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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part Bl. Beijing 2008
Figure 3 shows the results of this method: the 2 images before
correction and after and another image of the same landscape
at an other day as reference. The results led to a good
correction, allowing nice-looking images but not sufficiently
accurate: with a sought signal amplitude of 3pixels , it
remained a 0.1 pixel residual RMS error.
Figure 3. Image with instabilty on the left, corrected (middle)
and another image without vibration
Moreover, this method based on a single image couple is
inadequate for the following cases: when the disturbances
model is unstable, when clouds are widely present, when there
are inaccuracy on partial differential calculation and remaining
ambiguities between DTM / Pitch / Line of sight error.
This results showed however that a similar process can be
efficient using several differentials, low B/H ratio,another
integration method, and without VLF errors correction but only
MF, HF characterisation. That effort was continued in a
research and development work with Astrium in 2003 and then
a processing unit had been built by the CNES, which is
presented in this paper.
3. METHODOLOGY
3.1 Overall process
So the main issues related to attitude improvement on image
processing are:
• to measure the geometric significant disruptive
signal frequencies,
• to separate disruptive microvibrations effects from
MNT effects and from correlation noise and errors,
• to calculate the absolute signal correction using
several differentials inputs without using too much
analytical modeling hypothesis
• to apply quickly and automatically the corrections
on an image.
To reduce the measurements noise and errors, we avoided false
correlation using a a-priori criterion, we then post-filtered
results by synthesising them on each line of each image couple
assuming a parallelism of retinas, The vibration is then
obtained by synthesising at time t the attitude differentials of
the various couples in relation to a "not rigid" vibrations
model.. This need of filtering through a model is paramount,
this model depends on the satellite and its disturbances.
However, these data pertain only geometric aspects observable
on images. First, microvibration frequencies above the
correlation cut-off sampling frequency can never be corrected
(Shannon theorem) and aliasing effects are possible. Second,
because the method is based on expected (calculated) shifts
between two images, the bias, drift, and VLF signals
perturbing the system flight cannot be retrieved, as we would
not be able to estimate if these observed signals, for example
estimated on very long record length, are real or are coming
from the measurements method itself. Third, if the differentials
are not independent some frequencies are blind or heavily
depreciated.
The processing is generic and will be conducted in 4 stages as
shown in Figure 4.
Couple 1 Couple 2 Couple n
Figure 4. Algorithm flow for instability correction
The first step computes co localisations prediction (with or
without MNT) and correlation masks to focus on the relevant
points, then measures shift between images by correlations. It
takes advantage of the various satellite retinas to increase the
information at the same moment: it has several pairs of
correlations. This first step gives, for each couple,, n line-shift
and n column-shift temporally sampled on each correlation line.
The following steps correspond to the heart of the system: the
interpretation of these shift measurements and their
transformation into attitude correction signals. First, the
instant synthesis works on the shifts in the correlation line and
obtains a roll and pitch differential AR(t, xj) ) & AT(t,xj) in
assumption that the two retina of couple are almost
parallel and separate of xj.
Second, the temporal integration of the attitude differentials
estimates the absolute value of the disturbance at each
correlation line. In the overall process, it is defined as a plug-in
which depends on the satellite and available disturbances.
Several methods are possible, they must be robust to inputs
gaps and noise. A local innovative method has been developed
with assumptions that the vibrations are PHR system-like:
quasi harmonic signals and almost stationary with
homogeneity of differential (same convolution, spatial
orientation...).
An iteration is possible in order to improve the accuracy of the
results, taking into account the estimated vibration to refine the
instant synthesis and compute again the correction signal.