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Zhang, K., Sumbul, G., & Demir, B. (2020). An Approach To Super-Resolution Of Sentinel-2 Images
Based On Generative Adversarial Networks. 2020 Mediterranean and Middle-East Geoscience and Remote
Sensing Symposium (M2GARSS). https://doi.org/10.1109/m2garss47143.2020.9105165
Kexin Zhang, Gencer Sumbul, Begüm Demir
An Approach To Super-Resolution Of
Sentinel-2 Images Based On Generative
Adversarial Networks
Accepted manuscript (Postprint) Conference paper |

AN APPR O A CH T O SUPER-RESOLUTION OF SENTINEL-2 IMA GES B ASED ON
GENERA TIVE AD VERSARIAL NETW ORKS
K e xin Zhang 1 , Gencer Sumb ul 2 , Be g ¨
um Demir 2
1 Shanghai Jiao T ong Uni versity , 2 T echnische Uni versit ¨
at Berlin
ABSTRA CT
This paper presents a Generati ve Adv ersarial Network based
super -resolution (SR) approach (which is called as S2GAN)
to enhance the spatial resolution of Sentinel-2 spectral bands.
The proposed approach consists of two main steps. The first
step aims to increase the spatial resolution of 20m and 60m
bands by the scaling factors of 2 and 6, respecti vely . T o this
end, we introduce a generator netw ork that performs SR on the
lo wer resolution bands with the guidance of 10m bands by uti-
lizing the con volutional layers with residual connections and
a long skip-connection between inputs and outputs. The sec-
ond step aims to distinguish SR bands from their ground truth
bands. This is achiev ed by the proposed discriminator netw ork,
which alternately characterizes the high le vel features of the
two sets of bands and applying binary classification on the e x-
tracted features. Then, we formulate the adv ersarial learning of
the generator and discriminator networks as a min-max game.
In this learning procedure, the generator aims to produce real-
istic SR bands as much as possible so that the discriminator
incorrectly classifies SR bands. Experimental results obtained
on dif ferent Sentinel-2 images sho w the effecti veness of the
proposed approach compared to both con ventional and deep
learning based SR approaches.
Index T erms —
Sentinel-2 images, super -resolution, gen-
erati ve adv ersarial network, remote sensing
1. INTR ODUCTION
The ne w generation of satellite multispectral sensors (e.g.,
W orldV ie w-3 and Sentinel-2) can acquire images with mul-
tiple spectral bands with dif ferent spatial resolutions. This is
mainly due to the storage and transmission bandwidth restric-
tions [
1
]. Accordingly , one of the most important research
topics in remote sensing (RS) is to de velop methods for super -
resolving the lo wer-resolution bands and ha ving all image
bands at the highest spatial resolution. T o this end, se veral
super -resolution (SR) methods are introduced in RS. During
the last years deep neural networks, in particular con volutional
neural networks (CNNs), are found v ery ef fectiv e for SR prob-
lems. As an exampl e, in [
2
] SR of multispectral RS images is
applied with con volutional layers by utilizing only lo wer res-
olution bands (i.e., single image SR). In [
3
], residual connec-
tions are integrated into the single image SR based architecture
to enhance SR performance. In [
1
], a SR approach based on
deep residual networks is introduced to further utilize higher
resolution bands present in RS images unlike the single image
SR approaches. Recent works sho w that generati ve adv ersarial
networks (GANs) can significantly increase the performance
of image enhancement methods in computer vision [
4
,
5
].
Ho wev er , there is only a small number of GAN-based SR stud-
ies in RS. As an example, in [
6
] a particular GAN (PSGAN)
frame work is utilized to address RS image pan-sharpening
problem. The PSGAN significantly improv es the performance
of con ventional pan-sharpening methods. Howe ver , it requires
a single band panchromatic image and thus does not directly
applicable to SR problems. In [
7
], SRGAN architecture with-
out the batch normalization layers (TGAN) is trained on com-
puter vision images and fine-tuned with RS images to apply
the SR. This approach utilizes only RGB image bands and
thus limits to apply SR on high dimensional RS images. T o ad-
dress these limitations, we propose a GAN based SR approach
(S2GAN) on multispectral multi-resolution RS images. In this
paper , we mainly focus on the super-resolution of Sentinel-2
images. The proposed approach aims at increasing the spatial
resolution of Sentinel-2 20m and 60m bands to accurately pro-
vide the fine spatial details. T o this end, the proposed S2GAN
exploits the Sentinel-2 bands associated to 10m spatial resolu-
tion as a guidance for learning the SR task on lo wer resolution
bands. Experimental results confirm that the S2GAN eff ec-
ti vely and accurately pro vides high resolution bands with sig-
nificant details from lo w resolution bands by the adversarial
training of generator and discriminator networks. T o the best
of our kno wledge, we present the first study on the application
of GANs in the frame work of Sentinel-2 image SR problems.
2. PR OPOSED SUPER-RESOLUTION APPR O A CH
Let
I
be a Sentinel-2 image and
I LR
,
I H R
,
I S R
be the sets of
lo wer resolution, higher resolution and SR bands, respecti v ely .
Sentinel-2 images contain
13
spectral bands with 10m, 20m
and 60m spatial resolutions. Bands 2 to 4 and 8 are associated
to 10m resolution, whereas bands 5 to 7, 8A, 11 and 12 ha ve
20m resolution. Remaining bands (1, 9 and 10) are associated
to 60m resolution.
I H R
composes of the spectral bands 2 to 4
and 8, each of which is a section of
W × H
pixels. W e assume

