101 
MAPPING GROUND DEFORMATION BY RADAR INTERFEROMETRY BASED ON 
PERMANENT-SCATTERER NETWORK: ALGORITHM AND TESTING RESULTS 
Guoxiang Liu 3, *, S. M. Buckley b , Xiaoli Ding c , Qiang Chen 3 , Xiaojun Luo 3 
a Dept. of Surveying Engineering, Southwest Jiaotong University, Chengdu, China - rsgxliu@swjtu.edu.cn 
b Centre for Space Research, The University of Texas at Austin, Austin, Texas, USA - sean.buckley@mail.utexas.edu 
c Dept. of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China - 
lsxlding@polyu.edu.hk 
KEY WORDS: Ground Deformation, Radar Interferometry, PS Networking, Atmospheric Signal, Empirical Mode Decomposition 
ABSTRACT: 
The full operational capability of synthetic aperture radar (SAR) interferometry in deformation monitoring has not been achieved yet 
due to the negative influences of spatio-temporal decorrelation and atmospheric delay. With the use of time series of SAR images, 
deformation extraction can be however improved by only tracking some objects with steady radar reflectivity, generally referred to 
as permanent scatterer (PS). This paper presents an attempt to explore a PS-networking approach to isolate deformations from other 
effects such as atmospheric signals and topographic errors. The deforming process in time and space is modelled and estimated with 
a very strong network which is formed by connecting adjacent PSs. The linear deformations and topographic errors are estimated by 
optimizing objective functions and by adjusting the network via weighted least squares (LS) solution. The time series of nonlinear 
deformations and atmospheric signals are computed by singular value decomposition (SVD) and empirical mode decomposition 
(EMD). To validate the algorithm, 39 ERS C-band SAR images acquired over Phoenix in Arizona (USA) from 1992 to 2002 are 
used to detect land subsidence caused by the excessive groundwater withdrawal. 
1. INTRODUCTION 
Existing studies have shown that there are two major limitations 
in conventional differential SAR interferometry (DInSAR) for 
land deformation monitoring, i.e., spatio-temporal decorrelation 
and atmospheric artifacts (e.g., Zebker & Villaseno, 1992; 
Buckley, 2000; Liu, 2003; Ding et al., 2004). To mitigate such 
drawbacks, many research efforts have been made in recent 
years to explore various techniques for detecting the temporal 
evolution of deformations using time series of SAR images. 
A strategy proposed early is to stack the multiple 
interferograms (Sandwell & Price, 1998). Ground deformation 
analysis can be therefore improved by enhancing fringe clarity 
and suppressing atmospheric effects. Afterwards, a very generic 
approach, called permanent scatters (PS) technique, was 
proposed to extract both linear and nonlinear deformations from 
a set of interferograms by isolating atmospheric effects and 
topographic errors (Ferretti et al., 2000, 2001). Since PSs are 
usually some hard objects such as buildings and rocks, they can 
remain temporal coherent radar reflectivity, and thus facilitating 
deformation extraction on the basis of PSs’ phase measurements 
with high signal-to-noise ratio (SNR). Subsequently, another 
effective approach, called small-baseline subset (SBAS) method, 
was developed to further decrease the negative influences due 
to decorrelation noise and bias (Berardino et al. 2002). 
PS technique suffers from spatial decorrelation as some long- 
spatial baselines may result in by sharing a unique master image 
in forming interferometric combinations, while SBAS technique 
suffers from errors caused by full-resolution phase unwrapping. 
However, the two techniques can complement each other (Mora 
et al. 2003). Combining the merits of PS and SBAS technique, 
this paper presents an improved algorithm to isolate and extract 
deformations, topographic errors and atmospheric signals with a 
very strong network formed by freely connecting neighbouring 
PSs. To maximize coherence of all the SAR dataset, the spatial 
and temporal baseline thresholding are applied when forming . 
interferometric combinations. The phase modelling is based on 
the network. The linear deformation velocities and topographic 
errors are first estimated by optimizing an objective function of 
each arc (a connection of two PSs) and adjusting the network by 
LS solution. Time series of phase measurements at each PS is 
then reconstructed by singular value decomposition (SVD) and 
decoupled into nonlinear deformations and atmospheric signals 
by a relatively new signal analysis method - empirical mode 
decomposition (EMD), proposed by Huang et al. (1998). For 
algorithm validation, some experiments have been carried out to 
analyze subsidence evolution in Phoenix metropolitan with 39 
ERS-1/2 C-band (wavelength of 5.6 cm) SAR images acquired 
1992 through 2000. 
2. DATA PREPROCESSING AND PS NETWORKING 
2.1 Data Preprocessing 
Suppose that deformation analysis is based on N+1 SAR images 
acquired at the ordered times (t 0 ,t,,A ,t N ) over the same area. 
An interferometric combination is acceptable if and only if its 
temporal baseline is below a given threshold (e.g., 4 years) and 
its spatial baseline is also below a given threshold (e.g., 120 m 
for ERS SAR). Let us assume that M interferograms may be 
formed in this way. Prior to further analysis, several procedures 
are necessarily performed to compute differential 
interferograms. 
Since co-registering SAR imagery is a key prerequisite for any 
* Corresponding author.