Full text: CMRT09

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 
GCPs Imagery data j 
Computed RPCs 
Image Pre-processing 
Triangulation (Tie Point Measurement & Block Adjustment) 
Image Matching 
Quasi-Epipolar Image 
Object Measurement 
(Manual / Semi-automatic: Mono 
plotting / Stereoscopic) 
CC Modeler (Construct 
3D Models & Texture 
3D Database, DTMs. Orthophotos, Vector 
Data, etc. 
Figure 2. SAT-PP workflow © Chair of Photogrammetry and 
Remote Sensing ETH Zurich. 
4.2 Preprocessing of the satellite data 
Before processing the VHR imagery a contrast enhancement is 
executed as this leads to a more reliable image matching. 
Especially between images of the same area but taken at 
different dates from different orbits large radiometric 
dissimilarities can occur due to different illumination and 
atmospheric conditions, leading to poor matching results. To 
enhance the contrast for each image individually and to equalize 
the radiometric differences between the imagery, a Wallis filter 
was applied (Wallis, 1976). 
The general form of a Wallis filter is given by: 
g W (x,y) = g(x,y)*r ]+ r 0 
r \ 
cs h 
r 0 = bm h + (1 -b-r x )m g 
with g w (x,y) and g(x,y) = filtered and original image 
m c and s g = original mean and standard deviation values 
m h and s h = target value for mean and standard deviation 
c and b = contrast expansion and brightness forcing c lc 
The Wallis filter performs a non linear, locally adaptive contrast 
enhancement. Actually a large kernel divides the image in 
different sections and within each section the local contrast is 
optimized. Applying a Wallis filter on the original images does 
not only result in an enhancement and sharpening of texture 
patterns in areas of low contrast and equal overall contrast but 
normalizes also the radiometry, especially between images 
taken at different dates. The effect of radiometric enhancement 
of very high resolution satellite imagery is illustrated in figure 3 
& 4. The Wallis filter enhances existing texture patterns, 
leading to optimization of the contrast in shadow areas. Note 
that in the shadow rich areas axis-aligned artefacts are 
introduced due to the Wallis filtering. 
Figure 3. Extract of original 11-bit Ikonos image, illustrating an 
area with high buildings. There is very little contrast within the 
shadow areas, leading to mismatches during the image matching 
Figure 4. Extract of Wallis-filtered 11-bit Ikonos image. The 
radiometric filter enhances the existing texture patterns locally, 
leading to optimization of the contrast in the shadow areas. 
Also an adaptive smoothing filter is applied to reduce image 
noise while sharpening edges. As noise is an important data- 
source for mismatches, reducing it further improves the quality 
of the surface model. 
Next to the radiometric enhancement a method for geometric 
normalization was devised. The Ikonos 2002 stereo couple is 
epipolar projected and suitable for stereo applications. As the 
2005 Ikonos image is taken from a different orbit, the images 
are displaced to each other and the internal geometry will be 
slightly different because of the different scan direction. 
Geometric normalization of the 2005 Ikonos image with the

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