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International Journal of Image Processing and Visual Communication
ISSN (Online) 2319-1724 : Volume 5 , Issue 1 , April 2018
http://www.ijipvc.org/ijipvcv5i101.html 1
Pansharpening via Deep Guided Filtering Network Ahmad Alsmadi#, Shuyuan Yang*, Kai Zhang#
Dep. of Electrical Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China
Xi’an, China
[email protected], [email protected], [email protected]
Abstract— In order to enhance spatial details and reduce spectral
distortions in the fused image, in this paper, we introduce a simple
pan-sharpening way based on deep guided filtering network, our
proposed method was designed by combining the deep guided
filtering network with intensity hue saturation(IHS) transform.
First, by using the intensity component(I) which is calculated from
the up-sampled MS image via IHS transform; second, a deep
guided filtering network is constructed to filter the
panchromatic(PAN) image to extract a semantic detail map from
the panchromatic image; third, the details are injected into every
band of the up-sampled multi-spectral image to acquire the fused
image by an adaptive injection, which will aid in controlling the
quantity of the injected details. We empirically evaluated the
proposed method under a several data-sets from the GEO-2,
IKONOS- 2, and GF-2 satellites, and the results are compared
with those of its counterparts. The results demonstrate that the
proposed method can well-preserve spatial and spectral
information, in both subjective and objective aspects. The results
show that the proposed approach can acquire fusion results and
outperform the other methods in-indices of SAM, RMSE,
ERGAS, SAM, CC, UIQI and Q4.
Keywords— Pansharpening, intensity-hue-saturation (IHS)
transform, guided filtering network.
I. INTRODUCTION
The target of image fusion is to generate a single image by
combining relevant information from two or more images,
therefore, satellites-remote-sensing image fusion has been a hot
research topic of image fusion [1]. There are many commercial
satellites such as GEO-2, IKONOS-2, and GF-2, that provides
a high-spatial/low-spectral resolution panchromatic (PAN)
image and a high-spectral/low-spatial resolution multi-spectral
(MS) image concurrently. To recover this problem, there is an
effective way to combine a multi-spectral and a panchromatic
images, which is called remote-sensing image fusion, intended
for a result in a single image containing both a higher
spatial/spectral resolutions [2]. Today the fused remote sensing
image is a great significance technique and is used in providing
the imagery seen in the common Google-Maps/Earth and
Microsoft-Bing-Maps products. There are also some
applications within the field of remote-sensing that benefit from
pan-sharpened imagery, such as change-detection,
classification [3], vegetation identification, and lithology-
analysis [4].
In the last two decades, many fusion approaches have been
published to improve the spatial-resolution of the LR
multispectral image. The main goal of these approaches is to
inject the spatial-detail-information, which is extracted from
the panchromatic image into the multi-spectral image within a
predefined process, which is the injection gains. However, to
improve the spatial resolution of LRM band, there are two
common methods, which are the component-substitution (CS)
based method and the multi-resolution-analysis (MRA) based
method [5]. The component-substitution-based method is most
commonly used in remote-sensing image fusion, such as (IHS)
transform [6]-[7], Gram-Schmidt (GS) [8] and principal
component-analysis (PCA) [9]-[10]. The fusion images during
this method suffer from spectral distortions.
In the multi-resolution-analysis (MRA) based method, such as
the Laplacian-pyramid (LP) [11] and the “`a trous” wavelet
transform (ATWT) [12]. In order, the MRA based method can
provide more accurate spectral information but suffer from
spatial distortions. In the predefined process, the quantity of
panchromatic image spatial-detail-information that is injected
into the multi-spectral image is determined by the injection
gains, which affects the fusion result immediately [13].
Recently, Remote-Sensing-Image-Fusion Based on Adaptive
IHS and Multi-scale Guided Filter (AIHS) has been proposed
in [14]. The method in [14] takes advantage of the multi-scale
form of filtering, in order to extract sufficient details from the
panchromatic image. Hence, the obtained fused results through
this method are impressive. They used the panchromatic image
as the input image of a guided filter and the (I) component of
the multi-spectral image was used as the guided image at every
stage. Li et al. [15] proposed a multi-stage guided filter based
pan-sharpening method (MGF). They selected high-spatial
resolution panchromatic image as the guidance in transferring
the structures into the up-scaled MS images and smoothing the
PAN image itself. Qi et al. [16] also proposed multi-stage
guided filtering based on hyperspherical color transform
(HCS), they used the (I) component of the multi-spectral image
as the input of the guided filter, which was extracted from the
hyperspherical color transformation (HCT), and the PAN
image was used as the guidance-image at the first stage, but in
the second stage, at the same time, they made the PAN image
as the guidance and the filtering input. This method obtains the
fused image with a high-spatial-resolution, but the obvious
color distortions often occur in the sharpening process. Note
that, this method is more effective for WorldView-2 satellite
imagery, which utilizes more bands.
