The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B7. Beijing 2008
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artifacts) are compensated for at PSs by exploiting their
different spectral behaviors in a multidimensional framework of
time, space and normal baseline (i.e., acquisition geometry).
4.1 Processing Result
The 26 scenes of archived ERS-1/2 SLC images are processed
by TRE (In the framework of Dragon I Project). 22,139 PSs are
identified in the test area of about 1,000km 2 , i.e. with a density
of 22 points/ km 2 . The subsidence velocities on the PSs from
1993 to 2000 are derived to produce the subsidence map, as
showed in figure.4.
4.2 Validation
In the test area, 126 LBs were measured from 1993 to 2000 year,
as showed in figure.5. The levelling campaign was made each
year in this period, LBs rank Level 1, Level 2 as the levelling
standards of China, with standard deviation less than 2mm. The
average of multiple surveys at each LB is used as the criterion
to validate the result in long time series.
Nearest neighboring method is used to validate PSs computed
by PS. The distance between PSs and LBs is limited to range of
100m. Within the distance, PS closest to LP will be selected for
comparison. In figure.8, 115 pairs of PSs and LBs are chosen
for comparison, and the comparison result shows good
agreement between PSs and LBs. The mean and standard
deviation of errors (i.e. the difference between PSs and LBs) are
respectively -2.02mm/yr and 3.44mm/yr. 85.22 % absolute
value of errors are centralized in the range of 6mm, and the
number and percentage of errors decrease with the increase of
errors, as shown in figure. 10.
5. STUN TECHNIQUE
In the processing system of STUN technique, the PSs are
initially identified with sublook correlation method (Lijun Lu et
al., 2008) instead of amplitude dispersion threshold method in
the primitive STUN as it can identify PSs with a limited number
of observations or even without temporal baseline. The PSs
identification method is applicable for small dataset. STUN is
based on the refined differential interferometric model
(functional model and stochastic model). The functional model
is established similar to interferometric phase model at
identified PSs. And the stochastic model is also introduced to
adjust the different weight for interferometric data, which
contains disturbing phase terms (e.g. atmospheric artifacts and
noises). On the basis of functional and stochastic model,
weighted integer least-square estimator is utilized to estimate
the integer ambiguity with the highest probability and the
unknown key parameters including DEM error and subsidence
velocity.
5.1 Processing Result
We processed the 9 latest scenes of ASAR SLC images. 83,044
PSs are identified in the test area of about 1,000km 2 ,i.e. with a
density of 83 points/ km 2 . The density of PSs in short time
series is much bigger than ones in long time series as more
points exhibiting phase stability in the shorter time span are
identified as PSs. The subsidence velocities on the PSs from
2003 to 2005 are derived to produce the subsidence map, as
showed in figure.6.
5.2 Validation
In the test area, 177 LBs ranked Level 1 and Level 2 are
measured from 2003 to 2005 year, as showed in figure.7. There
are two levelling campaign in this period. The average of two
surveys is used as the criterion to validate the result in short
time series.
Nearest neighboring method is also used to validate PSs
computed by STUN. As shown in figure.9, there are 140 pairs
of PSs and LBs chosen for comparison, and the result shows
good agreement between PSs and LBs. However, we also notice
that a few bigger errors appear and the possible cause may be
that subsidence variation does not fit the referred the linear
model. The mean and standard deviation of errors are -
2.01mm/yr and 4.30mm/yr respectively. Seen from figure.ll,
79.29% absolute value of errors are centralized in the range of
6mm, and the number and percentage of errors decrease with
the increase of errors.
Figure 4. Subsidence velocity map in 1993-2000
Figure 5. The Leveling Benchmarks surveyed in 1993-2000