Lower
Resolution
Bands
Higher
Resolution
Bands
Upsampling
Concatenation
Convolution
ReLU
Add
Super Resolved
Bands
Convolution
Convolution
ReLU
Convolution
Scaling
Add
Residual Block

Fig. 1 : The proposed generator neural netw ork for the characterization of super-resolv ed bands.
that the set of lo wer resolution bands can include either bands
5 to 7, 8A, 11, 12 or 1, 5 to 7, 8A, 9 to 12. This can be defined
with respect to the scaling factor of the approach, which is
either
2
or
6
. W e aim to learn a function
f
, which applies
super -resolution on I LR by exploiting I H R as follo ws:
f : I H R , I LR → I S R
∀ I H R ∈ R W × H × 4
∀ ( I LR ∈ R W
2 × H
2 × 6 ) ⊕ ( I LR ∈ R W
2 × H
2 × 6 × R W
6 × H
6 × 3 )
∃ I S R ∈ R W × H × 6
(1)
where
I S R
denotes the SR bands of
I LR
and
⊕
denotes the
XOR gate, which results true if only one of the inputs to the
gate is true. T o this end, we propose a GAN based SR ap-
proach, which consists of two main steps: 1) characterization
of SR bands by the generator neural network; and 2) classifica-
tion of SR and ground truth bands by the discriminator neural
network. Let
G
and
D
be the generator and discriminator net-
works, respecti vely .
G
maps the sets of
I LR
and
I H R
to the set
I S R
.
D
aims to accurately distinguish generated image bands
I S R
from their ground truth bands. T o this end, we define the
adversarial loss o ver N training images as follo ws:
L Adv ersar ial =
N
X
n =1
l og (1 − D ( G ( I LR , I H R ))) . (2)
D
aims to maximize the adversarial loss for better discrimi-
nation ability , whereas
G
aims to minimize this loss to fool
discriminator such that discriminator incorrectly labels SR im-
age bands as true bands. Thus, this min-max game of
G
and
D is formulated as follo ws:
min
θ max
β
E I GS ∼ p data ( I GS ) log D ( I GS ; β )+
E ( I LR ,I H R ) ∼ p G ( I LR ,I H R ) log (1 − D ( G ( I LR , I H R ; θ ); β ))
(3)
where
θ
and
β
are the parameters of generator and discrimina-
tor , respectiv ely , and
I GS
is the set of higher resolution ground
truth bands associated to
I S R
. Each step of the proposed ap-
proach is explained in the follo wing sections.
2.1. Characterization of Super -Resolution Bands
This step aims at producing realistic SR image bands, which
ha ve similar data distrib ution as ground truth bands. T o obtain
the SR image bands, we propose a generator neural netw ork
inspired by [
1
]. Dif ferent from con ventional single image SR
approaches, the higher resolution image bands are also utilized
in this step together with the lo wer resolution bands to guide
the SR learning approach. Thus, the generator learns to trans-
fer information present in higher resolution bands to lo wer
resolution bands. T o this end, lo w resolution image bands are
first upsampled with the bilinear interpolation and then con-
catenated with higher resolution bands. The subsequent con vo-
lution layer , activ ation layer and 18 Residual blocks are used
to extract essential features from combined set of image bands.
In addition, a long skip-connection between upsampled lo wer
resolution bands and the final output enable the generator net-
work to map the upsampled image bands to the desired higher
resolution output. This preserves the radiometry of the input
image [
1
]. In residual blocks, we remo ve the batch normaliza-
tion layers. This reduces computational complexity and results
in better performance in SR [
5
]. The proposed generator neu-
ral netw ork is illustrated in Fig. 1. It is worth noting that, in
addition to the adversarial loss, the pix el-wise mean absolute
error (MAE) between the SR and the ground truth bands (
I GS
)
is also utilized as the content loss of the generator .
2.2. Classification of Super -Resolution Bands
This step aims to correctly distinguish SR image bands from
their ground truth bands by extracting the high le vel features
for better classification. T o this end, this step includes three
consecuti ve blocks, each of which includes a single layer of
con volution, acti vation and batch normalization. The kernel
size of all con volutional layers in the discriminator is
3 × 3
.
Numbers of filters in con volutional layers are 64, 128 and 128.
Strides of 2 and 1 are utilized in those layers to reduce the di-
mensionality of the input. T o increase the stability of the adv er-
sarial training, Leaky ReLU is used as the activ ation function
of the blocks with batch normalization. Finally , a fully con-
nected layer is included to produce final binary classification
probabilities. The proposed discriminator neural network is il-

