IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - 32
A Survey on the Applications of Convolutional Neural Networks for Synthetic Aperture Radar: Recent Advances
tend to overfit on the scarce SAR data set. Second, SAR
image classification are sometimes implemented in
resource-constrained systems where storage, computation,
and power are limited, such as embedded airborne SAR
processing devices. In some scenarios, such as ground
moving target indication (GMTI) in a terminal military
guidance, it is required to have low-latency analysis,
therefore, deep and large pretrained networks, which typically
have considerable inference and storage costs, are
not suitable [199]. Falqueto et al. [203] used VGGNet for
the specific problem of oil rig recognition on Sentinel-1
SAR Images. Wurm et al. [204] studied the transfer learning
capabilities of FCNs, from very high-resolution
(VHR) optical satellite imagery of Quickbird to active
SAR imagery ofTerraSAR-X.
DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL
NETWORKS
Figure 14.
Transferring knowledge from unlabeled SAR images proposed
by [194].
they utilized the subaperture decomposition technique
[202] to obtain three-channel subaperture SAR images.
Utilizing a large pretrained network has also some downsides.
First, since these pretrained networks contain millions
or billions of parameters, the transferred networks
GANs [205] are DL architectures typically used for generating
new instances of the input data that mimic the real data.
They can also be used to distinguish between real and fake
data. AGAN consists ofa generator network and a discriminator
network that compete against each other. The generator
network tries to produce fake data and the discriminator
tries to identify the real data from the fake one in order not
to be fooled by the generator. At the end of this adversarial
game, they reach the Nash equilibrium point. Radford et al.
[206] introduced deep convolutional GANs (DCGANs) in
2015. Guo et al. [207] used DCGANs to implement a SAR
image simulator. This simulator could be helpful to synthesize
SAR images in a desired observation angle from a limited
set of aspects. This is important when orbital geometry
limitations and high maintenance costs are taken into consideration.
Gao et al. [208] employed DCGANs to predict
the labels of SAR samples by training the network with a
Figure 15.
Suggested transfer learning scheme by [195].
32
IEEE A&E SYSTEMS MAGAZINE
MAY 2022
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV
Table of Contents for the Digital Edition of IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV
Contents
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Contents
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Cover2
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - 1
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IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Cover3
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Cover4
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