Journal of Software Engineering and Applications, 2012, 5, 163-166
doi:10.4236 /jsea.2 012.512b031 Published Online December 2012 (http://www.SciRP.org/journal/jsea)
163
A Study of Pansharpened Images Based on the HSI
Transformation App roac h
Yoshihiro Mitani1*, Yoshi hiko Hamamoto2
1Department of Intelligent System Engineering, Ube National College of Technology, Ube, Japan; 2Faculty of Engineering, Yama-
guchi University, Ub e, Japan.
Received 2012
ABSTRACT
A pan-sharpen technique artificially produces a high-resol ut i o n i mage b y i ma ge fu si on t echniq ue s us in g hig h -resolution
panchromatic and low-resolution multispectral images. Thus, the appearance of the color image can improve. In this
paper, the effectiveness of three pan-sharpening methods based on the HSI transform approach is investigated. Three
models are the hexcone, double hexcones, and Haydn’s approach. Furthermore, the e f fec t of s moo t hi ng t he low- resol u-
tion multispectral image is also investigated. The smoothing techniques are the Gaussian filter and the bilateral filter.
The experime ntal res ults sho w that Haydn’s mode l is super ior to o thers. The effective ness o f smoothi ng the lo w- reso-
lution multisp e c tral image is also shown.
Keywords: Multispectral Image; Pan-Sharpening; HSI Transformation; Smoothing Techniques
1. Introduction
Recently, there are many Earth observation satellites,
which usually provide both high-resol ution p a nchro mat ic
and lo w-resolution multispectral images. In remote sens-
ing, it is important to acquire high-resolution images. A
pan-sharpen technique artificially produces a high-reso-
lution image by fusing a high-resolution panchromatic
image and a low-resolution multispectral image [1]. Fig-
ure 1 ill ustrate s the outli ne of gener ating a pans harpened
image. By this pansharpening technique, the appearance
of the color image can improve. The pansharpening me-
thod is not limited to only a remote sensing image. If
there are both high-resolution panchromatic and low-
resolution multispectral images, the pansharpening ap-
proach is an adaptive technique for every image. There-
fore , t he fu ndament al stud y o f a p ans ha r pe nin g te c h nique
is believed to c ontribute to the develop ment of the image
processing techniques. J. Zhang reviews current tech-
niques of multi -source remote sensing data fusion [2]. In
[2], a considerable amount of effort has been devoted to
summarize the data fusion techniques of remote sensing
data. Moreover, the evaluation of pansharpening fusion
methods is reported [3]. However, there is little study of
a pan-sharpening method of HSI transformation models
[4,5]. Therefore, in this paper, the effectiveness of three
pan-sharpening methods based on the HSI transformation
approach is investigated. Three models are the hexcone,
double hexcones, and Haydn’s methods [6]. From the
experimental results, the HSI transformation approach
based on the Haydn's model works well. Furthermore,
the effectiveness of smoothing the low-resolution mul-
tispectral image before the HSI transformation is also
investigated. The smoothing methods are the use of the
Gaussian [4] and bilateral filters [7]. From the experi-
mental results, the pansharpened image is shown to be
improved by using the smoothing techniques of Gaussian
and bilatera l filters.
2. Pansharpened Images
A color is known to be expressed by various color fea-
ture models [4,6]. In this paper, we used the HSI family
of color model for making pansharpened images. The
HSI model is a color appearance system. It consis ts of hue,
saturation, and lightness (or intensity). The model based
on intensity, hue, and saturation is considered to be better
suited for huma n inter actio n. As t he a dva nta ge o f t he use
of the H SI mode l, ea ch o f hue , sa tur ati o n, a nd intensit y is
considered to be independent. In the studies of the pan-
sharpening method, the HSI transformation approach is
usually used. Figure 2 shows a flow of pan-sharpening
by the HSI transformation. We describe the pansharpened
images based on the HSI transformation approach as
follows: Suppose that there exist both the low-resolution
RGB color image and the high-resolution panchromatic
image. Firstly, each RGB element of the low-resolution
*Corresponding author.

A Study of Pansharpened Images Based on the HSI Transformation Approach
164
color image transforms into the HSI element, hue, saturation,
and intensity. Secondly, the gray value at the high-reso l u -
tion panchromatic image is replaced by the intensity
obtained from the HSI transformation at the low-resolution
color image. Finally, the pseudo high-resolution RGB
color image is generated by inverse transformation of the
HSI transformation. In this paper, we examine the
pansharpened images generated by three representative
HSI transformations. Three models are the hexcone,
double hexcones, and Haydn’s models [6]. Note that the
intensities (I) of the hexcone, double hexcones, and
Haydn’s models are defined by the brightness as I =
max{R,G,B}, I = (max{R,G,B}+min{R,G,B})/2, a nd I = R
+ G + B, respectively. The details of the HSI trans-
formation and inverse transformation are shown in the
image processing handbook [6].
