International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
Landsat-7 ETM images includes the topographic maps, aerial 
photographs, orthophotos and personal knowledge about the 
area. However, before the image classification processes, 
geometric correction of Landsat images was completed. For this 
purpose, 21 uniformly distributed GCPs digitized from the 
1:25,000 scales topographic maps of the interest arca were used. 
Planimetric accuracy of these GCPs can be expected in the 
range of 7.5m. On Landsat images, linear features appeared 
sharp enough, so GCPs are mainly selected from road crossings 
and bridges. Digital image coordinates for GCPs were measured 
manually using the GCPWorks module of PCI system with the 
sub-pixel point determination. Then, affine transformation was 
applied between the GCPs's image and ground coordinates. 
Root means square errors for X and Y directions were found to 
be 0.69 pixels (20.7m) and 0.67 pixels (20.1m) respectively. 
After producing transformation function, for image registration, 
bilinear resembling method was used to determine the pixel 
values to fill into the output matrix from the original image 
matrix. 
Table 1. Phenomena revealed by different bands of Landsat 7 
ETM+ data 
  
  
  
Band Phenomena revealed 
  
Shorelines and water depths (these 
n 45.0 5^ EY 3 
0.45-0.52 jam (visible blue) wavelengths penetrate water) 
  
  
Plant types and vigor (peak vegetation 
0.52-0.60 um (visible green 
Spm e green) reflects these wavelengths strongly) 
  
  
Photosynthetic activity (plants absorb 
0.63-0.69 pm (visible red) these wavelengths during 
photosynthesis) 
  
  
Plant vigor (healthy plant tissue reflects 
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0:76-0:90 um (near IR) these wavelengths strongly) 
  
  
Plant water stress, soil moisture, rock 
1 75 TD 
1.55-1,75 pum nid IR) types, cloud cover vs. Snow 
  
10.40-12.50 pum (thermal IR) Relative amounts of heat. soil moisture 
  
  
Plant water stress, mineral and rock 
types 
  
2.08-2.35 um (mid IR) 
  
  
  
4. CLASSIFICATION AND RESULTS 
4.1 Pixel-based classification 
Pixel-based classification of Landsat ETM image of interest 
area was realized in two steps. In the first phase, ISODATA 
(Iterative Self Organizing Data Analysis Technique) has been 
applied and thus, spectral clusters have been determined, which 
gives pre-knowledge about the site. Amongst the obtained 
clusters, some have been eliminated or combined based on the 
ground truth materials. Finally, 7 main classes have been used 
as training areas for the classification procedure (see Figure 3). 
  
    
  
  
  
  
  
  
Value Name Color 
| |sea | 
2 |damlake 
  
  
3 |settlement areas| 
_ 4 |dense forest |W 
jopenareas | 
coal waste — 
wood land — 
  
    
  
    
  
  
  
  
  
Figure 3. Result of ISODATA unsupervised classification. 
In the second stage, supervised classification algorithms 
(parallelepiped, minimum-distance and maximum-likelihood) 
have been applied respectively to the Landsat image based on 
the determined training patterns and reference materials. For 
comparative analysis of each method, same training sites have 
been utilized with the same colour information. Classifications 
have been undertaken by the related module of PCI Geomatica 
V9.1.2 software package and respective results are given in 
Figure 4. 
Name 
a 
sea 
dam lake 
settlement areas; 
dense forrest 
areas 
coal waste 
1 
2 
3 
4 
5 
6 
7 
wood land 
  
  
c. Result of Maximum-Likelihood classification. 
Figure 4. Results of classical supervised classification 
techniques (a. Paralelpiped, b. Minimum Distance and €. 
Maximum Likelihood). 
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