IEEE Geoscience and Remote Sensing Magazine - September 2023 - 56
where the superscripts indicate the time series sequence
and ()kcal
jt
is the kth of the calibration vector.
Figure 8(a) displays an original interferogram covering
670 days in Mexico City, Mexico, while Figure 8(b) is a
compressed version (with M = 10). Compared to the original
[in Figure 8(d) and (e)], the compressed interferogram
exhibits superior quality and coherence. The average coherence
improves from 0.4 to 0.8 [shown in Figure 8(c)], mainly
because the noise component of the data is reduced in the
compression process. Essentially, the noisy short-lived components
are eliminated from the artificially compressed interferograms
created from the ministacks. Consequently,
these interferograms have a higher signal-to-noise ratio
than the initial ones.
Since the estimation of the linked phase is ambiguous,
we set the phase of the first image in each ministack to zero.
Finally, PS values at these multireference images will be
extracted from the original SLCs and integrated into compressed
phase time series for PSDS analysis [16].
An interesting development by Ho Tong Minh and
Ngo is the implementation of the PSDS and ComSAR
algorithms, which have been made available as an open
source TomoSAR package [16], [54], [56]. To the best of
our knowledge, this package is the first publicly accessible
tool
that enables the simultaneous handling of
both PS and DS targets (https://github.com/DinhHoTong
Minh/TomoSAR).
PERSPECTIVE WITH DEEP LEARNING
This section highlights the potential of the DL approach in
SAR data, emphasizing the value of DL in enhancing the
accuracy and efficiency of PL. The " Brief Review of Deep
Learning for Synthetic Aperture Radar Data " section serves
as a bridge, introducing DL's potential for SAR data processing
and paving the way for its application in improving PL
techniques, in the " Perspective for Phase Linking " section.
BRIEF REVIEW OF DEEP LEARNING FOR
SYNTHETIC APERTURE RADAR DATA
In recent years,
the increase in SAR Earth observation
missions, such as TerraSar-X, Sentinel-1, ALOS, and RADARSAT,
has led to a new scenario characterized by the continuous
generation of a massive amount of data. On the one
hand, this trend has allowed us to observe the inadequacy
-π
+π
(a)
× 104
12
10
8
6
4
2
0 0.2 0.4 0.6
Coherence
(c)
0.8 1
Compressed Coherence
Raw Coherence
(b)
(d)
(e)
FIGURE 8. Mexico interferograms. (a) A raw 670-day interferogram. (b) A compressed 670-day interferogram. (c) The coherence distribution.
(d) and (e) Zoomed-in versions of (a) and (b), respectively. The figure is adapted from [54, Fig. 3].
56
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE SEPTEMBER 2023
Frequency
https://github.com/DinhHoTongMinh/TomoSAR
https://github.com/DinhHoTongMinh/TomoSAR
IEEE Geoscience and Remote Sensing Magazine - September 2023
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