International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
  
  
3. DATA AND METHODOLOGY 
3.1 Data 
IRS 1 D Pan and LISS III images acquired on June 5, 1996 and 
ERS image acquired on June 13, 1996 were used for the aim of 
the study. 1/5000 scaled topographic maps and aerial 
photographs were used for rectification process. Also, these 
data were used to select training sites and perform accuracy 
assessment during classification procedure. 
3.2 Pre-processing 
Digital image processing techniques were performed to extract 
land cover categories from fused images in different phases. 
[mage pre-processing was carried out including image 
enhancement, and geometric correction. ERS radar image was 
filtered using (3*3) mean and (5*5) median filter in order to 
supress speckle effect and improve interpretation capabilities. 
Geometric correction of all three images were performed using 
first order polynomial equations. Image to map and image to 
image registrations were applied to images in order to prepare 
them for an accurate fusion application. IRS 1 D Pan image was 
transformed into UTM coordinate system by means of GCPs 
obtained by 1/5000 scaled standard topographic maps. LISS III 
and ERS data were transformed to the same coordinate system 
by using rectified IRS 1 D image. All images were resampled 
to the same pixel size using nearest neighbourhood algorithm. 
3.3 Combining high- and low resolution image data 
After having transformed the dataset into the same coordinate 
system, the images were fused to produce images which have 
better spatial and spectral characteristics. The information 
content of the resulting image may adversely changed by using 
fusion techniques. 
3.3.1 Brovey Transform 
The Brovey Transform was developed to visually increase 
contrast in the low and high ends of an image’s histogram. 
Consequently, the Brovey Transform should not be used if 
preserving the original scene radiometry is important. However, 
it is good for producing RGB images with a higher degree of 
contrast in the low and high ends of the image histogram and 
for producing visually appealing images (Erdas Field Guide 
1999). The Brovey transform is a simple method to merge data 
from different sensors (Zhou, 1998). The formulae used are 
shown in equations (1), (2), and (3): 
RED = band 5/(band 2+band 4+band 5) * Pan (1) 
GREEN = band 4/(band 2+band 4+band 5) * Pan (2) 
BLUE = band 2/(band 2+band 4+band 5)* Pan (3) 
3.3.2 Multiplicative 
The multiplicative algorithm is derived by using the four 
possible arithmetic methods to incorporate an intensity image 
into a chromatic image (addition, subtraction, division, and 
multiplication), only multiplication is unlikely to distort the 
color. Equation of the algorithm is as follow; 
(DNMSn)*(DNPAN) = DNnew*MSn (4) 
576 
DNMSn- Digital number of pixel belongs to n-th multi spectral 
band 
DNPAN- Digital number of corresponding pixel belongs to 
panchromatic band 
DNnew MSn- New digital number of corresponding pixel 
(Erdas, 1999). 
3.3.3 Principal Component Analysis (PCA) 
The purpose of PCA is to compress all of the information 
contained in an original n-band data set info fewer then n “new 
bands" or components. These components are computed by 
linear combinations of the original images. None of the 
component is linearly correlated with other because these n 
components are orthogonal. The total variance of original 
images is mapped onto new components. The first principal 
component (PCI) has the greatest percentage of the total 
variance and succeeding components (PC2, PC3,.., PCn) each 
contains a decreasing percentage of the total variance (Lillesand 
and Kiefer, 2000; Lucian Wald, 2002). 
3.3.4 Intensity Hue Saturation Transformation 
The general THS procedure uses three bands of a lower spatial 
resolution dataset and transforms these data to IHS space. This 
numerical procedure was developed to convert a three-band 
RGB  (red-green-blue) display into its fundamental 
physiological (IHS) elements of human color perception 
(Grasso 1993). The IHS color coordinate system is based on a 
hypothetical color sphere. The vertical axis represents intensity, 
which ranges from 0 (black) to 255 (white). The circumference 
of the sphere represents hue, which is the dominant wavelength 
of color. Hue ranges from 0 at the midpoint of red tones through 
green, blue and back to 255, adjacent to 0. Saturation represents 
the purity of the color and ranges from 0 at the center of the 
color sphere to 255 at the circumference (Jensen, 1996). The 
IHS values can be derived from the RGB values through 
transformation equations. In order to apply this technique for 
the enhancement of spatial resolution, a panchromatic higher 
resolution channel replaces the intensity component of a lower 
resolution multi spectral dataset. 
3.4 Comparison of the spectral and spatial effects of the 
merged IRS 1 D Pan- LISS III and ERS — LISS III images 
3.4.1 Visual comparison 
Visual image interpretation was performed for comparison of 
the merged images. Visual interpretation analysis showed that 
merged images of IRS 1 D pan and LISS III have better spatial 
and spectral details then original IRS 1 D Pan and LISS [II 
images. Especially, buildings, roads and crossroads were 
identified easily from merged images compared to original 
LISS III image. Visual interpretation of ERS image was 
improved by merging ERS and LISS TIT images. Figure 3 shows 
all images which were merged by using different fusion 
techniques. Same section of the fused optic images were 
examined and interpratation results showed that Multiplicative 
method caused smoothing 
  
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