In this paper, we propose a novel pan-sharpening approach. By
using the guided filter, the approximation version of the
International Journal of Image Processing and Visual Communication
ISSN (Online) 2319-1724 : Volume 5 , Issue 1 , April 2018
http://www.ijipvc.org/ijipvcv5i101.html 2
panchromatic image is obtained, in which the input image is the
panchromatic image in the first stage of guided filter, and the
(I) component of the multi-spectral image is used as the
guidance image, because the (I) component change has a bit-
effect on the spectral-information and due to its ease of usage.
In the second stage, the opposite of the aforementioned steps
will be executed. Moreover, to control the detail information
injected into the multi-spectral image, an adaptive improved
injection gains procedure is designed. We employ a guided
filter to filter the panchromatic image and the (I) component of
the multispectral image to obtain the approximation low-
resolution image in the first stage and the second stage,
respectively. The guided filter calculates the filtering output by
taking into consideration the content of a guidance-image, as it
can transfer the structures of the guidance-image to the filtering
output [17]-[18]. We use a multi-stage strategy, in order to be
able to extract more detail information. Hence, the intensity
component is obtained by using linear weighting. Enhancing
the-efficiency of our method can be achieved by the improving
the injection gains that are being obtained from both of the
panchromatic and each band of the multi-spectral images.
Super resolution(SR) reconstruction was accomplished by
employing an iterative-back-projection (IBP), which is become
a known and computationally efficient method for improving
the spatial-resolution of the image [19]. However, IBP
algorithm has some presenting limitations, like ringing artifacts
in the strong edge-area of the image [20]. In this method, the
IBP is being used for each band in the MS image.
Our fusion method produces a higher spatial and spectral
resolution with minimum-spectral-distortion for the fused
image among of existing methods. The contributions of our
method are synthesis, in which the (I) component is obtained
from the upsampled MS image, a multi-stage guided filter
procedure to filter the panchromatic image, which extracts
more detail Information, as a semantic detail map is injected
into each band to obtain the fused image by a model-based
procedure, then, it makes an iterative back projection (IBP) for
each band. For both of the subjective and the objective
evaluations, the experimental results indicate that our method
has best results among of other existing methods.
The rest of this paper is structured into five sections. In Section
II, briefly reviews the guided filter. In Section III our proposed
fusion method is described. We apply our proposed method on
different data-sets and compare our fusion results with some
other existing method in Section IV. In Section V, a conclusion
is presented.
II. GUIDED FILTER
As we know, the edge preserving filters have been recently a
hot topic in image processing, which has received a great
attention from the research community. In order to avoid
ringing artifacts, there are some of edge preserving smoothing
filters such as guided filter [21], weighted-least-squares [22],
and bilateral-filter [23]. Currently, the fastest edge preserving
filter is the guided filter. It is useful for detail enhancement,
image matting/feathering, and dehazing. The image guided
filter function implements edge preserving smoothing on an
image, by using the content of a second image, which is being
used as a guided image, in order to effect the filtering.
Furthermore, the guided image could be the same image. Like
other filtering processes, the guided filter is a neighborhood
process, but takes consideration while computing the value of
the output pixel; the statistics of a region in the corresponding
spatial neighborhood in the guidance-image [24]. The guided
filter can avoid gradient reversal artifacts. In this paper, the
guided filter is employed for pan-sharpening. It is worth to
mention; the guided filter is a local linear model between
filtering output image 𝑂 and the guidance image 𝑌 .
Theoretically, by using a guided image 𝑌 in filtering the input
image I, in order to get the output image 𝑂 , moreover, the
output image 𝑂 has the structures of the guidance-image, and it
can maintain the main information of the input image.
