IEEE Geoscience and Remote Sensing Magazine - September 2023 - 59

computational cost [55]. This is particularly important for
near-real-time processing of big SAR data, where the computational
cost of the PL process is a significant concern.
In summary, DL holds considerable promise for various
applications in PL, offering opportunities to enhance
precision estimation and computational efficiency. Nevertheless,
it is important to acknowledge the challenges and
limitations associated with its implementation. These include
data quality, feature extraction, model complexity,
and interpretability concerns. While DL presents an enticing
avenue for future research trends in PL, it necessitates
a meticulous design and thorough evaluation of both the
models and the data involved.
EXAMPLE WITH U-NET MODEL
To demonstrate the applicability of DL in PL, we conducted
a proof-of-concept test on a synthetic dataset.
The simulation settings included generating a radar data
stack with temporal noise behaviors, consisting of 10
SLC images with a revisit time of 35 days. We simulated
a deformation signal by assuming a simple Gaussian deformation
bowl with a maximum LOS deformation rate
of 14 mm/year at the center and a radius of 600 m. The
simulation was conducted on a flat area, resulting in a
zero topographic signal. We assumed a crop of 1,280 #
1,280 m, a radar wavelength of 56 mm, and a pixel size of
20 # 20 m. We employed the coherence Sentinel-1 model
to simulate the behavior of temporal coherence over time,
which generates a coherence matrix for a one-year time
series of 10 temporally ordered measurements taken at
35-day intervals [21], [16]. Each SLC included 64 # 64 homogeneous
pixels, and the simulation was repeated 5,000
times for DL data training. Figure 9(a) and (b) provides
a visual representation of both the noise-free and noisy
simulated datasets. Figure 9(c) and (d) demonstrates the
application of the MLE spectral regularization algorithm
using coherence matrix estimation over 11 # 11 windows
and a b value of 0.5 (see the " Recent Advances " section).
We used a MATLAB function called unetLayers to define
the U-Net architecture, with a depth of three in the
encoder network. The complex-valued interferometric
data were separated by magnitude and phase quantities as
input for the U-Net model, resulting in only N21channels
since the first phase is zero. The patch size was specified
as 64 # 64 # 19, and the number of output channels
was nine ESR phases. We modified the U-Net architecture
by replacing the original softmax layer with a regression
layer and trained the model on a dataset of 4,800, measurements
with 200 for validation, setting MaxEpochs
as 10. Figure 9(e) and (f) indicates that the DL approach is
expected to be comparable to the handcrafted MLE algorithm.
However, it is important to note that the synthetic
simulation used in this example was relatively basic. For
future studies to effectively address the PL challenge using
DL, it is crucial to have access to high-quality datasets
containing both synthetic and real-world interferometric
SEPTEMBER 2023 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
patch images from various landscapes. Additionally, utilizing
a more advanced U-Net model with greater depth
can significantly enhance the accuracy and efficiency of
the PL process.
CONCLUSIONS
Accurate estimation of linked phases is crucial in mitigating
decorrelation effects on SAR data in the PL technique.
Researchers have proposed various algorithms, such as
quasi-Newton, CAESAR, and EMI, to improve PL estimation's
precision and computational efficiency. Additionally,
new compression and regulation techniques have
been developed to enhance the estimation of the coherence
matrix. PL is widely used in TomoSAR, PSDS, and
ComSAR applications, and the adoption of DL is expected
to improve the accuracy and efficiency of the process. The
future of the DL approach for PL is promising, as ongoing
research in various areas can shape the development of
better algorithms and techniques. This will help improve
the detection and measurement of surface deformation
and parameter estimation in SAR applications, leading to
more accurate and efficient results.
ACKNOWLEDGMENT
This work was supported, in part, by the ESA; Center National
d'Etudes Spatiales/Terre, Ocean, Surfaces Continentales,
Atmosphere (project MekongInSAR); UMR TETIS;
and Institut National de Recherche en Agriculture, Alimentation,
et Environnement. The ALOS-2/PALSAR-2 data were
kindly provided by the Japanese Aerospace Exploration
Agency, under the third Research Announcement Program
on Earth Observation, with project ER3A2N097.
AUTHOR INFORMATION
Dinh Ho Tong Minh (dinh.ho-tong-minh@inrae.fr) is with
UMR TETIS, INRAE, University of Montpellier, 34090
Montpellier, France. He is a Member of IEEE.
Stefano Tebaldini (stefano.tebaldini@polimi.it) is with
Dipartimento di Elettronica, Informazione, e Bioingegneria,
Politecnico di Milano, 20133 Milano, Italy. He is a Senior
Member of IEEE.
REFERENCES
[1] D. Ho Tong Minh, R. Hanssen, and F. Rocca, " Radar interferometry:
20 years of development in time series techniques and
future perspectives, " Remote Sens., vol. 12, no. 9, Apr. 2020, Art.
no. 1364, doi: 10.3390/rs12091364. [Online]. Available: https://
www.mdpi.com/2072-4292/12/9/1364
[2] R. F. Hanssen, Radar Interferometry: Data Interpretation and Error
Analysis. Dordrecht, The Netherlands: Kluwer, 2001.
[3] H. A. Zebker and J. Villasenor, " Decorrelation in interferometric
radar echoes, " IEEE Trans. Geosci. Remote Sens., vol. 30, no. 5,
pp. 950-959, Sep. 1992, doi: 10.1109/36.175330.
[4] A. Ferretti, C. Prati, and F. Rocca, " Permanent scatterers in SAR
interferometry, " IEEE Trans. Geosci. Remote Sens., vol. 39, no. 1,
pp. 8-20, Jan. 2001, doi: 10.1109/36.898661.
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http://dx.doi.org/10.3390/rs12091364 https://www.mdpi.com/2072-4292/12/9/1364 https://www.mdpi.com/2072-4292/12/9/1364 http://dx.doi.org/10.1109/36.175330 http://dx.doi.org/10.1109/36.898661
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