be considered as 
losing operations 
1 each image was 
hological Profile 
|(x) serious with 
| pixel value was 
(5) 
oz S f(x) £7, 
e between 140 to 
uilding roof must 
lue 1. 
x by a composite 
? set of points, x, 
with x, Np fits 
LOY (9 
d (7,17), of 
lements sets for 
sists of 7,11,15 
  
(b) 
Ishinomaki area 
d for a series of 
e airborne images 
| Tsunami site of 
yan were used for 
and ENVI 48 
image processing 
; the RGB color 
City area Miyagi 
ınami event. 
  
3. BUILDING EXTRACTION 
The maximum DMP response indicates well with a matched SE 
value that the pixel resides within. There were 3 differential 
morphological opening profiles that were created using square 
to 15m (step size was equal to 4m). The SE that less than 7m 
was not reliable for use because they consist of small shadows 
and rubble of buildings. Because those figures give noise for the 
classification results, we used SE more than 7m to detect remain 
Buildings. Most of the bright building roofs gave the maximum 
response to the opening, roads and dark shadows were 
responded shape morphological elements with square size (s) 
increasing 7 to the closing. 
(f) Opening s-7 
   
(g) Opening s=ll 
   
(e) Opening s=15 (h) Opening s=15 
Figure.3 Images (c) to (e) represent binarized structural 
decomposition of the pre-event image differential 
morphological profile and (f) to (h) represents post event 
differential morphological profile. The images have been 
visually enhanced. The derivative has been calculated relative to 
a series generated by 3 iterations of the elementary SE with 7- 
15m rectangular shape roofs. Derivative of the opening profile 
with s=7(c,f), 11(d,g) and 15(e,h) are shown above respectively. 
Simple image binarization threshold was applied to each DMP 
profiles for noise reduction and avoid misclassification. Figure 
3 shows the binarized images of both pre and past event 
differential morphological opening profiles. The segmentation 
threshold value was set to 70 pixels and then the template 
matching based on Hit or miss transform method was applied to 
  
    
    
    
   
   
   
   
   
   
   
  
  
    
    
  
     
   
   
    
   
   
   
     
   
    
    
   
   
   
     
extract correspond building roofs. The data set included 7m x 
7m (3m x 3m foreground and 9m x 9m background) , 11m 
xlIm (5m x 5m foreground and 13m x 13m background)and 
15mx15m (7m x 7m foreground and 17 m x 17m background) 
template windows. 
4. INTERPRETATIONS AND QUALITY ASSESMENT 
The results have shown the usefulness of the proposed method 
during detection of various types of building, as illustrated by 
the portions given in Figure 4. 
   
@ 
  
(k) (n) 
Figure.4 Result of the building extraction according to 
approached method. Identified buildings are shown in red color, 
(1),(1) pre and post event building roofs corresponded to SE=7, 
(G),(m) pre and post event building roofs corresponded to 
SE=11 and (k),(n) show the pre and post event building roofs 
corresponded to SE=15 respectively. 
The quality of the results was assessed with exist GIS data (pre 
event) and visual inspection following manually labeling (post 
event) as ground truth. Although this result appeared to be 
slightly high, the confidence measures produced by the 
suggested a reliability of pre and post event gives 76.41% and 
88.26% in object based accuracy. For the applied area, the