Assuming that 𝑂 is a linear transform of 𝑌 in a window 𝑤𝑘
centered at a pixel 𝑘 , it can be calculated by the following
equation:
𝑂𝑖 = 𝑎𝑘𝑌𝑖 + 𝑏𝑘∀𝑖 ∈ 𝑤𝑘 (1)
where 𝑖 is the pixel index, and 𝑤𝑘 is a squared window of size
(2r +1) × (2r +1). The following equation will be used to
represent the guided filtering operation for this paper:
𝑂 = 𝐺𝑟,𝜁(𝐼, 𝑌) (2)
where 𝐺 denotes the guided filter function, the parameters 𝑟
and 𝜁 are the inputs of guided filter, which are determined the
size and the blur degree of the guided filter, respectively. Note
that, 𝐼 and 𝑌 are the input and the guidance images,
respectively.
III. PROPOSED METHOD
The proposed pan-sharpening method is shown in Fig. 1. It is
based on the component-substitution (CS) method and the
multiresolution-analysis (MRA) method, which use the popular
edge-preserving guided filter and iterative back projection
(IBP) in injecting the extracted spatial detail information from
the PAN image into the up-sampled MS image. Our method
includes two main steps: Step1) using the PAN image to extract
the semantic detail map; Step2) computing the injection gain
for each band.
A. Extracting a semantic detail map from the panchromatic
Image
In order to acquire the spatial-information, the conventional
methods estimate the variation between the panchromatic
image and the intensity component of multi-spectral image or
any type of a low-pass filtered panchromatic image. However,
these methods suffer from the spectral-distortion because the
spatial information will blend with low-frequency components
[22]. We proposed a simple way to extract the semantic detailed
International Journal of Image Processing and Visual Communication
ISSN (Online) 2319-1724 : Volume 5 , Issue 1 , April 2018
http://www.ijipvc.org/ijipvcv5i101.html 3
map by estimating the variation between the panchromatic
image and the multi-stage guided filter, which aids in
overcoming the aforementioned problem. The approximation
image of the input image is the result of the guided filter.
Hence, the semantic detailed map of the input image is the
variation between the input image and the approximation
image. In this paper, we present a multi-stage guided filter, in
which the I component is used as the guidance-image and the
panchromatic image as the input image in the first stage (s=1)
of guided filter. In the other stages of our proposed method, the
panchromatic image is used as the guidance-image and the
result of previous stage is used as the input image. The
procedure of the first stage can be formulated as follows:
𝐺𝐹(𝑃𝐴𝑁) = 𝐺𝑟, 𝜁(𝑃𝐴𝑁, 𝐼) (3)
Fig. 1 Flowchart of our proposed method.
where 𝐺𝐹 denotes the guided filter output. However, for 𝑠𝑡ℎ
stage (𝑠 > 1) , the procedure of the guided filter can be
formulated as
𝐺𝐹𝑠(𝑃𝐴𝑁) = 𝐺𝑟, 𝜁(𝑃𝐴𝑁(𝑠−1), 𝑃𝐴𝑁) (4)
where 𝑃𝐴𝑁(𝑠−1) is the approximation layer and (𝑠 − 1)𝑡ℎ
represents the index level of guided filtered output. The spatial
detail 𝑃𝐴𝑁𝐷𝑠 for the 𝑠𝑡ℎ stage can be shown as
𝑃𝐴𝑁𝐷𝑠 = 𝐺𝐹(𝑠−1)(𝑃𝐴𝑁) − 𝐺𝐹𝑠(𝑃𝐴𝑁) (5)
Note that, when s=1 the original PAN image is 𝐺𝐹(𝑠−1)(𝑃𝐴𝑁).
B. Computing the injection gain for each band The injection gains are defined by the quantity of the
panchromatic image spatial detail information, which is being
injected into the multi-spectral image. Consequently, the
injection gain has been shown to be critical in the spatial detail
injection model. Therefore, if the quantity of the spatial detail
is insufficient, the fused image will not reach its desired limits
and if the quantity of the spatial detail was very high, the fused
result will suffer from spectral distortion, which will cause
redundant information. However, each band in MS image has
different characteristics, cause the variety of the spectral
reflectance, therefore, each band has different trade-off
parameter β.
