method detected 1225 buildings among 1592 before the tsunami 
event and 425 buildings among 482 after the event. The error 
extraction of building structure could be due to over fitting of 
the decision surface to the data. 
5. CONCLUTIONS 
We applied a method for extraction of urban structures and 
hazard estimation using VHR airborne images. This method is 
adapted to pre and post event gray color images and do not 
require any kind of ancillary data to be performed. The first step 
was to segment structural information using morphological 
opening and closing by reconstruction operators. There for the 
pre-generated gray level airborne images of pre and post- 
earthquake and tsunami event in Ishinomaki area in Miyagi 
prefecture were applied to the morphological operators. The 
shadows of the buildings were masked and removed using their 
low spectral values. This work is a further extension of our 
previous study by introducing binary images and hit-or-miss 
transform. The segmented images were applied to hit-or-miss 
transform to extract the building roofs. Proposed combinations 
of building roofs reflectance value and shape increased the 
probability of building extraction. The candidate area contained 
various kinds of roofs with different color, shape and size. 
However, there were some building structures that are complex, 
hence these sometimes combine together to be classified as a 
one building. Further work is required to increase the accuracy 
of building detection and determine if damage ratio of the 
structure can be estimated. 
Acknowledgements 
We would like to thank for access airborne imagery and GIS 
data, provided by Geospatial Information Authority of Japan 
(GSD, Ministry of Land, Infrastructure, Transport and Tourism. 
The authors also acknowledge to Gareth Wyvill and Duminda 
Welikanna, Kyoto University, for knowledge sharing and 
facilitating. 
References 
Aytekin O., Ulusoy L, Abacioglu E.Z., and Gokcay E., 2009, 
“Building detection in high resolution remotely sensed images 
based on morphological operators", Recent Advances in Space 
Technologies, RAST '09, pp.376-379. 
Benediktsson J.A., Pesaresi M., and Arnason K., 2003, 
"Classification and Feature Extraction for Remote Sensing 
Images From Urban Areas Based on Morphological 
Transformations”, [EEE Transactions on Geoscience and 
Remote Sensing, VOL. 41, NO. 9, pp. 1940-1949. 
Heijmans H.J.A.M, and Roerdink B. T. M., 1998, "From 
connected operators to leveling" in Mathematical Morphology 
and Its Applications to Image and Signal Processing, Kluwer 
Academic Publishers, Dordrecht, pp. 191—198. 
Lefèvre S., Weber J, and Sheeren D., 2007, "Automatic 
Building Extraction in VHR Images Using Advanced 
Morphological Operators”, IEEE/ISPRS Joint Workshop on 
Remote Sensing and Data Fusion over Urban Areas (URBAN), 
Paris. 
   
Derivaux S., Lefevre S., Wemmert C., and Korczak J., 2006, 
“Watershed segmentation of remotely sensed images based on a 
supervised fuzzy pixel classification”, IEEE International 
Geosciences and Remote Sensing Symposium, Denver, USA, pp. 
3712-3715. 
Destival I., 2009, “Mathematical morphology applied to remote 
sensing”, Acta Astronautica, vol. 13, no. 6/7, pp. 371-385. 
Pesaresi M., and Benediktsson J.A., 2001, “A New Approach 
for the Morphological Segmentation of High-Resolution 
Satellite Imagery”, [EEE International Geosciences And 
Remote Sensing Symposium, Vol.39, No. 2, pp. 309-319. 
Shackelford A.K, and Davis C.H., 2004, “Automated 2-D 
Building Footprint Extraction from High-Resolution Satellite 
Multispectral Imagery A Hierarchical Fuzzy Classification 
Approach”, [EEE International Geosciences And Remote 
Sensing Symposium, pp.1996—1999. 
Soille.P, and Pesaresi M., 2002 “Advances in mathematical 
morphology applied to geoscience and remote sensing", JEEE 
Transactions on Geoscience and Remote Sensing, vol. 40, No.9, 
pp. 2042-2055. 
Sun X., Fu K., Long H., Yanfeng H., CaiL., and Wang H.,2008, 
*Contextual Models for Automatic Building Extraction in High 
Resolution Remote Sensing Image Using Object-Based 
Boosting Method", Geoscience and Remote Sensing Symposium, 
IGARSS . 
Vincent L., 1993, “Morphological area openings and closings 
for gray scale images”, Shape and Picture: Math. Description of 
shape in gray level images, NATO, p.p. 197-208. 
  
  
  
   
  
  
  
  
  
   
  
  
  
  
  
  
  
   
  
   
  
  
    
  
  
  
  
    
   
   
  
   
  
   
   
   
   
   
  
   
   
   
  
  
   
  
  
  
  
  
  
  
  
  
   
   
  
  
   
   
  
  
FEA] 
KEY W 
ABSTR 
The cla: 
commu 
for built 
features 
and locz 
the obje 
Despite 
the clas: 
problem 
metry ce 
2008; F 
et al., 2 
features 
First, im 
that imj 
specific 
ing obje 
Second, 
make re 
to merg 
techniqu 
that the 
amples. 
element 
the train 
feature « 
Koré an 
urban bi 
facade ii 
ison of « 
show fa 
facade 1 
the wind 
ability o 
In this v 
eTRIMS 
forest gi 
cade im: 
rected le 
as follo 
evaluatic 
troduce 
Section 
image c