International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
Figure 1. Artefacts.
All images were found to exhibit artifacts, which were visible,
especially in homogeneous area and/or after strong contrast
enhancement. Stripes in flight direction due to imperfect
calibration of the sensor elements. Strong reflections in both
PAN and MS images, which lead to saturation of the signal and
loss of information. Spilling (Fig. 1(a), IKONOS, 1(c) QB) of
bright target response in neighbouring lines in the flight
direction, visible almost exclusively in the PAN images and
blooming (Fig. 1 (b), IKONOS). Spilling is probably the most
grave radiometric problem, as it destroys image information
and may confuse subsequent feature and object extraction. It
increases with smaller pixel size and with smaller angle
between the line-of-sight of the sensor and the reflected sun
rays. It is pronounced because of the TDI and increases when
more TDI stages are used. It is apparent that with bright targets
the respective TDI pixels are saturated and the excess signal is
not properly discharged, influencing subsequent lines. QB has
much more spilling (more often, longer and wider) due to its
smaller pixel size but also due to its continuous rotation during
imaging. In Geneva, QB had 135 artifacts compared to 10 and
18 for IKONOS East and West. Ghosting of moving objects
(Fig. 1 (d), QB) is visible in pansharpened images, due to the
time difference in the acquisition of the PAN and MSI images.
Another factor influencing image quality are shadows. In both
IKONOS and QB images, most shadowed areas (especially in
urban areas) did not have significant signal variation, even after
strong contrast enhancement. However, in the winter images of
Thun, very large open shadowed areas of mountain cliffs
covered by snow could be enhanced quite successfully.
2.2 Image Preprocessing
In order to improve the radiometric quality and optimize the
images for subsequent processing, a series of filters are applied.
The performed preprocessing encompasses noise reduction,
contrast and edge enhancement and reduction to 8-bit by non-
linear methods. All filters are applied to the 11 bit data.
Noise reduction filters aim at reducing noise, while sharpening
edges and preserving corners and one pixel wide lines. The two
local filters employed have similar effects although they use
different parameters (Baltsavias et al., 2001). In Fig. 2, the
Adaptive Edge Preserving Weighted Smoothing is compared to
a Gaussian filter.
Be
Smoothing.
Apart from the visual verification, reduction of noise was
quantified by noise estimation in inhomogeneous areas.
Comparing Tables 3 and 4, a reduction of noise by a factor of
about 2.5 - 3.0 and 1.8 for PAN IKONOS and QB, respectively,
is estimated.
Figure 2. Effect of filtering: (left to right) Original image,
Gaussian 5x5 filter, Adaptive Edge Preserving Weighted
PAN 0 -|128-|256-| 384- | 512 — | 640 — | 768 —
Scenes 127 | 255 | 383 511 639 | 767 | 895
Geneva I - 1.04 | 1.01 1.17. | 1.21. ]. 1.84 1.1.90
Geneva Q | 0.80 | 0.86 | 0.89 | 0.88 | 0.82 | 0.98 | 1.24
Thun 0.53 | 0.54.) 1.56 12,55 - - -
stereo
Thun 0.541076 | 081. 1.00 1 1.36 | 1.94 -
triplet
Table 4. Noise level in inhomogeneous areas and different grey
value ranges (bins) for IKONOS scenes, after noise reduction.
Following noise reduction, local contrast enhancement is
applied using the Wallis filter. Moreover, 11-bit data are
reduced to 8-bit by an iterative non-linear method in order to
preserve the grey values that are more frequently occurring.
Two different approaches are implemented, one with flat
frequency of output grey values and one with Gaussian form
frequency, the latter being applied here. The improvement of
the image after preprocessing is shown in Fig. 3.
Figure 3. IKONOS image before (left) and after (right)
preprocessing.
3. IMAGE ORIENTATION
3.1 Methods and Sensor Models
With the supplied RPCs and the mathematical model proposed
by (Grodecki and Dial, 2003), a bundle adjustment is
performed. The model used is:
X+Ax=x+a, +ax+a,y= RPC, (p,À,h)
y+AY=X+b +bx+b,y = RPC, (p, À, h)
where as, aj, a; and b, b;, b; are the affine parameters for each
image, and (x, y) and (q. A, /) are image and object coordinates
of points.
Using this adjustment model, we expect that a, and 5, absorb
most errors in the exterior and interior orientation. The
parameters a;, a», b, b» are used to absorb the effects of on-
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