Accordingly, in [14], the trade-off parameters can be generated
adaptively, by resolving the next optimization problem:
min𝛽1,...,𝛽𝑛
∥ 𝑤𝑝 − ∑𝑛𝑖=1 𝛽𝑊𝑀𝑖 ∥2 𝑠. 𝑡. 𝛽1 ≥ 0, . . . , 𝛽𝑛 ≥ 0 (6)
where 𝑛 represents the number of the multi-spectral band and
𝛽𝑖 represents the 𝑖𝑡ℎ band trade-off parameter of the MS
image’s. Thenceforward, we can acquire the improved edge-
detecting-weighting-matrix as
𝑔𝑖 =𝑀𝑖
1/𝑛 ∑𝑛𝑖=1 𝑀𝑖
((1 − 𝛽𝑖)𝑤𝑝 + 𝛽𝑖𝑤𝑀𝑖) (7)
where 𝑤𝐴 represents the edge-detecting-weighting-matrix of
image A
𝑤𝐴 = 𝑒𝑥𝑝(−𝜆
|∇𝐴|4+𝜀) (8)
where ∇𝐴 denotes a gradient of the image A and 𝜆 and 𝜀 are the
tuning parameters. Fig. 2 illustrates the following procedure
obtaining the injection gain.
International Journal of Image Processing and Visual Communication
ISSN (Online) 2319-1724 : Volume 5 , Issue 1 , April 2018
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Fig. 2 The whole procedure of obtaining the injection gain.
Now to obtain the 𝐹 according to Fig. 1, the total semantic
details of the panchromatic image will inject into the up-
sampled multi-spectral image as formulated in Equation (9):
𝐹𝑖 = 𝑀𝑖 + 𝑔𝑖 ∑𝑘𝑠=1 𝑃𝐴𝑁𝐷
𝑠 (9)
where 𝐾 is the number of stages for the guided filter.
Then using IBP for each MS band by considering 𝐹𝑖 as LR
image and the up-sampled MS image as HR image to improve
the spatial-resolution of the fusion result. To improve more
spatial detail, we present multiple filter stages, but using an
excessive filter stage will suffer from some problems in the
fused image such as redundant information and produce
artifacts while increasing the computational cost. Fig. 3, shows
the fusion results of GEO-2 images with different guided filter
stages. The normalized results were obtained with respect to the
different filter stage as shown in Fig. 5. According to Fig. 5, the
best-fused results can be seen in stages 1 and 2.
Fig. 3 Illustration fusion results of GEO-2 images with different guided filter
stages. Figures (a) - (d) denote the filter stages 1 to 4, respectively.
IV. EXPERIMENTAL STUDY AND ANALYSIS
To evaluate the performance of our method, we consider
Wald’s protocol, which states that a fused image should be have
a close similarity to the image that the corresponding sensor
would observe at the highest spatial resolution [25]. We are
selected the original multi-spectral images as the true images to
be compared with the fusion results, therefore, the experiments
are accomplished on the degraded images. The parameter
settings, which is used in the proposed method are discussed.
To show the behavior of the proposed fusion method three data
sets i.e., IKONOS-2, GEO-2, and GF-2 are used
experimentally. After the subjective evaluation should be
appraised the performance of each fusion method
quantitatively. Hence, six typical evaluation metrics, namely,
the correlation coefficient (CC), the root mean square-error
(RMSE), universal-image-quality indexes (UIQI), the erreur-
relative-global-adimensionnelle de synth`ese (ERGAS), a
quaternion-based coefficient (Q4) index, and the spectral angle-
mapper (SAM), are used.
A. Parameters settings
The inputs of the guided filter are critical factors that must be
obtained. Moreover, an excessive number of iterations of IBP
may lose some part from the fused image. Note that, in our
proposed method, the Num-of-iterations is fixed to 5. The
experiments in this section are performed on the GEO-2 images.
Fig. 4. shows the normalized results with respect to different
input parameters of the guided filter, which are the square
window of radius r and the regularization parameter 𝜁. There
are three cases of interest: a) r=2 and 𝜁 = 0. 12; b) r=4 and 𝜁 =0. 22; c) r=8 and 𝜁 = 0. 42. Note that, the quality indicators are
calculated and some indexes are normalized to [0 1]. The better-
fused results have larger CC, UIQI, and 𝑄4, and smaller RMSE,
SAM, and ERGAS. Case (a) is shown in Fig. 4, where the fused
result achieves the optimal effect. Note that, the authors in [15]
fixed the parameters r=2 and 𝜁 = 0.001,0.8 in the first and the
second stage, respectively. Moreover, the authors in [16] fixed
the parameters r=4 and 𝜁 = 0.1 × 𝑛𝑏𝑖𝑡𝑠 , where nbits is the
radiometric resolution of each image.
Fig. 4 Performance of our fusion method with respect to different input
guided filter parameters.
