: Sepæatine an 
  
Partial constuction of the original image 
  
Applving morphological filters 
  
  
Construction of the edge map 
  
  
  
Calculation of region statistics 
  
  
  
Extracting the building parcels based on the shape and area characteristics 
  
Figure 3. The algorithm for buildings extraction using curvelet 
transforms 
The second step, of the algorithm deals with the classification 
step. In this step a multi-layer classification process based on 
new band ratios will be applied to extract seven classes (water, 
vegetation, bare soil, Asphalt, Shadows, Buildings)(Elsharkawy 
etal., 2012) 
The traditional NDVI ratio plus two new band ratios are 
introduced, first one the NDVI (R1) especially suited for 
vegetation and water; second ratio (R2) was used to detect 
asphalt, man-made object and shadow. Finally (R3) was 
applied to detect bare soil and buildings. Table 1 summarizes 
the three ratios and their usage. 
  
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 
  
  
  
R — NIR1 Vegetation 
R1 MORD 
R+NIR1 Water 
D. Asphalt 
R2 nr Shadow 
C+R Manmade object 
NIR1 ~Y Building 
R3 VU 
NIR1+Y Barren 
  
  
Table 1. Band ratios and their implementation 
R1 is applied first to separate the image into vegetation and non 
vegetation, and then R2 is applied to detect the water above a 
certain threshold. Asphalt, shadow and red roofs are detected 
within lower and upper thresholds; finally, the R3 ratio is 
applied to detect bright surfaces below certain threshold. Figure 
4 summarizes theses steps. 
    
    
     
  
   
  
  
Figure 4. Applying the band ratios with the proposed thresholds 
The final step involves the integration between the previous two 
steps to enhance the pixel-based classification results. The main 
idea behind this step is to incorporate the object-based results as 
a classification layer to be added to the  multi-layer 
classification process. In this integration step, we have 
confidence in the water, vegetation, asphalt and shadow classes, 
while building and bare soil classes can be modified according 
to the edge detection process. If we denote the pixel-based 
classification results by p(m,n) and object-based classification 
results as E(m,n) and the final classification results as f(m,n) so 
we can apply the following algorithm to integrate the object and 
pixel based results. 
  
If p(m,n) water then ..........f(m,n) = water 
If p(m,n) vegetation .......... f(m,n) = vegetation 
If p(m,n) shadow............. f(m,n) = shadow 
If p(m,n) asphalt and f(m,n) not buildings then f(m,n)= asphalt 
If p(m,n) bare soil and f(m,n) not buildings then f(m,n)= baresoil 
If p(m,n) building ........... f(m,n)= buildings 
If E(m,n)>0 and f(m,n) not vegetation or shadow or asphalt then 
f(m,n) is building 
  
  
  
Generally, any imagery will be used in a radiometric/spectral 
analysis must be converted to spectral radiance at a minimum or 
top of atmosphere reflectance in order to account for the 
variation in the relative positions between the sun, the earth and 
the satellite to obtain absolute values for the NDVI ratios can be 
applied in any other scene (Updike and Comp, 2010). 
Converting the Digital Numbers (DN) to Top of Atmosphere 
(ToA) reflectance is a two-step process. First DNs are 
converted to ToA radiance values. Then these radiance values 
are then converted to reflectance values(Elsharkawy et al., 
2012; Observation, 2010).