IEEE Geoscience and Remote Sensing Magazine - March 2020 - 26

InSAR techniques based on the PS for deformation analysis include PSI [4], PS pair (PSP) interferometry [39], interferometric point target analysis (IPTA) [40], the Stanford method for PS (StaMPS) [41], and spatiotemporal
unwrapping network (STUN) [42], among others.
DS targets contain several small and random scatterers without any particular one being dominant. DSs are
mainly distributed in rural and mountainous areas, such
as agricultural fields, bare soil, and rock surfaces. In SAR
images, a DS target is usually dispersed in multiple pixels. It has a low phase coherence in time sequence when
any one of the pixels is analyzed separately, but a stable
scatterer can be synthesized by combining multiple pixels using a specific algorithm; this process is depicted in
Figure 7(e) and (f). Figure 7(d)-(f) shows the DS objects,
the DS signal in the complex plane,
and the main direction of the DS
signal [43]. The simplest way to use
DSs is through multilook processing adopted by the small baseline
set (SBAS) [44] method, which does,
unfortunately, degrade the resolution of SAR images. One solution is
to integrate PSs into SBAS. The other
is to identify DSs using an adaptive
window based on a statistical test.
The time-series InSAR techniques
based on DSs for deformation analysis include SBAS [44], SqueeSAR
[45], joint-scatterer InSAR (JSInSAR)
[46], and other improved methods
[47]-[50]. A more comprehensive
analysis of DSs can be found in [51].
Interferogram generation can
be divided into the single-reference
and mult ireference mode-based
met hods. The techniques that use
FIGURE 5. Time-series InSAR extracts the deformation rate and cumulative deformation
the single-reference mode include
information from complex 3D signals.

SBAS
[Berardino et al.]

IPTA
[Werner et al.]

PSP
[Constantini et al.]

CPT
[Blanco-Sanchez et al.]

SqueeSAR
[Ferretti et al.]

Year

2020

2019

JSInSAR
[Lv et al.]

2018

2017

2016

QPS
[Perissin et al.]

2015

2014

2013

TCP-InSAR
[Zhang et al.]

2012

2011

2010

2008

2007

2006

2009

SPN
[Crosetto et al.]

StaMPS
[Hooper et al.]

2005

2004

2003

2002

STUN
[Kampes]

2001

PSI
[Ferretti et al.]

2000

In SAR images, PSs are the pixels corresponding to the
PS targets on the ground. The phase information recorded in these pixels with a high signal-to-noise ratio (SNR)
is less affected by temporal and spatial decorrelation.
Accordingly, they have high phase coherence in a long
time sequence. Figure 7(a)-(c) shows the PS objects, the
PS signal in the complex plane, and the main direction
of the PS signal.
The phase stability of the scatterer correlates to amplitude stability, which provides the basis for PS identification. The amplitude dispersion index (ADI) [4] is a criterion for selecting the PS: D A = v A /n A, where n A and v A
are the mean and standard deviation, respectively, of the
amplitude values of a pixel. In general, a pixel with a D A
lower than 0.25 is selected as the PS point. Time-series

MTDA
[Yu et al.]

FIGURE 6. The development of various time-series InSAR techniques. QPS: quasi-PS.

26

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

MARCH 2020
IEEE Geoscience and Remote Sensing Magazine - March 2020

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - March 2020
