Full text: Proceedings, XXth congress (Part 8)

2004 
  
International Archives of the Photogrammetry, Remote Sensin 
Furthermore, materials like soil and tiles present the 
same phenomenon and as a result many tile rooftops 
are classified as soil (Fig. 15, 16). 
Figure 16. Classified image 
Finally, the estimation of the classification was 
achieved using 84 points leveled for each class (12 
points per class). This procedure provided the error 
matrix and the Kappa statistics. The accuracies 
attained are presented in table 17. 
  
  
  
  
  
  
ified ima Accuracy Kappa 
Classified image totals (%) statistics (96) 
SYNTHETIC 1 80,95 77,78 
  
  
  
Table 17. Total results of the classification 
3.2.3 Classification improvement: The traditional 
techniques of processing urban areas images, due to 
their high spatial frequency, when based only to the 
spectral observation of the objects, do not always 
provide the right result. In order to solve this 
problem the procedure of the classification can be 
supported, besides from some special techniques, by 
further data, which can be from a topographic or 
thematic map to extra layers. Also, a Digital Surface 
Model (DSM) can be an important help for 
separating the classes in the classified image. The 
hypsometric information can be combined in the 
classification, so as to achieve better results in 
separating classes with similar spectral behavior. 
4. CONCLUSIONS 
Remote sensing, with the cóntinuously ongoing 
spatial resolution of modern satellites and the 
development of the processing methods for the 
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g and Spatial Information Sciences, Vol XXXV ‚Part B-YF. Istanbul 2004 
corresponding data, can offer  irreplaceable 
cartographic products (Tsakiri a.o. 1998), like the 
synthetic multispectral image with a spatial 
resolution of 1 meter, which became the background 
for the applications described above. Specifically, 
we saw how useful can the synthetic image be, 
combined with cartographic data, in visual 
interpretation of urban characteristics. This could be 
applied for the creation of reliable maps of several 
themes, where the information, compared to a 
classic map, is more and is not represented by 
geometric schemes and colors, but by their actual 
characteristics (texture, color, relief etc.) Also, the 
classification of a synthetic image of an urban area 
can have as a result thematic maps related to land 
use, from which useful conclusions can be derived, 
about the land use, for a period of time, showing the 
tension of urban development. This way they 
contribute to the procedure of making serious 
decisions. 
S. REFERENCES 
References from Books: 
Dermanis A., (1999) : Space Geodesy and 
Geodynamics - G.P.S., Editions Ziti, Thessaloniki. 
Karnavou E.(2000) : Introduction to Urban Planning 
Notes of the Course of the Department of 
Cadastre, Photogrammetry and Cartography of the 
Faculty of Rural and Surveying Engineering of 
Aristotle University of Thessaloniki, Editions 
Department of A.U.Th., Thessaloniki. 
Livieratos E., Fotiou A. (2000) : Geometric Geodesy 
and Networks, Editions Ziti, Thessaloniki. 
Tsakiri-Strati M. (1998) : Remote Sensing, Notes of . 
the Course of the Department of Cadastre, 
Photogrammetry and Cartography of the F aculty of 
Rural and Surveying Engineering of Aristotle 
University of Thessaloniki, Editions Department of 
A.U.Th., Thessaloniki. 
Tsakiri-Strati M. : Remote Sensing Applications : 
Notes of the Course of the Department of Cadastre, 
Photogrammetry and Cartography of the Faculty of 
Rural and Surveying Engineering of Aristotle 
University of Thessaloniki, Editions Department of 
A.U.Th., Thessaloniki. 
Tsakiri-Strati M. : Image Merging, Notes of the 
Postgraduate Studies Program of Geoinformatics of 
the Faculty of Rural and Surveying Engineering of 
A.U.Th., Thessaloniki. 
References from Other Literature: 
Papagiannopoulos A. (1982) : History of 
Thessaloniki, Editions Rekos & Co., Thessaloniki. 
Papagiannopoulos A. (1983) : Monuments of 
Thessaloniki, Editions Rekos & Co., Thessaloniki. 
Haala N., Walter V. (1999) : Automatic 
Classification of Urban Environments for Database 
Revision Using Lidar and Color Aerial Imagery, 
 
	        
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