In this paper, the rest of parameters are set as follows, the multi-
spectral image is matched to the same size of the panchromatic
International Journal of Image Processing and Visual Communication
ISSN (Online) 2319-1724 : Volume 5 , Issue 1 , April 2018
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image, the parameters 𝜆 = 10−9 and 𝜀 = 10−9 are set as in
[14].
We compared our proposed fusion method with several typical
fusion methods: the AIHS method [14]; the (MGF) method [15];
the (HCS) method [16].
B. Experiments with IKONOS-2 Data
The IKONOS-2 system simultaneously offers a four-band MS
image with 4m resolution and a single-band PAN image with
1m resolution. The results for IKONOS-2 are shown in Fig. 6.
The size of the PAN image and the MS image in this
experiment are 2048 × 2048 and 512 × 512, respectively. In
this study, by using the protocol in [25], we degrade the
original-MS and PAN images by a factor of 4, then we utilize a
simulated IKONOS-2 LR-MS image with the size of 128 × 128
and a corresponding panchromatic image with the size of 512
× 512. The original multi-spectral image with 4m resolution is
used as the true-image shown in Fig. 6 (a), in order to perform
comparative studies between the true-image and the fusion
result.
Fig. 5 Performance of our fusion method with respect to different guided filter stages.
The results of the different fusion methods are shown in Fig.
6(b)-(f). We cut a sub-image, which is located at the right-
bottom corner of the fused image, in order to demonstrate the
fusion results more clearly. Visually, spectral distortions from
MGF method (i.e. shown in Fig. 6(b)) are obvious as color
changes severely in the red corner. The fused result obtained by
the HCS method suffers from serious spectral distortion when
compared with all the other methods. The AIHS method has
improved, but the effect is not significant. To sum up, our
method outperforms the spectral and the spatial resolution. The
quantitative assessment indices results are listed in Table I,
where the best results of every quality indices are clearly
noticeable by font bold. Here, it can be clearly observed that
our method performs the superior results in the six evaluation
indexes when compared to the other methods.
C. Experiments with GEO-2 Data
In this section, we analyze another group of experimental
results of image fusion with GEO-2 data to further clarify the
performance of our fusion approach. The GEO-2 data set
provides a four band 2.8m resolution multi-spectral image and
a 0.7m resolution PAN image. The results for GEO-2 are
presented in Fig. 7. The size of the panchromatic and the multi-
spectral images in this experiment are 512 × 512 and 512 ×
512, respectively. In this study, by using the protocol in [25],
we degrade the original multi-spectral image by a factor of 4,
then we utilize a simulated GEO-2 LR-MS image with the size
of 64 × 64 and a corresponding PAN image with the size of
512 × 512. Fig. 7 (a) the original multi-spectral image with a
2.8m resolution which is used as the true-image to compare
between the fusion result.
The results of the different fusion methods are shown in Fig. 7
(b)-(f). Visually, the MGF method obtains MS images with
high-spatial-resolution, but the obvious color distortions occur
in the playground. Spectral distortions from HCS method (i.e.
shown in Fig. 7(c)) are clearly shown as color changes in the
high reflection areas, such as, the green areas.
Fig. 6 (a) Original Multi-spectral image. (b) MGF approach. (c) HCS
approach. (d) AIHS approach. (e) First stage proposed method. (f) Second stage proposed method.
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TABLE I
RESULTS COMPARISON OF OUR PROPOSED METHOD WITH
SOME EXISTING APPROACHES ON IKONOS-2 DATA-SET
The AIHS method has improved, but, it changes the color of
some parts of the fused image when compared with the
reference-image such as the playground. Fig. 7(e)-(f) verify that
the proposed method results are more similar to the reference-
image than the results of the other methods. The numerical
experiments of the fused methods in Fig. 7 are listed in Table
II. From Table II, it can be clearly observed that our fusion
results have the best values for most of the quality indices.
D. Experiments with GF-2 Data
In this part, we study and analyze the experimental-results on
GF-2 data set. The GF-2 satellite captures 4m multi-spectral
images, i.e., red, green, blue, NIR images, and 1m
panchromatic image. In this experiment, the size of the
panchromatic and the multi-spectral images are 1024 × 1024
and 256 × 256, respectively. As in the IKONOS-2 experiment,
we spatially degrade the GF-2 data, then we utilize a simulated
GF-2 LR multi-spectral image with the size of 64 × 64 and a
corresponding panchromatic image with the size of 256 × 256.
The results for GF-2 are presented in Fig. 8. The original-MS
image with a 4m resolution is used as the true-image, in order
to compare it with the fusion result is shown in Fig. 8(a).
