1 Set building area of pre-disaster was
iBefore
S
, building area of post-disaster was
iAfter
S
, area difference was
iAfter iBefore
i
S S
S −
= ∆
.When the area
i
S ∆
is less than the given minimum threshold
i
S Min
∆ , the membership degree
i S
R Area
∆
of building damage is 0;When the area
i
S ∆
is greater than the given maximum threshold
i
S Min
∆ , the membership degree
i S
R Area
∆
of building damage is 1;When
i
S Min
∆
i
S ∆
i
S Max
∆
, the membership degree of
building damage
is
i i S
S Min S
R Area= Max S
Min S
∆
∆ − ∆
∆ − ∆
. 2 The Gray Level Coocurrence MaxtrixGLCM
method is a way of extracting the second order statistical texture feature. This paper describe the
texture characteristics by using the energy characteristics ASM of GLCM, the ASM value of
the complete building is bigger, but the ASM value
of damage
building is
smallerJC.C,1990.When the
i
ASM
value of the detection building is greater than or equal to
the given maximum threshold
i
MaxASM
, the membership
degree
iASM
R ASM
of the
building damage is 0; when the
i
ASM
value is less than or equal to the minimum threshold
i
MinASM
, the
membership degree
iASM
R ASM
of the building damage is 1; when
i
MinASM
i
ASM
i
MaxASM
, the membership of
building damage
is
i iASM
MaxASM - ASM R
ASM= MaxASM - MinASM
.
2.7 Access building damage
The characteristics of a damaged building are strongly dependent on many factors, such height change, area
change and texture change. So only using a certain characteristics to determine the damage building is not
accurate. According to the principles of evidence theory, this paper will give evidence to the various
features of buildings damaged confidence in the probability distribution by the way of statistical
experts, on the basis of evidence we build the theory model to assess whether buildings damaged or not
through using various confidence coefficient. Building damaged confidence formula is as follows:
1
•
n j
j j
Judge
R P
n ρ
=
=
∑
4 Where R
j
is the membership degree of the evidence j,
j
ρ
is the prediction setting confidence degree of the evidence j. n is the total number of evidence.
Finally calculate the building damage of confidence
Judge
P
. 3. Experiment and Result
3.1 Experiments overall description
In this experimental part, DataSet is the UAV imagery of the Beichuan in 2006simulated data and 2014real
data.We apply the proposed building damage detection method of section 2 based on the 3D point
cloudshown in
Figure 2ab
and mosaic
imageshown in Figure 5ab generated from UAV imagery. First, Two DSMsshown in Figure 4ab
with 1m grid resolution are generated from 3D point cloud of pre- and post-disaster. The difference image
is generated by subtracting the pre-disaster DSM from post-disaster DSM. After Canny edge detecting and
image two-values processing, the change suspect area image shown in Figure 4c is obtained. Second,
building vector shown in Figure 3 is obtained by using the method of section 2.12. Third, the change
suspect area image is projected to the mosaic UAV image, then building vector is overlaid with the
mosaic image to obtain the building positionsee Figure 5ab in the change suspect area. Finally, the
damage buildings are detected by using the method of section 2.5 and 2.6.
ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey
This contribution has been peer-reviewed. doi:10.5194isprsarchives-XL-7-173-2014
176
a b Figure.2 3D point cloud of Beichuan. a pre-disaster b post-disaster
Figure.3 Building vector
a b c Figure.4 DSM and Change detection image a pre-disaster b post-disaster c Change detection image
a b c
ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey
This contribution has been peer-reviewed. doi:10.5194isprsarchives-XL-7-173-2014
177
Figure.5 Segmentation and Extraction of building in the suspect area a pre-disaster b post-disaster c the result of building damage.
3.2 Results of comparison
