The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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grid node can be re-validated as well. The full process may be 
re-iterated as needed. 
After the last iteration, each image has its own radiometric 
polynomial model (eq. 1) and cloud masks can be extracted 
from invalid points. 
3. IMPLEMENTATION AND TESTS 
3.1 Implementation 
3.2.2 Influence of parameters: Radiometry is extracted 
every 1000 m from sub-sampled images at 100m. To speed up 
the process, images are saved in a pyramidal format so that 
radiometry is interpolated directly in the right pyramid level. A 
grid node is ignored if it covers global mask and invalidated if 
its calculated radiometry is superior to initial threshold (180). 
Here, we get 412143 valid radiometric values to set up the 
global equation system. Degrees of polynomials P and Q are set 
to 1, so radiometric model for a given image I is as follows (eq. 
6): 
This algorithm has been implemented in GeoView Software, a 
photogrammetry and remote sensing suite developed in-house 
at IGN-France. We focused on SPOT5-HRS images but his 
module works also with any type of ortho-images or raw 
images with location modelling. It also works with 
multispectral images. In this case, each channel is 
radiometrically adjusted independently. 
3.2 Test case over Algeria 
3.2.1 Dataset overview: 9 SPOT5 HRS stereo-pairs 
covering a part of Algerian territory are used to test previously 
described process of radiometric block adjustment. It is a 
medium size dataset that can be considered as quite easy and 
favourable because: 
• There are large overlapping areas between images 
(20-25%), 
• Interval between first image taken on 09/08/2002 and 
last image taken on 26/10/2002 is very short, less than 
3 months, 
• Very few clouds are present in images. 
Radiometric adjustment is done on ortho-images calculated 
from back images of HRS stereo-pairs. These ortho-images are 
very large, up to 57474 columns by 115981 rows, their pixel 
size is 5m and the whole dataset covers 6° of longitude by 8° of 
latitude (530 km * 970 km). A global mask over Mediterranean 
Sea is added. 
Figure 1. SPOT5 HRS images over Algeria - Dataset overview 
FinalRad(oolro\ty= [l -col+bj ■ row+C/]• InitialRadfcolran} 
-\\dj • col+ej ■ row+fj ] 
There are 6 unknown values by image, 54 for the whole system 
that is therefore very redundant. Role of every parameter has 
been studied but let’s focus on punctual constraints influence. 
With Gpi=aQi=a the value used to weight punctual constraint 
equations (eq. 3), Table 1 and Figures 2, 3 show huge influence 
of these punctual constraints on final radiometry. With o=10, 
RMS of radiometric differences on overlapping area drop from 
26.5 numerical counts to 4.4, final mosaic is seamless and 
image dynamic is kept. Whereas, with o=0.1 and o=l, the 
system is too rigid, final radiometry close to initial one and 
seams remain visible. With o=100, final image dynamic is very 
poor. Standard deviation of radiometric values stored in the grid 
drops to 3.3, too much information has been lost. 
% valid 
nodes 
Grid average 
radiometry 
Grid std 
dev 
Residual 
RMS 
Initial 
99.1 
100.2 
25.9 
26.5 
o=0.1 
98.5 
100.2 
17.2 
13.5 
0=1 
98.1 
100.2 
14.7 
7.8 
o=10 
93.8 
100.2 
11.1 
4.4 
o=l 00 
93.8 
100.2 
3.3 
0.5 
Table 1. Influence of punctual constraints 
Figure 2. Influence of punctual constraints on final radiometry