mages with 
Figure 12. Geocoding VNIR mosaic with marked samples. 
The samples show approximately the same area on every image 
from which they have been taken for monitoring radiometric 
and geometric deformation during mosaicking and improvement 
of geometrical relationships during geocoding. The samples I 
à and III have been token into consideration because I sample was 
] ; unchanged and sample III had the biggest deformation in 
/ geometric and radiometric domain because that sample is the 
d furthest from the first image in mosaic. 
(green) and 
  
  
  
geometrical 
t has to be 
garding to 
ing. VNIR 
id infra red 
nosaics the 
ing mosaics 
' same area 
  
  
  
  
  
  
Wea Giz Leve 
ftus io 
Median 57 Panik 
bear CU iet 
Sales HAT Count 
Meter 53 Fercurtin 
Pus SEE Faust GENES 
Carne iew f Xaelewe ! 
  
Figure 13. Comparison between histogram of part of original 
VNIR RGB image (up left — I sample, up right — III sample), 
histogram of the same area, not geocoding RGB mosaic (in the 
middle left — I sample, in the middle right — III sample) and 
histogram of geocoding RGB mosaic (down left — I sample, 
down right — III sample). 
  
  
  
  
Figure 14. Comparison between histogram of part of original 
TIR image (left) with histogram of geocoding TIR mosaic 
(right), sample corresponds to III sample from VNIR mosaic. 
  
      
  
SAMPLE 
    
NUM. OF PIXELS MEAN  ST.DEV. MEDIAN 
     
    
          
    
VNIR 
] — image 460x370 61.99 8.16 62 
| — mosaic 460x370 57.52 7.16 57 
1-geo mos 700x538 57.24 7.04 57 
   
  
   
       
   
  
  
   
    
   
   
   
   
   
   
  
    
    
   
    
   
    
  
  
  
  
  
   
    
  
  
   
  
  
   
  
III-image 442x502 67.46 12.27 66 
[[I-mosaic 398x546 64.04 12.05 63 
[II-geo mos 601x695 64.07 11.47 63 
TIR 
III-image 128x128 193.94 22.16 196 
lil-zeo mos . 512x512 192.78 22.51 195 
This data show that the information on TIR image change a 
little as related to original image, in spite of fact that number of 
pixel have significantly increased by geocoding, and the 
differences on VNIR images are more significant and their 
influence can be visible on shape of histogram and MEAN and 
MEDIAN values between original image and geocoding 
mosaic. 
S. CONCLUSION 
Mosaics which were produced from digital images of 
DuncanTech MS3100 and Thermovision THV 1000 are useful 
information for the orientation on snapshot area and give 
possibility for radiometric analysis of data on entire snapshot 
area at the same time. Geometric deformation of details can be 
improved by geocoding. The mean value of deviation of details 
on VNIR and TIR geocoded mosaics related to DOF show that 
fact, that it is possible to make joint radiometric analysis (i.e. 
the classification which can be done because the pixel size is the 
same on both mosaics) with biggest tolerance of results. The 
deviation of VNIR geocoding mosaik regarding the model 
(DOF 1:5000) are fully satisfying geometrical accuracy of DOF 
1:5000 ((2) and (4)), because there aren’t any deviation larger 
than 1.5 m on both axes. Standard deviations of TIR mosaic, 
regarding the model (DOF 1:5000), are also within the accuracy 
of DOF 1:5000 ((1) and (3)), but 4 points have y coordinate and 
6 points have x coordinate deviation bigger than 1.5 m. In this 
case one should take into consideration the fact that the 
radiometric data VNIR of geocoded mosaic differ much from 
their original copies that they have been made of, than it was the 
case with TIR geocoded presentation. Base for geocoding was 
DOF 1:5000 with pixel size of 0.5 m. If we had better base for 
geocoding (smoller pixels of base image), geometric 
transformation would give better results. For the radiometric 
analysis it is necessary to find the adequate methods of 
comparison.