This paper extended the best tradeoff fast intensity-hue-saturation (BTFIHS) fusion to a more general model. Firstly,
based-on the methods proposed by previous researches, we integrated injecting strategy of spatial detail information into
the fusion model and also adopted the idea of BTFIHS to keep the intensity unchanged with the corresponding pixel in
panchromatic (PAN) image, while the hue and saturation of every pixel using BTFIHS were equal to them using tradeoff
fast intensity-hue-saturation (TFIHS). Then we got a general formula of the improved best tradeoff for high-resolution
image fusion based-on getting injecting parameter by spectral reflection function (SRF). At last, several experiments
were carried on to analysis and discuss the quality of the new methods compared with their originals. Results show that
the new method could yield a "true" high-resolution multispectral (MS) image, with vast improvement in blue band and
nearly 3.3% improvement in Q4 for all bands.
In this paper, we took into account both the spectral information and the spatial information and estimated how well the needed information contained within the multispectral (MS) and panchromatic (PAN) images was represented by the pan-sharpened image. Based on that, we proposed a new quality index which could be seen as an expanded index of the global quality measurement Q4. In our method, we first measured the spectral information preserving quality between the MS image and the fusion result. Then, we constructed a virtual spatial detail image considering the spatial resolution ratio between the source MS image and the PAN image, and also extracted the detail image contained in the merged image using the same technology, followed by a spatial information preserving quality index calculated from these two detail images. At last, we integrated the two indices by means of weighted addition determined by fusion model. To
illustrate the superiority of this new index, we took experiments on two pairs of ZY-2 PAN and ASTER MS (1 2 3 bands) remote sensing imageries, and adopted the tradeoff FIHS fusion method in which the tradeoff parameter was set to different values standing for different fusion models. After using the proposed index to assess the quality of fusion, we think that the new index is compliant with subjective evaluations and could therefore be used to compare different image
fusion or to find the best parameters for a given fusion model. Finally, we gave an experiential weight parameter of the
quality index while assessing the tradeoff FIHS fusion with images from these two sensors by the author's experiments.