Convolution
LeakyReLU
Flatten
Dense
LeakyReLU
Dense
Label
Sigmoid
Ground T ruth
Bands
Super Resolved
Bands
Convolution
LeakyReLU
Batch
Normalization
OR
Discriminator Block

Fig. 2 : The proposed discriminator neural netw ork for the classification of super-resolv ed and ground truth bands.
T able 1
: SR results (associated to the scaling factor of 2) ob-
tained by the bicubic interpolation, the A TPRK, the SupReME,
the Superres, the DSen2 and the proposed S2GAN on the
do wnsampled 20m resolution bands.
Method RMSE SRE SAM UIQ
Bicubic 123.5 25.3 1.24 0.821
A TPRK [8] 116.2 25.7 1.68 0.855
SupReME [9] 69.7 29.7 1.26 0.887
Superres [10] 66.2 30.4 1.02 0.915
DSen2 [1] 34.5 36.0 0.78 0.941
S2GAN 33.1 36.4 0.74 0.950
lustrated in Fig. 2. The input to the network is either SR bands
from the generator network or the corresponding ground truth
bands, whereas the output is the label, which denotes whether
the input is ground truth or SR bands (
I S R
). Accordingly , to
define the discriminator loss, we incorporate the adversarial
loss with the follo wing discriminator loss:
L D iscr iminator =
N
X
n =1
l og (1 − D ( I GS )) . (4)
If the input data is the ground truth bands of the
I S R
, the output
v alue will be close to 1. In this case, the input has a lar ge
probability to be realistic.
3. EXPERIMENT AL RESUL TS
Experiments were conducted on dif ferent Sentinel-2 images
provided in [
1
]. W e used the same training, validation and test
images as suggested in [
1
]. T o optimize the loss functions, we
used the mini-batches of size 128 throughout 56 epochs. At
each iteration, the generator and the discriminator networks
were trained sequentially on NVIDIA T esla P100 GPU. W e
compared our approach with: 1) the bicubic interpolation; 2)
the area-to-point regression kriging (A TPRK) [
8
] that is a
pan-sharpening based approach; 3) the Super -Resolution for
Multispectral Multiresolution Estimation (SuperReME) [
9
]
approach; 4) the Superres [
10
] that is a geometrical model
based approach; and 5) the DSen2 [
1
] that is a CNN based
approach. Results of each approach are provided in terms of
four performance e v aluation metrics: 1) Root Mean Squared
Error (RMSE), 2) Signal to Reconstruction Error Ratio (SRE),
3) Uni versal Image Quality Index (UIQ) and 4) Spectral An-
gle Mapper (SAM). SRE measures the error relativ e to the
mean intensity of a SR image band, and thus provides v alues
in decibels (dB). UIQ e v aluates the luminance, contrast, and
structure of a SR image band with the maximum v alue of 1.
SAM measures the angular de viation between the spectral sig-
natures of the ground truth and SR bands, and thus provides
the v alues in degrees.
W e applied SR to the bands associated with 20m and 60m
spatial resolutions. Due to the una v ailability of ground truth
bands at 10m spatial resolution for these bands, we follo wed
the do wnsampling strategy to train the proposed architecture
and to e v aluate the performance of the S2GAN. T o this end,
the bands associated with 20m and 60m spatial resolutions
were do wnsampled to 40m and 360m spatial resolutions, and
