The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
launch. These activities cover many topics gathered in two 
families : radiometric and geometric Image Quality. 
Radiometric activities concern the absolute calibration, the 
normalization coefficients computation, the refocusing 
operations, the MTF assessment, the estimation of signal to 
noise ratio and also the tuning of the ground processing 
parameters in order to fit the images to the users needs. 
Geometric activities deal with the geometric model calibration, 
the assessment of localization accuracy, focal plane cartography, 
multi-spectral and multi-temporal overlapping, static and 
dynamic stability, planimetric and altimétrie accuracy. 
These operations require specific control of the payload and, for 
some of them, dedicated guidance of the satellite platform. The 
new capabilities offered by Pleiades-HR agility allow to 
imagine new methods of image calibration and performances 
assessment. Two of them are described here. 
2. NORMALIZATION CALIBRATION ON NON 
SPECIFIC LANDSCAPE 
2.1 Objective 
The aim of normalization is to correct raw images for relative 
inter-detector sensitivities, so that a uniform landscape gives a 
uniform image. Normalization residuals may cause vertical 
stripes. 
Where : C(j) is the dark current of detector j (L=0) 
g(j) is the relative sensitivity of detector j 
A is the absolute calibration coefficient 
With a linear model, normalization function F can be easily 
performed to get the normalized digital number Y as a function 
of X : 
YG,L) = F (X(j ,L),g(j ),C(j)) = [X(j,L)-C(j)]/g(j) = AL (2) 
This relation shows that normalized images are proportional to 
input radiances. 
Dark currents C(j) are computed thanks to specific images 
acquired over the oceans when the satellite is in the dark (night 
orbit). To make it simple, starting from this section, only Z=X- 
C will be used in the normalization model. 
Because high resolution optical satellites like Pleiades-HR have 
to face a lack of signal, which may move the useful signal 
range towards the non-linear part of the detector response, 
normalization may have to be run with a non-linear model. 
Considering the computational constraint, we use for F a 
piecewise linear function designed to fit the detectors relative 
responses : 
Y(j) = al(j) Z(j) ifZ(j)<Zs(j) (3.1) 
Y(j) = a2(j) (Z(j)-Zs(j))+al(j)Zs(j) if Z(j) > Zs(j) (3.2) 
Figure 4 : Pleiades HR normalization model 
The calibration consists in computing for each detector j, the 
triplet p(j)={al(j), a2(j), Zs(j)} used in the normalization 
function F / Y(j) = F(p(j), Z(j)). 
2.3 Resolution principle 
In this non-linear case, we need for each detector the response 
to different radiances. Then the parameters are computed in the 
least-squares sense. 
Given N different input radiances, let us define Z(kj) the 
corresponding response of detector j where index k points to 
the input radiance value. For each radiance index k, the average 
response of the whole line is YM(k). The unknown triplet is 
determined in order to minimize the following least-squares 
criterion LSC(j): 
Figure 2 : Raw image 
Figure 3 : Normalized image without residuals 
2.2 Radiometric model and normalization function 
When the camera observes the top-of-atmosphere radiance L, 
the output digital number X(j,L) delivered by detector j is 
modelled by the following relation: 
(1) 
LSC(j) = £ 
fc=l 
/ YM(k)-F(p(j\Z(k,j)) V 
y <r(Kj) y 
(4) 
where cr(kj) weights can be used to balance the residuals 
according to the signal level. 
X(j,L)=A.g(j).L+C(j)