areas such as road surface quality, ocean depth, and plant 
health(Globe, 2009). Taking advantage of all these properties a 
new pixel/object-based technique is introduced to extract 
shadows, water, vegetation, buildings, bare soil and asphalt 
roads for a complex scene in Ismailia city, 120 Kms to the 
north-east of Cairo the capital of Egypt. The new technique 
integrates the classification result from three new band ratios 
along with edge detection algorithm using the second 
generation curvelet transform(Elsharkawy et al., 2011b). The 
band ratio section showed very good results regarding water, 
shadow, asphalt roads, vegetation. While this approach gives 
relatively poor results distinguishing between buildings and 
bare soil due to the great similarity in spectral responses of 
these two classes. Curvelet transforms will be implemented as 
an edge detector to extract building parcels. The output of the 
last step will be integrated with the classification results to 
enhance the overall accuracy. 
  
  
  
GeoEye-1 FAN 
MS 
PAN 
IKONOS 
MS 
PAN 
QB MS 
V PAN 
-2 
MS 
nm 
; ; ; ; ; NIR-2 | ; 
T : : T T T ces 
400 500 600 700 800 900 1000 1100 
Figure 1 Panchromatic and multispectral wavelengths for 
different satellites, (Elsharkawy et al., 2011a) 
Generally, The new spectral bands in WorldView-2, Coastal 
blue, Yellow, Red edge and NIR-2, are targeting costal and 
vegetation land cover types with applications in plant species 
identification, mapping of vegetation stress and crop types, 
wetlands, coast water quality, and bathymetry (Marchisio et al., 
2010) . To be more specific, the Yellow and Red edge bands 
are filling important gaps in the spectrum that relate to the 
ability of capturing vegetation (Shafri et al., 2006). Moreover, 
Coastal blue and NIR2 bands are very helpful for 
discriminating among different types of vegetation and many 
man-made objects (Herold et al., 2002). 
2. DATA AND METHOD 
2.1 Area of study 
The study area is a residential area in Ismailia city about 120 
Km to the northeast direction from Cairo the capital of EGYPT. 
The study area is an urban area comprises scattered buildings, 
roads, vegetation areas, shadowed areas, shoreline and water 
body. The data was provided by Digital Globe, 
http://www.digitalglobe.com; the images were captured on 
April 7th, 2011 in morning time. Figure 2 illustrates false-color 
composite, NIR-1 G B of part of the study area. 
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 
   
  
Figure 2. Area of study 
2.2 Methodology 
The proposed algorithm begins with a data fusion between the 
panchromatic band of the WorldView data, 0.50 m, and the 
multispectral ones, 2.00 m resolution, to generate 8-spectral 
bands with a resolution of 0.50 m. One of the most common 
fusion techniques is the Brovey Transform. This technique is 
optimum when contrast in shadows, water, and high reflectance 
areas such as urban features is needed. The procedure of this 
transform starts with multiplying each multi spectral band by 
the high-resolution panchromatic band, and then divides each 
product by the sum of the multi spectral bands. Since the 
Brovey Transform is intended to produce RGB images, only 
three bands at a time should be merged from the input 
multispectral scene (Nikolakopoulos, 2008) in our case, we 
choose NIR-1, R and Y bands. The next step is applying a 
Gaussian high-pass filter to enhance the edges. Based on the 
curvelet transform theory an implementation for detecting 
edges will be introduced depending on the fact that the values 
of curvelet coefficients are determined by how they are aligned 
in the real image, the more accurately a curvelet is aligned with 
a given curve in an image; the higher is its coefficient value. 
Each scale level will contain different information related to the 
size and shape of the edges. Consequently, by arranging the 
coefficients of each level from the higher to the lower values 
and take the most significant part of them, this will enhance the 
edge information that represents the important part of the image 
to us. Then, the coefficients are reconstructed to get a new 
image where the edge parts are enhanced. 
Morphological filters will be applied to remove the undesired 
noised pixels. After that, a filling process will be used to 
generate the candidate parcels, as in figure 7, where buildings 
will be extracted from it in a final step. 
The final step involves calculation of statistics for the enclosed 
boundaries; such as area, major and minor axis and solidity. 
Based on shape and area characteristics, buildings will be 
extracted from the candidate parcels. The algorithm is 
summarized in figure 3; more details about this technique can 
be found in (Elsharkawy et al., 2011b) .