Furthermore, in order to improve the pansharpened
images, we consider applying the smoothing of the low-
resolution multispectral image. The smoothing methods
are the use of the Gaussian and bilateral filters. The
smoothing technique is expected to use not only one at-
tention pixel but also the color information of its neigh-
bouring pixels. The Gaussia n filter is defined as follows:
( )( )
22
n wwm ww
22
22
n wwm ww
22
1 mn
fim, jnexp()
2πσ2σ
gi, j1 mn
exp( )
2πσ2σ
=−=−
=−=−
+
++ −
=+
−
∑∑
∑∑
(1)
where g(i,j) and f(i,j) denote the intensity of after and
before transformation at (i,j) coordinates, respectively.
The parameter
2
σ
is the spatial variance. The effect of
smoothing is determined by a value of σ. The image gets
more blurred as the value of σ is getting larger. On the
other hand, if the value of σ is small, the image is con-
sidered to get sharpen.
The bilateral filter [7] is considered to smooth the im-
age, while preserving the edge information of the image.
In smoothing an image, the bilateral filter takes into ac-
count of not only pixel difference but also intensity dif-
ference. The bilateral filter [7] is defined as Equation (2).
Here, the parameters
2
1
σ
and
2
2
σ
are the spatial and
intensity variance. The larger the value 2
1
σ or
2
2
σ
is,
the more an image gets smoothed. Otherwise, the image
gets sharpened. The role of
2
1
σ
is the same as the 2
σ
in the Gaussian filter. The effect of smoothing is deter-
mined by va lues of 1 σ and 2 σ.
( )
( )( )()
( )
( )() ( )
2
22
n wwm ww
22
12
2
22
n wwm ww
22
12
fi, jfim,jn
mn
fim, jnexp()exp()
2σ2σ
gi, jfi, jfim,jn
mn
exp( )exp()
2σ2σ
=−=−
=−=−
−++
+
++ −−
=
−++
+
−−
∑∑
∑∑
(2)
Figure 1 . Outl ine of generati ng a pansha rpened image.
Figure 2. Flow of pan-sharpening by the HS I tra nsformation.

A Study of Pansharpened Images Based on the HSI Transformation Approach
165
3. Experi m ents
In the e xperi ments, bo th the low-resolution color and the
high -resolution panchromatic images are generated from
an original image. Then, we use 3 types of a ratio. Fig-
ure 3 shows a ratio of the image resolution between the
multispectral and panchromatic images. It indicates that
one pixel of the multispectral image corresponds to two
by two pixels of the panchromatic image. This shows a
ratio 1:2. In the exper iment, we change the ratio: 1:2, 1:3,
and 1:4. It is fundamentally important to investigate the
influence s of the ratio on the pansharpened images. For
these images, we obtain the pansharpened images using
three types of HSI transformation techniques. The effect-
tive ness o f pans harpe ned i mages i s exa mined in ter ms o f
the RMSE value. The RMSE value is one of the effective
measures of the image quality performance. The RMSE
value is defined by
() ()
j 0Y1
i0X 122
2
1
RMSE( RrGg(Bb))
3XY
= −
= −
=−+ −+−
∑∑
(3)
where the RGB elements of the original image are R, G,
and B. On the other hand, the RGB elements of the pan-
sharpened image denote r, g, and b . The small er val ue of
the RMSE means that the pan-sharpening method works
better.
Table 1 is the RMSE values of three types of HSI
transformation approach for each of a ratio. From Table
1, the Haydn's model gives a smaller RMSE value at any
i mages and a ratio. The results also show that the double
hexcones type is usually superior to the hexcone one.
From the experimental result, we recommend to use the
Haydn’s model.
Figure 3 . A ratio of the imag e resoluti on between the multispectral and panchromatic images.
Table 1. RMSE values of th ree types of HSI t ransformation approach for ea ch of a ratio.
Ratio 1:2 1:3 1:4
Method Hexcone Double
Hexcone Haydn Hexcon e Double Hexcone Haydn Hexcone Double Hexcone H aydn
Aerial 16.68 7.49 6.87 16.87 8.72 8.47 17.02 9.28 9.26
Lenna 42.37 16. 80 8.75 42.52 1 7. 36 10.09 42.66 17.27 10.08
Mandrill 30.61 15.72 10.53 30.95 16.42 11.92 31.13 16.86 12.76
Table 2. RMSE values of the Gaussian, bilateral, and without filters using the Haydn's model for e ach of a r atio.