The fusion results are compared with the original multi-spectral
image, in order to obtain the desired objective measures, as
shown in Fig. 8(b)-(f). By comparing the fusion results with the
reference multi-spectral image visually, it can be observed that
the fusion result of the HCS method suffers from a high-
spectral-distortion. However, the spatial details in some areas
Fig. 7 (a) Original Multi-spectral image. (b) MGF approach. (c) HCS
approach. (d) AIHS approach. (e) First stage proposed method. (f) Second
stage proposed method.
are lost. The result obtained from the MGF method suffers from
spectral distortion while losing some spatial details. As shown
in Fig. 8(d), AIHS performs the fused result with well preserved
spectral and spatial details. To sum up, our proposed method
results is a much better result and a more similar image to the
reference-image compared with the other methods.
Method CC RMSE ERGAS SAM UIQI Q4
MGF 0.9396 0.9168 0.9168 0.9422 0.9366 0.9429
HCS 21.9473 27.8220 25.6520 21.3698 22.7985 21.4905
AIHS 0.7502 0.7223 0.7368 0.7570 0.7558 0.7661
Proposed
𝟏𝒔𝒕 stage
0.9495 19.9996 2.9292 5.7394 0.9465 0.7728
Proposed
𝟐𝒏𝒅 stage
0.9476 20.4610 2.9958 5.7643 0.9449 0.7731
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Fig. 8 (a) Original Multi-spectral image. (b) MGF approach. (c) HCS
approach. (d) AIHS approach. (e) First stage proposed method. (f) Second
stage proposed method.
Table III reports the quantitative assessments indexes
calculated for GF-2 data set. From Table III, it can be clearly
observed that our method outperforms all the other methods.
V. CONCLUSION
In this paper, a novel image fusion method based on an IHS
injection model and a multi-stage guided filter is presented. In
the first stage of the guided filter we used the 𝐼 component,
which is acquired from the up-sampled multi-spectral image as
the input image and the panchromatic image as the guidance-
image, but in the second stage we used the panchromatic image
as the guidance-image and the result of the first stage as the
guidance-image, then the total semantic details map, which are
extracted by the multi-stage guided filter and were injected into
the up-sampled MS-image. Our contribution here is threefold.
Firstly, the 𝐼 component is acquired from the up-sampled MS-
image. Secondly, a multi-stage guided filter strategy was used
in filtering the panchromatic image, in order to acquire more
detail information. Third, the total semantic details map is
injected into every band of the up-sampled multi-spectral image
to acquire the fusion result, then, it makes an iterative back
projection (IBP) for each band. Our method is tested on three
degraded data-sets i.e., IKONOS-2, GEO-2, and GF-2, and
compared with three existing methods. The empirical-results
show that our fusion method is more robust in an image-
registration among of the other methods. Furthermore, the
proposed method is highly-effective among of the other
methods.
TABLE II
RESULTS COMPARISON OF OUR PROPOSED METHOD WITH
SOME EXISTING APPROACHES ON GEO-2 DATA-SET
TABLE III
RESULTS COMPARISON OF OUR PROPOSED METHOD WITH
SOME EXISTING APPROACHES ON GF-2 DATA-SET
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Method CC RMSE ERGAS SAM UIQI Q4
MGF 0.8165 41.0929 10.4306 7.9985 0.8163 0.4623
HCS 0.4262 66.8888 17.4652 26.9324 0.4224 0.3369
AIHS 0.8061 43.5032 10.8956 5.8268 0.8084 0.4616
Proposed
𝟏𝒔𝒕 stage
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Proposed
𝟐𝒏𝒅 stage
0.8461 37.97 9.5127 5.3391 0.8458 0.5190
Method CC RMSE ERGAS SAM UIQI Q4
MGF 0.9480 17.4182 4.1219 6.2736 0.9476 0.7619
HCS 0.7755 34.3642 10.2706 15.1813 0.7587 0.6646
AIHS 0.9575 16.2179 3.7496 4.6017 0.9571 0.7609
Proposed
𝟏𝒔𝒕 stage
0.9656 14.1916 3.3016 4.4401 0.9650 0.7642
Proposed
𝟐𝒏𝒅 stage
0.9597 15.4653 3.5877 4.5087 0.9596 0.7616
International Journal of Image Processing and Visual Communication
ISSN (Online) 2319-1724 : Volume 5 , Issue 1 , April 2018
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