3. Istanbul 2004 
for removal of 
cking in ISAT. 
)W POINTS 
in the image, 
es. The most 
ent around the 
1 abrupt change 
dow is cast by 
od for detecting 
based on these 
ts 
d the first is the 
shadow usually 
| objects, points 
gradients. The 
ted as shown in 
gradient of the 
gonal directions 
(1) 
itensity gradient 
onal directions. 
calculated, it is 
t is selected as a 
> threshold. 
buildings. There 
x buildings that 
be detected by 
area around the 
Model (DSM): 
around the point 
The area should 
1adow. Once the 
are matched by 
area may not be 
g is used. The 
uted by forward 
nages from first 
: a local DSM is 
^d to the created 
a small area is à 
ibrupt change in 
, buildings, etc. 
rst-order surface 
squares method. 
The standard deviation of elevation difference between points 
and the fitted surface is then computed, which represents the 
change of terrain slope in the area. When the standard deviation 
is larger than the given threshold, it can be determined that 
there is probably a tree or a building in the area and point is 
removed as a shadow point. 
Elevation 
4—— A tree 
o». 
Lad 
Figure 3. Slope Change in the Area with Trees 
4. SOME EXAMPLES 
The developed approach has been tested within Z/I Imaging's 
ISAT and some test results are presented in this section. The 
project presented in this paper consists of 4 strips and each strip 
  
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(c) 
  
    
  
    
    
   
    
     
     
     
   
   
   
  
    
    
   
   
    
   
     
   
   
  
   
   
  
  
   
  
  
  
  
  
  
  
  
  
  
  
  
  
   
   
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
has 7 images, taken with analog aerial camera. The images have 
a scale of 1:6800 and were scanned at 14um. There are a lot of 
trees and shadows in the project area. The project was run with 
and without the function of removing shadow points. Figures 
4(a) and 4(b) show the tie/pass points of an image in the project 
generated by ISAT before and after removal of shadow points 
respectively. It can be seen that plenty of tie/pass were created 
cross the image before removal of shadow points, among which 
some are good points, such as corners of sidewalks. But, many 
shadow points and points on top of trees were generated at the 
same time since the main features in this area are houses, large 
buildings, trees and shadows cast by these objects. With the 
function of removal of shadow points, most shadow points were 
removed successfully. Figures 4(c) and 4(d) show the points in 
a small area (black boxes) before and after removal of shadow 
points. Totally there were 12 tie/pass points generated in this 
area, seven of which are shadow points. After removal of 
shadow points, all shadow points were removed and there were 
eight points generated, as shown in Figure 4(d). Almost all of 
these points are new points since the algorithm removed some 
non-shadow points incorrectly and added some good points 
automatically. Another example is shown in Figure 5. 
  
  
(d) 
Figure 4. Automatically generated Tie/Pass Points (a), (c) extracted tie/pass points before removal of shadow points, (b), (d) 
extracted tie/pass points after removal of shadow points 
93