may offer a sensitive measure for the distinction 
of extreme similarity in the primitive feature 
space. For the Least Squares Matching method, 
although the approach is completely different 
(i.e. not searching / comparing and selecting, but 
simultaneous Solution / determination of the 
unknowns), the basic idea is similar, namely, 
matching with very high accuracy to the degree of 
subpixel. This also has the advantage that Least 
Squares Adjustment is flexible which allows the 
users to perform matching with more than two 
images which can increase accuracy and reliabil- 
ity, and offers the theoretical quality estimation 
of the result as well. 
3. THE SYSTEM COMPONENTS OF THE APPROACH 
3.1 Preprocessing 
3.1.1 Coordinate System Transformation 
a) For mapping purposes, the coordinates of Ground 
Control Point (GCP) are offered in the Mapping 
System, because a SPOT image covers an area of 60 
km x 60 km, hence the effect of the earth curva- 
ture can not be neglected any more. Therefore, a 
transformation from the Map Coordinate System to 
the Geographic System, and further to the Geocen- 
tric System is necessary. However, as the number 
of digits of coordinates in the Geocentric System 
is large, and ‚a Double Precision is required in 
the data process to avoid truncation error, it is 
necessary to transfer the coordinates to Local 
Tangent Plane System to reduce the number of 
digits, and thus to save memory and speed up processing. 
b) For using the on board data which are in the 
Geographic System / the Geocentric System / the 
Local Orbit Reference System / the Local Attitude 
Reference System, transformation to the Local 
Tangent Plane System is needed also. 
3.1.2 Region Matching for DEM Generation and 
Change Detection Because the matching would fail 
within the homogeneous intensity region, or in the 
regions which the land cover has been changed when 
the satellite stereo pairs are taken with a long 
interval of time. Therefor, we use Conditional 
Rankorder Operator to smooth the intensity within 
the region first, then start Region Growing for 
image segmentation, the boundary of region can be 
extracted, and the shape can be described by y-s 
Curve, combine with other properties of region, 
such as the area, the position of gravity centre, 
etc., to form a Property List. The initial prob- 
ability of region matching can be obtained by 
minimum cost function with the weighting prop- 
erties in the list. Then the matching probability 
are being adjusted by Relaxation Processes until 
the final conjugated region pairs are determined. 
The elevation of the region can be calculated with 
the conjugated region, and the change detection 
can be done by checking the mismatching regions 
and comparing the intensity between the conjugated 
region after region matching, the matching failure 
problem can be solved in these area [Lo,1992]. 
3.1.3 Aerial Triangulation ( SPOT Orbit 
Determination ) 
a) On board data are used to define the overlap 
area of multi-view images and to choose the Tie 
Points at the proper position and evaluate / 
select the specific image properties (e.g. high 
contrast in X and Y direction) i.e. the most 
suitable features for matching required for auto- 
matic point transfer. After image segmentation is 
performed, the crossing of lines / edges are 
detected as GCP, and sufficient properties (struc- 
ture representation) of GCP can be obtained for 
GCP identification by the Line-Based Structure 
Matching Method or Chain-Coding Matching.  More- 
over, on board auxiliary data. and ground 
coordinates of GCP are also used to help automatic 
identification of GCP with property list for the 
correspondence analysis between maps/photos. 
b) Establish the model for simulating the short 
arc orbit of SPOT as a function of time [Konecny 
et al.,1987] [Kratky,1988]. 
c) Extract on board data from CCT of SPOT [SPOT 
User's Handbook, 1988] and use them as constraint 
(e.g. the attitude data) by the Pseudo Observation 
134 
technique, in order to solve the problem of high 
correlation between orientation parameters caused 
by narrow FOV of SPOT (4.125 degrees only) [Chen 
& Lee, 1989] [Shibasaki, el at.,1988]. At the same 
time, we try to reduce the number of unknown orbit 
parameters (e.g. simulating the orbital model for 
position by 2nd degree polynomial, and linear 
polynomial for attitude) resulting in a reduced 
number of GCP, and aim at finding out the best 
distribution of position of GCP in the adjustment 
of A.T. providing sufficient accuracy for later 
matching. 
d) Application of Object Space Least Squares 
Matching with exterior orientation parameters as 
unknown; using on board auxiliary data as initial 
value, try to perform highly accurate Tie Point 
Transfer with interaction in adjustment of A.T. by 
iteration. This is the most difficult part to 
solve, because the known exterior parameters are 
the back bone of Object Space Least Squares Match- 
ing. If we treat them as unknown with on board 
data as initial values instead, we need to know 
how good the initial value should be to make the 
iteration convergent. 
e) Empirical accuracy study of exterior orienta- 
tion parameters from A.T. with the application of 
previously mentioned techniques, trying to get 
sufficient accuracy to meet the requirement of 
Object Space Least Squares Matching. 
3.2 Coarse DEM Generation by Correspondence 
Analysis with Property List 
a) There are several methods to enhance the Linear 
Features ( Line / Edge ) and then extract them; 
however, the original position and intensity of 
linear features should not be changed if the 
features will be used for matching (not for visual 
satisfaction) later. Therefore, a non-linear 
filter, such as Conditional Rankorder Filter 
[Mulder & Sijmons, 1984], can be selected for 
segmentation; thereby the enhancement of features 
is done by smoothing the background (suppressing 
the minor features / noise also) and keeping 
distinguished Linear Features. in their original 
situation. 
b) Reduce the 2-D search to a 1-D search during 
matching, by the resampling of image data into 
parallel line pairs (the approximate Epipolar Line 
pairs). This is, however, more difficult to apply 
to SPOT images, because the orientation parameters 
are a function of time [Otto,1988][Zhang & Zhou, 
1989]. 
C) Apply a Gradient Filter to the image and detect 
the Linear Features with Zero-Crossing. 
d) Property List Formation by collecting the 
properties of Linear Features such as Position, 
Amplitude and Shape of peak / valley in intensity 
profile along the parallel line pairs [Lo,1989], 
and the Orientation of Linear Features [Kostwinder 
el at,1988]. 
e) To offer the criterion for Correspondence 
Analysis, Cost Function Modelling is required by 
assigning different weights to the individual 
properties according to it's reliability and 
major/minor contribution to express the character- 
istic of feature. The weight can be assigned by 
prior analysis or by experiment with Trial and 
Error. 
f) Between the conjugated parallel line pairs, the 
corresponding linear feature can be extracted by 
String Matching which selects the Minimum Cost as 
best matching, based on information from the Cost 
Function. For the conventional matching strategy, 
the Target Area of the left image is selected to 
search for the best match in Search Area of the 
right image only; the result may be different, 
however, if the matching is from right to left. 
The String Matching uses the mutually matching 
strategy which matches not only left to right but 
also right to left, then selects the real Minimum 
Cost among them as best matching with the marking 
technique for extracting them. It increases the 
reliability of the result. If we confirm the 
extracted linear features again by checking the 
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