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of features (six were sufficient) that were common to both 
images using the 20 m resolution image as “slave” and the 10 m 
image as “master”. Features taken as ground control points 
(GCPs) were line intersections, field boundaries and street 
intersections. The registration process was executed by running 
the interactive ground control point selection program to 
generate the control point file (Essadiki, M, 2004). 
  
Figure 7. SPOT HRV panchromatic image of study area 
A bilinear interpolation was used for resampling, which does 
not introduce geometric artefacts. The differences between GCP 
locations on the slave and master image were submitted to a 
least squares regression analysis that interrelated both image 
coordinates. The resulting residuals were less than one-half 
element spacing in both row and column, denoting that a good 
registration was achieved. Three files were created, representing 
the resampled three multispectral bands that were merged 
afterwards. 
  
Figure 8. SPOT HRV color composite image of study area 
The data are usually enhanced after correction. In order to 
highlight the three classes of spatial features: homogeneous 
areas, edges (boundaries) and lines, a set of feature extraction 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
operators are applied to enhance the image. This operation 
consists in using a 3 x 3 kernel filter (convolution) moved 
across the image which yields a transformed image. Each 
element of a sub-image is processed with its eight neighbours in 
the input image by multiplying all elements by their respective 
weighting coefficients and the results are summed. The result is 
assigned to the central element in the output image. 
Furthermore a saturation enhancement was used to improve the 
image interpretability (Essadiki, M, 2004). 
Having the high ground resolution panchromatic 10 m image 
and the multispectral resolution 20 m images, the basic idea was 
to merge them to use the double data set to get a better image 
with more details. 
The combination process used to integrate the two images was 
as follows: first the panchromatic image was enhanced, then the 
multispectral images was atmospheric-corrected and saturation 
enhancement of colours were applied. In the end a 
normalization of the bands and their combination was made. 
  
Figure 9. SPOT HRV pansharpened image of study area 
Based on the fact that intensity I (digital number, DN, of each 
pixel) depends mostly on external factors, such as sun angles, 
surface orientation, shadow, a band normalization was used 
which splits the intensity and reflectance to allow proper feature 
extraction. This was carried out by dividing the DN value of the 
panchromatic band by the DNs sum of the three multispectral 
bands for each image pixel (resampled to 10 m scene pixel). 
Ip/Id x scaling factor, where Ip is the DN of panchromatic 
image pixel and Id is R+G+B= sum norm. First, for all image 
pixels the three bands (R, G, B) sum which represents the total 
intensity was calculated and the result was merged with the 
enhanced panchromatic image to get the ratio. The final step 
was to merge the ratio result and the enhanced multispectral 
image. 
The wider color range in the resulted images allows 
distinguishing waters, vegetation and soils classes. Main 
morphologic features as streets, city's parceled structure, 
uncovered soils and different vegetation types (compact or 
isolated trees, grass vegetation) are revealed. 
Digital processing has been done using Idrisi Andes GIS 
software.