International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
with an incidence angle of 27 degrees). The second corresponds 
to the case of a reference map (Michelin Map) and a historic 
map (Cassini map of 1756). The local approach guaranties a 
residual value of 0 for the tie points used by the process. Table 
1 shows the errors (mean, minimum and maximum values) 
estimated using control tie points. A considerable improvement 
through the use of the local correction is noted due to the fact 
that the local deformation is usually smooth. 
Polynomial 
approach 
Polynomial + local 
approach 
Mountainous area 
(34 control tie 
points) 
19.5,0.5,46.6 
9.1,0.7,28.3 
Cassini map 
(22 control tie 
points) 
9.1,4.8 , 19.9 
6.2,1.1 , 13.1 
Table 1. Coregistration errors (mean, minimum, maximum) for 
two methods (in pixels). 
When a DEM is available (only required for the master image), 
we assume that the correction to be applied is a function of the 
height (see Eq. 4). 
dxi = a Zj + b (4) 
dy ; = a’Zj + b’ 
The coefficients in Eq. 4 are estimated using the set of tie points 
(weighted in the same way as previously). 
When a DEM is available, this approach can provide more 
accurate results. For instance, in the case of the mountainous 
area, the error decreases from 9.1 to 1.3 pixels (see Table 2). 
Polynomial + local 
approach with DEM 
Mountainous area 
(34 control tie points) 
1.3,0.2,2.6 
Table 2. Coregistration errors (mean, minimum, maximum) 
when using local corractions and a DEM (in pixels). 
2.3. Use of Linear Features 
Another important characteristic of the GEORIS software is the 
capability to use, in addition to the tie points, selected linear 
features (road, railway, power line, shoreline, pipe, etc.) to 
compute the global and local models. These features are directly 
pointed by the operator (depending on the spatial resolution and 
the positioning accuracy to be reached, the features should be 
carefully pointed at, e.g. either the road strip or one of the edges 
of the road or the centerline of the road). When pointing a linear 
feature (i.e. road strip), it is not required that the beginning and 
the end of the linear feature have the same location in both 
images to be registered; only a common segment is needed. The 
linear features can be used either to calculate the registration 
error (from the results of matching of the linear features after 
the global and local models are applied to the image to be 
registered) or to compute the models themselves by using 
thousand of tie points which are automatically generated along 
the selected linear features. 
The registration error is estimated using a statistical approach. 
For each point of a considered feature, the model is applied and 
we search the nearest pixel of the corresponding feature in the 
other image. The distance obtained is used as an estimate of the 
matching accuracy. To limit matching errors, the matched pixels 
which involve the end points of the feature are ignored. 
This very simple approach permits to benefit in many cases, 
e.g., if only one straight line feature is available, it could be 
used by the system. This method is especially interesting to 
provide an estimation of the accuracy of the model without 
needing a large set of control tie points and with a better spatial 
distribution. Table 3 shows results (mean error) obtained for the 
previous examples. 
Polynomial 
approach 
Polynomial + 
local approach 
Polynomial + 
local approach 
with DEM 
Mountainous 
area 
(ca. 5500 points) 
8.3 
3.8 
1.0 
Cassini map 
(ca. 2800 points) 
3.7 
3.4 
- 
Table 3. Mean coregistration error (in pixels) using feature 
points as control tie points. 
In this case, linear features are used by the system as control tie 
points. Another mode that can be requested from the system is 
to use linear features as tie lines in order to improve the model. 
This function is implemented by an iterative process which 
automatically adds new tie points along a feature. This approach 
permits the user to visualise and control the tie points added by 
the system. 
3. GEORIS TOOL 
To meet the user and performance requirements specified, an 
exploitation procedure has been implemented to guide the 
operator step by step: select the zones of interest; catch the 
common features; calculate the registration model parameters; 
assess the positioning accuracy; apply the registration model; 
archive and export the registered images together with the 
registration models and the feature attributes. 
On-line easy-to-use tools are available to support the operator to 
draw precisely the features with scrollable zoom, improve the 
image contrast, adjust the position of ground control points and 
assess the relative positioning accuracy of any pixel in the 
image to be registered. 
As GEORIS is dedicated to prepare a set of images for photo 
interpretation tasks, an export facility has been developed to 
allow either projection of the raster and vector data in a selected