: 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).
