ables on the 
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rovement of 
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> obtained as 
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the natural 
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(10) 
he possibilty 
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All described 
were imple 
ige all trans 
Some of the 
| 
| 
I 
| 
results (three PTC-images) as well as the original CIR- 
input image, the TC-(reference)input image and the class 
map of the classification are illustrated in colour within 
these proceedings. 
4. RESULTS 
The quality control of the results of the CIR- to PTC- 
image transformation and with this a judgement of the 
chosen algorithm was a weak point within the presented 
project: The colour infrared photographs and the 
corresponding reference true colour photographs were 
not taken in the same year, not in the same season, not 
at the same time and not with the same camera ob- 
jective. Due to this fact there are some objects in the 
photographs which are not identical (e.g. there are some 
fields that have different vegetation status). This 
circumstance was considered by delimiting the object 
classes to determine the transformation parameters, but 
it was not taken in consideration by the computation of 
statistical values as presented in Chapter 4.2. So the 
results are made worse. 
The quality of the PTC-image depends on following 
influences: 
= quality of photographs 
= contents of photographs 
= amount of distinguished classes 
= selection of training areas for the classification 
= kind of transformation 
In the presented paper two possibilities of quality control 
are outlined. For one test area CIR-images were trans- 
formed to PTC-images and afterwards compared with 
theTC-image of the same area. When judging the results 
it is to take into account that in this case the CIR and the 
TC photographs were not taken at the same time, as 
noted above. 
4.1 Visual quality control: 
Every visual quality control is subjective: Each person 
has a different preference of contrast, brightness and 
colour balance of a photo and so within this paper only 
the impressions of the authors are outlined in table f: 
  
class class center | classwise 
center | with classwise | (method 
(method | improvement 3.3.3) 
3.3.5) (method 3.3.6) 
  
transformation 
of one band e e 
- (equation 9) 
  
transformation 
of two bands e e 
- equation 7) * 
  
transformation 
of all bands e e 
  
equation 3 + 
  
  
  
  
  
table 1: visual quality control 
Legend of table 1: - bad, e neutral, + good 
473 
4.2 Quality control with statistical values: 
For the quantitative quality control the corresponding 
pixel values of the colour bands of the PTC-image 
(orthoimage) 
and of the 
original 
TC-image were 
subtracted. The mean value of each colour band and the 
corresponding variance are outlined in table 2 to 4. As 
exspected, due to the high degree of freedom, the 
classwise transformation of CIR-pixel values to PTC-pixel 
values gives the best results. 
  
  
  
  
  
  
  
  
class center | class center classwise 
method with classwise method 
335 improvement 333 
method 3.3.6 
Red band -7.4/54.6 -7.4/54.6 -7.4/54.6 
Green band | 30.2 / 48.4 30.2 / 48.4 30.2 / 48.4 
Blue band | 10.1 /38.0 10.0 / 37.9 -3.1/34.0 
  
table 2: quality control with statistical values - transfor- 
mation of one (blue) band (equation 9) 
(mean value / variance of differences between TC and 
  
  
  
  
  
  
  
  
PTC pixel values) 
class cen- | class center | classwise 
ter with classwise | method 
method | improvement 3.3.8 
335 method 3.3.6 
Red band -7.4/54.6 -7.4/54.6 |-7.4/54.6 
Green band 4.7/41.8 4.6/41.8 -3,6/37.1 
Blue band 10.1 / 38.0 10.0/37.9 | -3.1/34.0 
  
table 3: quality control with statistical values - transfor- 
mation of two (blue, green) bands (equation 7) 
(mean value / variance of differences between TC and 
  
  
  
  
  
  
  
  
PTC pixel values) 
class cen- | class center | classwise 
ter with classwise | method 
method | improvement 333 
335 method 3.3.6 
Red band 52/511 5.1/51.0 -3.7/44.0 
Green band 4.7/41.8 4.6/41.8 -3,6 / 37.1 
Blue band 10.1/38.0 10.0/37.9 |-3.1/34.0 
  
table 4: quality control with statistical values - transfor- 
mation of all (red, green,blue) bands (equation 3) 
(mean value / variance of differences between TC and 
PTC pixel values) 
5. CONCLUDING REMARKS AND OUTLOOK 
Producing pseudo-true-colour orthophotos using colour- 
infrared-photographs is a possible compromise of having 
excellent film material for interpretation and getting reali- 
stic colour orthophotos for visualization purposes. The 
presented paper showed a first approach of the determi- 
nation of transformation parameters between CIR and 
TC (resp. PTC) colour space. 
Within the presented project the efford of producing PTC 
orthophotos was very low. As the land use classification 
was part of the contract with the customer, the only 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996