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 
acquisition date on 08/19/2008, dry season, spatial resolution of 
20m. 
• Satellite image from CBERS 2B, HRC sensor, 
panchromatic band, and 158/A/119/2 Point Orbits; acquisition 
on 10/10/2008, dry season, spatial resolution of 2.5m. 
For the image geo-referencing step, an ortophoto was used in 
2006, and to validate the extraction of vegetation a QuickBird 
Image of 2002 was used. Both images have spatial resolution of 
60 cm, and we use true color composition (no infrared). 
Figure 1 - City of Goiania, Brazil. Urban area appears in red 
color. 
Census data, correspondent to the 2000 census, was obtained at 
the Brazilian Institute of Geography and Statistics (IBGE). This 
aggregated data was made available by the Census Districts. 
Additional vector data was acquired together with the city of 
Goiania in the form of MUBDG (Basic Urban Digital Map of 
Goiania) and were updated in 2008. 
(http://www.goiania.go.gov.br/html/geoprocessamento/mapa. 
htm). 
Figure 2 presents the flowchart of the methodology carried out 
for processing the satellite images, and then to generate the 
vegetation change map. This methodology was adapted from 
Domingos (2005). Following, we provide a detailed description 
about the digital image processing techniques used in this study. 
2.1 Image Pre-processing 
Due to differences in the images generated by the sensors, it is 
natural that distortions between the images occur. Thus some 
pre-processing steps are necessary to correct the data so that 
they become consistent to the proposed procedure. 
The adjustment in question began with mosaic of images from 
TM and HRC sensors as shown in Figure 3. Next, we performed 
the atmospheric correction through the subtraction technique of 
the dark pixel (Chavez, 1988). Subsequently, we processed the 
images with a restoration filter, which improves the effective 
spatial resolution of the image and interpolates them at a finer 
sampling grid (Fonseca et al., 1993). The pixel size of CCD (20 
m) and TM (30 m) was changed to 10 meters using the 
aforementioned restoration algorithm. 
Finally, the data were interpolated (cubic convolution) to 2.5m 
to present the same pixel size of image HRC, a fundamental 
condition for the success of the fusion process. 
For geo-referencing HRC, CCD and TM images, we used a 
2006 ortophoto, in UTM Projection system with Datum Sad 69, 
as reference. For this, a set of 47 identifiable and well 
distributed control points throughout the study area were 
collected, having a pixel error of less than 0.38. To further 
minimize the registration error, the REGEEMY 
(http://regima.dpi.inpe.br/) system, version 0.2.43, was used, 
which allowed a refinement of the control points with an error 
below 0.17 pixel. 
As the scenes went beyond the area of interest after mosaicking, 
it was necessary to superimpose the image with a vector file 
with the limits of the region of interest, to eliminate the area 
that would not be used. 
Figure 2 - Methodological Flowchart.