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(Clark, 1991). Chen and Dowman (1996) proposed an analytic approach to carry out space intersection using the least- 
squares adjustment for ERS-1 data and reported results without using any GCPs, if good quality orbit data is available. 
Further consideration and refinement for the geometric model to be applied to the SAR data with inferior quality orbit 
data, such as RADARSAT SAR imagery, has been carried out (Chen, 2000). Image correlation is one of the key steps 
from analytic to digital radargrammetry. Pyramidal correlation strategy using the least-squares correlation method with 
a region-growing approach has been proved useful to generate a parallax file from SIR-B SAR data, (Denos, 1992), and 
a DEM can be generated from ERS-1 images (Twu, 1996). An optimized strategy for giving the pyramidal structure has 
also been developed (Dowman and Chen, 1998). 
Providing ground control is indeed a problem in digital radargrammetry. Manual operations for collecting GCPs from 
radar images and maps are usually limited by visual image-map correlation, and are not always stable hence the GCP 
quality cannot be assured. In order to cope with this problem, SAR image simulation techniques have been used for 
provision of ground control in geocoding, for instance, Kwok ef al. (1990) and Guindon (1995). The accuracy of the 
automatically provided ground control relies on the quality of the simulator as well as on the outcome derived from the 
real-simulated SAR image correlation. Poor quality simulated images may lead to poor results of real-simulated image 
correlation. Previous work shows that most of the currently available SAR image simulators still require many human 
interventions. A SAR image simulator is designed to provide ground control by using four corner elements of a known 
DEM chip, according to a SAR imaging model to relate the DEM sensed and the SAR image , provided that a small 
DEM chip with significant terrain relief is available (Chen, 2000). 
This paper describes the use of the DEMs derived from stereo RADARSAT SAR data and the automatically derived 
GCPs for geocoding to achieve a higher level of automation in radargrammetry. Section 2 reviews the algorithms 
developed briefly. The detailed descriptions of the algorithms proposed in this paper are given in Chen (2000). In 
section 3, the accuracy of the DEM derived and the GCPs generated automatically are verified. The geocoded SAR 
images derived, as shown in section 4, are used to verify the proposed algorithms. 
2 ALGORITHMS 
2.1 Automatic Generation of DEMs from Stereo SAR Data 
Generation of spatial data from a pair of stereo SAR images requires a geometric model and an image correlation 
method. An ideal geometric model for extracting spatial data from a SAR image pair is expected to obtain high quality 
results, to require a minimal number of GCPs and human interventions. The range/Doppler equations can be used as 
observation equations in a least squares adjustment for solving ground points from ERS data, (Chen and Dowman, 
1996). Refinement of the algorithm has been made using a weighting matrix to cope with inferior orbit data provided by 
RADARSAT SAR imagery in order that the effects from the azimuth timing error on the results derived can be 
minimized. GCPs are not required in the geometric model. Systematic correction can be made using only two GCPs 
after space intersection. An area-based least squares correlation method, (Gruen, 1995), with a region-growing 
approach, (Otto and Chau, 1989), is adopted to generate a dense coverage of elevation data set. This method has been 
refined using a pyramidal harness to propagate control from the top to bottom image tier hence the effect of speckle in 
SAR imagery can be reduced, (Denos, 1992). An optimized strategy of parameter determination for commanding the 
pyramidal structure has been proposed to improve the level of automation, provided that the maximal parallax of the 
image pair to be correlated can be measured or calculated using a coarse DEM, (Dowman and Chen, 1998). 
2.2 Automatically Generated Control 
In order to reduce manual operations, and then to stabilize the quality of GCPs, a SAR image product simulation 
method is proposed to automatically provide ground control for radargrammetry. The proposed simulator is based on 
the assumption of the perpendicular relationship between the resultant range and velocity vectors in SAR imagery. A 
known DEM chip of a small area with varied terrain surface and the header/orbit data of a real SAR image are required. 
Also, it is assumed that the structure of a real SAR image is well defined by the header/orbit information allowing 
automatic detection of the extent of the simulated area without human intervention. The geometric considerations for a 
SAR image simulator include the simulation of the image structure of a real image with no manually defined parameters 
required, such as the range and azimuth time offsets for a sub-scene SAR image and incidence angles for individual 
pixels. In terms of radiometry, a simple reflectivity model is applied to the entire simulated area without regard to 
speckle or different ground cover. The reflectivity model is defined as the scalar product of a range vector from the 
sensor to a DEM element and the outward surface normal of that DEM cell of interest. This simplification cannot be 
avoided because the detailed ground truth data of an arbitrary real image, which affects the reflectance of a radar wave, 
is not always available. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part Bl. Amsterdam 2000. 39