IEEE Geoscience and Remote Sensing Magazine - March 2023 - 17

GD-Wishart
HDEC-TFA
L-Shaped
Building
I-Shaped
Building
SOLEIL
Synchrotron
H-Shape
Building
Google Earth
Quad-Pol
HH Channel
FIGURE 8. The SOLEIL synchrotron in France and the surrounding buildings with different shapes are depicted in the Gaofen-3 SAR image.
Both the GD-Wishart [38] result on quad-polarization (quad-pol) SAR data and the HDEC-TFA [29] result on HH channel single-pol SAR can
capture the special scattering characteristics of the objects.
Figure 9(b) illustrates a second
line of thought, which we refer to
as physics-guided learning. First, the
physical layer is used for generating
meaningful physical representations,
like scattering mechanisms [physical
layers 1and 2 can both achieve this].
Meanwhile, the neural network extracts
hierarchical spatial features
from the SAR image. The crucial
point is how to establish a connection
between physical properties and
image features. We propose exploiting
physical layer 3 to reveal relationships
and thereby design an objective
function for self-supervised learning.
Our recent work [37], [42], [43]
details the paradigm in Figure 9(b). A
physics-guided network for SAR image
feature learning was proposed as
shown in Figure 10. First, a physical
layer is deployed at the beginning,
where the physical scattering properties
are derived. Based on the crucial
assumption that SAR image features
and the abstract physical scattering
mechanisms should share common
attributes in the semantic level, a
surrogate task was established via
the other physical layer that defines
a loss function. The inspiration is
SSL Loss
Physical Layer
1 or 2
DNN
Relations
Different
Representations
x1
x2
xn
Representation 1
Physical Layer
SAR Image
(a)
DNN
SAR Image
(b)
Representation 2
Physical Layer 3
SSL Loss
FIGURE 9. The SAR physical layer can be integrated in a self-supervised learning framework
to guide the neural network training without a ground truth. (a) The physical layer generates
various modalities of an SAR image using well-established physical models, such as subaperture
images, different polarimetric features, and so on. The self-supervised learning can be
conducted with a contrastive learning paradigm. (b) The physical layer produces a physical
representation of the image, serving as a guided signal that drives the neural network to learn
a similar representation. SSL: self-supervised learning.
MARCH 2023 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
17

IEEE Geoscience and Remote Sensing Magazine - March 2023

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