then SR was applied by the considered methods. The a verage
results ov er all test images associated to the scaling factor of
2 are gi ven in T able 1. As we can see from the table, our ap-
proach (S2GAN) performs better than the other approaches
under all metrics. These results sho w that our approach ef-
fecti vely applies SR on the lo wer resolution Sentinel-2 bands
to accurately enhance their spatial resolutions similar to the
ground truth bands. T o visually e valuate the performance of
the S2GAN, we selected test images, which include relati vely
high subtle details. Fig. 3 sho ws the true color composite of
RGB bands and the false color composite of SR bands ob-
tained by the S2GAN. In addition, T able 2 presents the a verage
RMSE v alues for SR bands o ver these test images obtained
by the DSen2 and the S2GAN. In such a relati vely dif ficult
scenario, the performance of the S2GAN for SR task is more
significant compared to the DSen2. This also sho ws the suc-
cess of our approach ov er the state-of-the-art approaches.
4. CONCLUSION
This paper proposes a GAN based approach (S2GAN) to en-
hance the spatial resolution of multispectral multi-resolution
Sentinel-2 images. The proposed approach consists of two
main steps: 1) accurately increasing the spatial resolution of
20m or 60m bands with the guidance of 10m bands by the
generator neural netw ork; and 2) ef fecti vely distinguishing
the SR image bands from their ground truth bands by the
discriminator neural network. W e also applied the adv erse-
rial learning of generator and discriminator networks. Experi-

a)
b)

Fig. 3
: An example of SR results (associated to the scaling f actor of 2) obtained by the proposed S2GAN on the downsampled
bands associated with 20m spatial resolutions. (a) The true color composite of do wnsampled RGB bands associated with 10m
spatial resolution. (b) The false color composite of SR bands 5 to 7 obtained by the S2GAN.
T able 2
: RMSE (associated to the scaling factor of 2) obtained
by the DSen2 and the proposed S2GAN on downsampled 20m
resolution bands of the test images gi ven in Fig. 3.
Method B5 B6 B7 B8A B11 B12 A vg.
DSen2 25.3 51.0 61.2 63.2 33.4 30.9 44.2
S2GAN 23.1 43.4 52.4 53.1 30.7 28.3 38.5
mental results obtained on the Sentinel-2 images indicate that
our approach achie ves promising performance for the SR of
Sentinel-2 lo wer resolution bands with respect to the state-of-
the-art approaches. W e would like to note that the S2GAN
approach can be also applied to an y other RS image. As a
future work, we plan to improv e the network structures of
generator and discriminator steps, which can be achie ved by
integrating the realistic discriminator or W asserstein GAN into
our approach.
5. A CKNO WLEDGMENTS
This work w as supported by the European Research Council
under the ERC Starting Grant BigEarth-759764. The authors
would like to thank Y akun Li, DFKI GmbH, Germany and
Dr . Hua Y ang, Shanghai Jiao T ong Univ ersity , China for the
helpful suggestions.
6. REFERENCES
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