Ratio
1:2
1:3
1:4
Filter Gaussian Bilateral Without
filter
Gaussian Bilateral Without
filter
Gaussian Bilateral Without
filter
Aerial 6.64 6.64 6.87 7.90 7.86 8.47 8.78 8.78 9.26
Lenna 8.70 8.69 8.75 9.31 9.30 10.09 9.80 9.80 10.08
Mandrill 10.27 10.27 10. 53 11.30 11.29 11. 92 12.12 12.11 12.76
Table 3. Optimal parameter value which gives the minimum RMSE value.
Ratio
1:2
1:3
1:4
Filter Gaussian B i l at eral Gauss ian Bil at er al Gaus si an Bil ater al
,
,
,
Aerial 0.6 0.6, 80 0.8 0.8, 200 1.2 1.2, 200
Lenna 0.4 0.6, 100 0.8 0.8, 200 0.8 1.2, 300
Mandrill 0.6 0.6, 200 0.8 0.8, 300 1.2 1.8, 200

A Study of Pansharpened Images Based on the HSI Transformation Approach
166
From the results of Table 1, we focus on improving
the Ha ydn’s model. The effects of the parameter value σ
of the Gaussian filter a re investigated while changing the
values from 0.2 to 2.0. On the other hand, for the bilat-
eral filter, the value of 1 σ varies from 0.2 to 2.0, and
that of the
2
σ
is from 10 to 400. The size of the Gaus-
sian and bilateral filter is 3 x 3. Table 2 is the RMSE
values of the Gaussian, bilateral, and witho ut filters using
the Ha ydn’s model for each of a ratio. The results of the
Gaussian and bilateral filters show the minimum RMSE
value s. T he n, Table 3 s ho ws t he op t imal parameter value
which gives the minimum RMSE value. From Table 2,
the results of the Gaussian and bilateral filters outper-
form that of without filter at any images and a ratio . Fur-
thermore, the RMSE value of the bilateral filter is equal
to or slightly superior to that of the Gaussian filter. By
using the Gaussian or bilateral filters, the pansharpened
image can improve. This shows the effectiveness of
smoothing the low-resolution multispectral image. From
Table 3, the optimal parameter values of σ for Gaussian
filter and 1 σ for bilateral filter are comparatively very
small. This means that the low-resolution multispectral
image may be getting sharpen. And, its image quality is
considered to improve for making pansharpened images
by appropriately adjusting the parameter values.
4. Conclusion
In this paper, we have examined the effectiveness of
pansharpened images using three types of HSI transfor-
mation approach. Experimental results show that the
Haydn's model is a promising method in the limited
study. And the use of the Gaussian or bilateral filters for
the low-resolution multispectral image is shown to be
effective. Therefore, the Haydn’s model with image
smoo th in g te ch niques s uch as Ga us sia n o r b ilater al filters
for the low-resolution multispectral image should be used.
In the future study, the effectiveness of other panshar-
pening methods [3 ] will be investigate d.
REFERENCES
[1] Y. Zhang and G. Hong, “An IHS and Wavelet Integrated
Approach to Improve Pan-Sharpening Visual Quality of
Natural Colour IKONOS and QuickBird Images,” Info r-
mation Fusion, 6, 20 05, pp. 22 5-234.
doi:10.1016/j.inffus.2004.06.009
[2] J. Zhang, “Multi-Source Remote Sensing Data Fusion:
Status and Trends,” International Journal of Image and
Data Fusion, Vol. 1, N o. 1, 2010, pp. 5-24.
doi:10.1080/19479830903561035
[3] M. Ehlers, S. Klonus, P. J. Astrand and P. Rosso, “Mul-
ti-Sensor Image Fusion for Pansharpening in Remote
Sensing,” International Journal of Image and Data Fu-
sion, Vol. 1, No. 1, 20 10, pp. 25-45.
doi:10.1080/19479830903561985
[4] K. N. Plataniotis and A. N. Venetsanopoulos, “Color
Image Processing and Applications,” Springer, 2000.
[5] Y. Mitani and Y. Hamamoto, “A Consideration of
Pan-Sharpen Images by HSI Transformation Approach,”
Proceedings of the Society of Instrument and Control En-
gineers Annual Conference, 2010, pp. 1283 -1284.
[6] M. Takagi and H. Shimoda, “Handbook of Image Analy-
sis,” Revised Edition, University of Tokyo Press, 2004.
[7] C. To masi and Y. M anduchi, “Bilateral F ilterin g for Gray
and Color Images,” Proceedings of the 1998 IEEE Inter-
national Conference on Computer Vision, 1998, pp.
839-846.