International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004 
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Figure 5. Results of the DInSAR analysis of the subsidence of Sallent (Spain), based on two independent datasets. Left image: 
geocoded mean velocity fields over about five years, estimated with 13 ascending interferograms. Right image: geocoded mean 
velocity fields over the same period, which was estimated with 14 descending interferometric pairs. The two fields are superposed to 
a 1:5000 orthoimage of the Cartographic Institute of Catalonia (ICC). 
As already mentioned in the introduction, one of the most 
important characteristic. of DInSAR is its capability to 
provide a wide area coverage, say 100 by 100 km, associated 
with a high sampling density (20 by 20 m pixel footprint with 
a 5-look compression). This property is illustrated in Figures 
3 and 4. In Figure 3 one may appreciated the wide area 
coverage of the screening analysis, which includes several 
cities and villages over an area of about 340 km”. Figure 4 
shows a zoom of the results of Figure 3 over an industrial 
area of less than one square kilometre. In this case one may 
appreciate the high spatial resolution of the velocity field, 
which allows the analysis of deformation phenomena of 
small spatial extent to be performed. In this case the pixels 
have a 40 by 40 m footprint, since a compression of 10 by 2 
looks was used. It is important to underline that the results 
shown in Figure 3 and 4 come from the same input data and 
the same LS adjustment. The differences are related to the 
scales of the two images and the way the results are 
visualized. In fact, in Figure 3 the deformation velocity field 
is represented in the image space, superposed to an amplitude 
SAR image, while Figure 4 shows a geocoded deformation 
velocity field (ie. a DINSAR product given in the object 
space) superposed to a orthoimage. This last type of 
visualization, which needs a image-to-object transformation 
and hence the calibration of the geometric model, represents 
the key factor to exploit the DInSAR products. 
The second example considered in this work is the 
quantitative analysis of a known urban subsidence of small 
spatial extent, located in the village of Sallent (Spain). A 
portion of the village, which lies on an old pottassic salt 
mine, is subjected to subsidence, which is mainly caused by 
168 . 
water filtration in the salt layers. This area has been already 
studied by DInSAR, see Crosetto et al. (2002) and Crosetto et 
al. (2003). The Sallent subsidence, which affects an area of 
less than one km?, was analysed using two ascending and 
descending SAR datasets, in order to derive two independent 
estimates of the same deformation field. The two datasets 
cover the same period, from 1995 to 2000, and include 14 
ascending and 13 descending ERS interferograms. The two 
geocoded mean velocity fields, superposed to an orthoimage 
at scale 1:5000, are shown in Figure 5. One may notice that 
the two fields show a quite similar pattern. There are small 
differences in their area coverage, which are mainly due to 
the different image acquisition geometries. The quantitative 
comparison of these results is described in Crosetto et al. 
(2003b). In general, there is a good agreement between the 
two estimated velocity fields: despite the small number of 
interferograms (13 for the descending dataset) the obtained 
results show a good consistency. A further step in the 
analysis of this subsidence will be the estimation of its 
temporal evolution, by fusing the observations coming from 
the ascending and descending datasets. 
4. CONCLUSIONS 
The DInSAR technique can provide deformation 
measurements with a quality that is comparable with that of 
the traditional geodetic techniques. This capability, which 
can only be achieved by implementing advanced DInSAR 
processing and analysis procedures, is associated with three 
other important features of this remote sensing technique: 
the wide area coverage, the high spatial resolution, and the 
availability of large historical SAR datasets that for the ERS 
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