roofs especially in strip 1 and 2 have a high probability of being 
correct (red and yellow colours by default). Some of the classified 
building roofs especially in strip 3 have a low probability of being 
correct (dark blue colour by default). These building roofs mostly 
consist of heterogeneous surface materials. Therefore, depending 
on the scene, accuracy requirements and material classes of 
interest, more training regions should be defined for these areas 
and the classification process should be performed again to 
achieve results that represent ground features more accurately. 
  
(a) Stripl (b) Strip 2 
  
(c) Strip 3 
Figure 5: Classification probability maps 
The average likelihood probability of each of the strips is shown 
in Table 1. This indicates the degree of membership for each pixel 
to a particular roof material class. 
  
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
  
Strip | Average likelihood probability 
  
  
  
  
  
  
1 91.4% 
2 85.9% 
3 88.5% 
  
Table 1: Average likelihood probability 
4. CONCLUSION 
This paper focuses on the development of an approach for 
classification of roofs using hyperspectral data. The application of 
feature extraction methods such as the discriminant analysis in the 
identification of roofs using hyperspectral data shows good 
potential. In the investigation, the DAFE is combined with a 
spatial-spectral classifier (ECHO) to classify 10 roof materials. 
The ECHO classifier segments the scene into statistically 
homogeneous regions and then classifies the data based upon the 
maximum likelihood object classification scheme. The probability 
maps of the classification results for the test and research area 
show that the output classification maps have very few errors and 
thus confirm the success of the approach. In addition, the 
integration of ALK vector data for roofs in the classification 
process results in better discrimination of spectrally similar 
materials belonging to spatially different objects. This work will 
be continued by involving a specialist on roof surfaces (future 
ground truthing). 
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http://www.gisdevelopment.net/aars/acrs/2000/ps1/ps112pf.htm 
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Chisense, C., 2011. Classification of Roofs using Hyperspectral 
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Stuttgart, Germany. 
Dell'Acqua, F., Gamba, P., Ferrari, A. Palmason, J.A. 
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