International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B2, 2012 
XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia 
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(a) Path/Row: 158/A/l 191 and 158/A/1192. 
(b) Path/Row: 222/071 and 222/072. 
Figure 3 - Image Mosaicking (a) HRC and (b) TM. 
We used a limit vector of the pilot area in Goiania (Figure 4) 
that contains 5 macro-areas (Campinas, South, Central, 
Macambira and Vale do Maia Ponte) to produce a mask that 
delimits the area of interest, instead of using the whole scene, as 
shown in Figure 5. These areas were chosen due to their high 
population and large construction density. 
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Figure 4 - Mask Generation (in red). 
Figure 5 - Study Area. 
2.2 Image and Data Processing 
The fusion of HRC panchromatic band and multispectral images 
from TM and CCD sensors were carried out by using the 
EHLERS fusion method (Ehlers, et al., 2010). Figures 6 and 7 
show details of the fused images, which have spatial resolution 
of2.5 m. 
The next step was to generate a mask that would identify only 
the vegetation areas in the hybrid images. We applied the HIS 
transformation to the hybrid TM image (3R4G2B) to generate 
the hue component (H), which was used to identify the 
vegetation in the image. 
Figure 6 - TM images (3R4G2B), Goiânia Airport: 
Multispectral image (left) and hybrid image (right). 
Figure 7 - CCD images (3R4G2B), Serra Dourada Stadium: 
(left) and hybrid image (right). 
At this stage only the hybrid TM image was used because, by 
the hypothesis initially raised, this image should contain more 
areas with high spectral response for vegetation for being in the 
rainy season. Figure 8 presents the Hue image which shows the 
predominant vegetation in brighter tones. 
Figure 8 - Hue image for identifying the vegetation. 
To identify and separate the vegetation targets from the non 
vegetation targets, the Hue image was classified into two