IEEE Geoscience and Remote Sensing Magazine - September 2022 - 232

and testing data sets that might occur from different sensing
platforms, light conditions, and so on.
DIFFERENT SOURCES FOR THE INPUT AND OUTPUT
Contrary to most approaches in this category that exploit
Wald's protocol, a number of methods have been proposed
that utilize different sources for the input and output. Galar
et al. [177] (S2PS) propose the use of PlanetScope images
as the target to downscale the four Sentinel-2 10-m bands.
They train a modified version of the EDSR separately for
each of the NIR and red bands, accounting also for the style
transfer loss [178] between the prediction and target.
TABLE 5. A SUMMARY OF THE STATE-OF-THE-ART DL MODELS FOR SR IN RS. (Continued)
MODEL
SGFBGAN
[220]
SR-GAN
[223]
NF-GAN
[224]
Di-GAN
[225]
FSRCNN
[226]
PSSR
[227]
WDCCN
[228]
MSSRRC
[229]
SR
TYPE
DESCRIPTION/NOVELTY
RefSR Extension of SD-GAN with a triplet of
discriminators and recursive layers in the
generator, curriculum learning also used
SISR -
SISR Generator based on residual
encoder-decoder, discriminator
based on ResNet50, and embodies
despeckling component
SISR Generator based on U-Net and discriminator
based on PatchGAN-like
network
SISR -
SISR Learnable preupsampling, uses a
complex structure block for complex
numbers, uses residual compensation
approach, and uses fully polSAR
SISR Uses residual compensation and
uses fully polSAR data
CV
MODEL BUILDING BLOCKS
-
UPSAMPLING
FRAMEWORK ARCHITECTURE
Residual learning and
subpixel convolution
SRGAN Residual learning and
subpixel convolution
-
Residual learning and
transposed convolution
-
Residual learning and
transposed convolution
-
-
Residual learning
Residual learning and
transposed convolution
SISR Import weighted dense connections DRCN Residual learning
VDSR
Residual learning
Postupsampling GAN
Postupsampling GAN
Preupsampling GAN
CODE AVAILABLE/
NUMBER
OF PARAMETERS
Yes/-
No/-
No/-
Preupsampling GAN
No/-
Preupsampling CNN
Preupsampling CNN
No/-
No/-
Preupsampling CNN
No/-
Preupsampling CNN
No/-
CV Model refers to the models presented in Table 2. ConvGru: convolutional gated recurrent unit; LISS: linear imaging self scanning; polSAR: polarimetric synthetic aperture radar; SAR:
synthetic aperture radar.
LGCNet
LR Image
HR Image
Upscale
Representation
Local-Global
Combination
Reconstruction
FIGURE 26. A high-level overview of LGCNet. Blue boxes represent convolutional layers followed by ReLU activation, orange boxes represent
the concatenation of selected feature maps via a convolutional layer, and the green box represents the last convolutional layer for the
final reconstruction. (Source: [171]; used with permission.)
232
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