IEEE Geoscience and Remote Sensing Magazine - September 2023 - 54

If the PL
c coherence is above a certain threshold, a DS
EXPLORING THE SYNERGY
BETWEEN PL AND THE TSPA
COULD LEAD TO EXCITING
RESEARCH OPPORTUNITIES
AND FURTHER
ADVANCEMENTS IN
HANDLING CHALLENGING
INSAR SCENARIOS.
point with the linked N - 1 phase value will replace the
original points. Finally, the selected DS will jointly process
using the same PSI technique
as the PS. Figure 6(b) is an
example of PL coherence,
which is in full resolution, allowing
for more comprehensive
PL performance.
Improving PL and selecting
SHPs are critical aspects
of PSDS techniques. Various
modified approaches have
been proposed to select
SHPs, including the Anderson-Darling
test [34], time
series likelihood ratios [41],
t-test
[42], fast SHP selection, Baumgartner-Weiss-
Schindler test [40], mean amplitude difference [43], and
similar time series interferometric phase [44], among
others. These approaches aim to increase the density
of DSs, mitigating sample coherence bias. Additionally,
the conventional DS assumption of independent
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small scatterers with a uniform scattering mechanism
can be relaxed by considering DS targets dominated by
two or more scattering mechanisms [36], [45]. Engelbrecht
et al. [45] showed that incorporating multiple
scattering mechanisms in L-band ALOS PALSAR data
improved deformation measurement extraction in dynamic
agricultural regions. Recently, it has been demonstrated
that adding polarimetric information can
increase the number of coherent pixels by a factor of
eight compared to a single-polarization channel [46].
Overall, improving SHP selection and PL, as well as
considering more complex scattering mechanisms, can
enhance the performance of PSDS techniques in deformation
monitoring and other applications.
COMPRESSED PERSISTENT SCATTERERS AND
DISTRIBUTED SCATTERERS INTERFEROMETRIC
SYNTHETIC APERTURE RADAR TECHNIQUE
The recent trend of spaceborne SAR missions focuses on
systematic Earth monitoring with high temporal resolution
[47], [48]. This has resulted in unprecedented SAR
data volumes due to short revisit cycles, as brief as six to
12 days, with missions such as the ESA's Sentinel-1 [49]
and NASA-Indian Space Research Organization Synthetic
Aperture Radar mission [50]. However, interferometric
processing of these large data stacks with currently available
algorithms is infeasible. To address this demand, efforts
have focused on reducing product latency through
parallelized computation and cloud computing. One of
the first efficient stacking techniques that allows for efficient
PL in sequences using isolated data batches of the
time series is the sequential estimator [51]. The ComSAR
algorithm introduced by Ho Tong Minh and Ngo [16] has
been proposed for processing PSDS analysis. Since most
deformation phenomena develop slowly, a processing
scheme can be devised using reduced-volume data sets.
The algorithm divides massive data into many ministacks
and compresses them.
Conventional MLE PL suffers from a high computaFIGURE
6. Internal results from PSDS-based processing. (a) The
number of SHPs was identified using the Baumgartner-Weiss-
Schindler test on a 9#35 window. (b) The PL coherence corresponds
to the SHP map. The figure was adapted from [16, Fig. 4].
54
tional time complexity driven by the number of SLCs
involved, primarily due to the iterative maximum likelihood
optimization for the phase estimation and secondarily
due to the regularization and inversion of the
complex coherence matrix, both of which are affected by
the number of interferograms. To address the high-datavolume
problem, data compression is a classic approach
in dealing with high data volumes. In the case of multipass
SAR, the objective is to compress a stack of coregistered
SAR data in the temporal direction so that the size
of the time series is reduced but the spatial size of each
image is intact. To perform data compression, PCA is a
well-known technique [52], [53]. However, PCA fails to
incorporate the statistical properties of the complex covariance
matrix data correctly, as it is a geometrical rather
than a probabilistic approach [53]. On the other hand,
MLE PL is a purely probabilistic approach and is well
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE SEPTEMBER 2023
IEEE Geoscience and Remote Sensing Magazine - September 2023

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