The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part 87. Beijing 2008 
I = Y^ W i MS i (5) 
where MS', is the corresponding MS bands which covered with 
PAN band, and w, is the weights of band i. For IKONOS, MS, 
represent blue, green, red and near infrared band. For Landsat7 
(ETM+), MS, represent green, red and near infrared band. And 
for the other satellites, it can be referred to Table 1. 
(2) Modulation of the spatial detail 
Similar to GIF method proposed by Wang et al (2005), we 
introduce a modulation coefficient (a) to modulate the spatial 
detail. Then the method can extend traditional three-order 
transformations to an arbitrary order and all the MS bands could 
be fused at the same time. In this way, the equation (4) would 
be rewritten as: 
F(MS l )=MS k +a k -S=MS k +a t -(I mw -I) (6) 
where MS k represent the MS bands which will be fused, / is the 
intensity component that constructed according to (5), and 
a k =MS k /I in order to keep the added spatial detail 
proportional to their original values. 
Take IKONOS images as an example, the difference among the 
proposed method, the traditional IHS method, the fast IHS 
method (FIHS) and the fast IHS method with spectral 
adjustment (FIHS-SA) are shown in Table 2. In the proposed 
method, the sensor spectral response has been considered 
adequately and the spatial detail is injected into each band 
discriminatively. There are several ways to obtain the weight 
coefficients (Gonzâlez-Audicana et al,2006; Dou et al,2007). 
However, they only consider the nominal spectral responses 
which would be influenced by the on-orbit working conditions, 
atmospheric effects or postprocessing effects. In this paper, the 
PAN image is degraded to the same resolution as MS images by 
means of low-pass filtering and subsampling. Assumed that 
degraded PAN and MS bands satisfy the equation (3), a linear 
regression algorithm is performed in order to estimate the 
weight coefficients. Considering that there is a constant item 
{other) in equation (3), an adjustment of mean value is required 
to keep the global spectral balance. 
Fusion method 
Wj 
W>2 
VV? 
w 4 
a k 
IHS 
1/3 
1/3 
1/3 
0 
1 
FIHS 
1/4 
1/4 
1/4 
1/4 
1 
FIHS-SA 
1/12 
1/4 
1/3 
1/3 
1 
Proposed method 
w B 
w G 
w R 
W NIR 
MS/JI 
Table2. Comparisons of different fusion methods 
4. EXPERIMENTAL RESULTS 
To evaluate the performance and efficiency of the proposed 
method, experiments are carried out on IKONOS, Landsat 7 
ETM+ and EO-1 ALI images respectively. For the experiment 
on the fusion of IKONOS images, the original PAN and MS 
images are first atmospherically corrected and then spatially 
degraded to a resolution of 4 and 16 meter, respectively. The 
performance of the proposed method is compared together with 
traditional IHS method and three typical modified IHS methods 
(IIHS method proposed by Xiao 2003; FIHS method proposed 
by Tu,2004; IHS-WT method provided in ERDAS) both 
visually and quantitatively. Part of image is extracted to 
compare the visual effect of the fused images with reference 
image (the original MS image). From Figure.2, it can be easily 
seen that the fused image generated from traditional IHS 
method has obvious colour distortion. The spectral quality of 
FIHS fusion result has improved to some extent, but the colour 
of vegetation area in top right comer is still changed. The fused 
images generated from IIHS and IHS-WT methods keep good 
spectral quality, but the spatial quality of them are not as good 
as the other ones. The proposed method preserves almost all the 
spatial details and minimizes spectral distortion. The fused 
image generated from it is most similar to the reference image. 
To quantitatively assess the spectral and spatial quality of the 
fused images, some indices including bias, correlation 
coefficient (CC), spatial correlation coefficient (sCC,), and the 
universal image quality index (UIQI) are used. The bias refers 
to the difference between the means of the fused and reference 
images. The smaller the difference, the better the spectral 
quality is. The CC between the fused and reference image 
shows similarity between them. The sCC is proposed by Zhou 
et al (1998). In the procedure, the PAN and fused images are 
filtered with a Laplacian filter and the correlation coefficient 
between the filtered images is defined as sCC. The high 
correlation coefficients indicate that most of the spatial details 
are injected during the merging process. The UIQI indicates the 
spectral quality of the fused image (Wang et al,2002). The 
bigger the value of UIQI, the better the spectral quality is. From 
Table 2, we can find that except a litter smaller in CC index 
than IHS-WT method, the proposed method has superior 
performance than other methods in both the bias and UIQI 
index, which means the smallest spectral distortion. 
Furthermore, it is clear that the fused image from the proposed 
method has a similar sCC in comparison to those generated 
from IHS and FIHS method, which is much higher than those 
from IIHS and IHS-WT methods. To sum up, the proposed 
method has the best comprehensive performance. 
In addition, the experiment results of Landsat 7 ETM+ and EO- 
1 ALI images are shown in Figure 3 and Figure 4 respectively. 
For better comparison, a subset of images and the fused results 
are selected and displayed by using the same linear stretch 
method. From the picture, it is noticeable that the fused images 
from the traditional IHS method have obvious spectral 
distortion, such as the airport runway and water in Figure 3 and 
the vegetation area in Figure 4. Unlike the traditional IHS 
method, the improved IHS fusion method proposed in this paper 
generates fused images with both high spectral fidelity and high 
spatial resolution. Moreover, all the MS bands (IKONOS (1-4), 
ETM+ (1-5, 7)) have been fused at the same time by using the 
proposed method. 
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