation competitive? How to achieve spectral consistency defined by Wald et al. in 1997? In their definition any synthetic image should be as identical as possible to the original image once degraded to its original resolution. To answer these questions, this research first finds out that all the CS and MRA methods can be derived from the Bayesian fusion method by adjusting a weight parameter to balance contributions from the spatial injection and spectral preservation models. The spectral preservation model assumes a Gaussian distribution of the desired high-resolution MS images, with the up-sampled low-resolution MS images comprising the mean value. The spatial injection model assumes a linear correlation between Pan and MS images. Thus the spatial enhancement depends on the weight parameter but is irrelevant of which category (i.e., MRA or CS) the method belongs to. This paper then adds a spectral consistency model in the Bayesian fusion framework to guarantee Wald’s spectral consistency with regard to arbitrary sensor point spread function. Although the spectral preservation in the EGIF methods is competitive to spatial enhancement, the Wald’s spectral consistency property is complementary with spatial enhancement. We conducted experiments on satellite images acquired by the QuickBird and WorldView-2 satellites to confirm our analysis, and found that the performance of the traditional EGIF methods improved significantly after adding the spectral consistency model.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.