Buildin

g

Chan

g

e Detection with UAV Ima

g

es

Tao Wang

Guangzhou Institute of Geography, Guangzhou, China

Keywords: UAV, change detection, DSM.

Abstract: In this article, we try to extract DSM in urban area by photogrammetry. Firstly, image orientation are

calculated by GPS-supported bundle adjustment. Then, DSM are extracted from urban by multi-view dense

match method SURE. DSM difference are generated from two DSM in different time to detect change of

building. This method has good effect in detecting change of village in city.

1 INTRODUCTION

Due to the unprecedented technology development

of sensors, platforms and algorithms for 3D data

acquisition and generation, 3D spaceborne, airborne

and close-range data, in the form of image based，

Light Detection and Ranging based point clouds,

Digital Elevation Models and 3D city models,

become more accessible than ever before. Change

detection or time-series data analysis in 3D has

gained great attention due to its capability of

providing volumetric dynamics to facilitate more

applications and provide more accurate results.

The European Spatial Data Research

Organization started a benchmark on image based

digital surface model (DSM) generation in February

2013. This test is based on two representative image

blocks, which were processed by different groups

with different software systems. There is only city in

the image blocks. There is no test on the forest area.

There is still some problem in the city. Because

there are a lot of shade in the image.

In this article, we try to detect change in urban

area by UAV photogrammetry method.

2 MATERIALS

2.1 Study area

The test site was located at Guangzhou, Guangdong

Province in Southern China (38◦ 32’ N, 100◦15’ E).

Aerial images used in this study were acquired on

June 8, 2015 and 2016. Figure 1 shows the location

of the test area. Figure 2 shows the flight lines in

2015 and 2016.

Figure 1: Location of test area

Figure 2: image of test area

564

Wang, T.

Building Change Detection with UAV Images.

In 3rd International Conference on Electromechanical Control Technology and Transportation (ICECTT 2018), pages 564-567

ISBN: 978-989-758-312-4

3 METHOD

This method includes accurate image orientation,

DSM extraction, and change detection. After the

images were captured, they were matched to

generate connection points. A bundle block

adjustment was then used to obtain the accurate

orientation parameter. The DSM was generated

using dense matching. It is difficult to set control

points. Therefore, we obtained the image orientation

parameters by using a GPS-supported bundle

adjustment, then a multi-view dense match to

generate the dense matched point cloud. Figure 3

shows the algorithm flow chart.

Figure 3: algorithm flow chart

Original image has initial orientation parameter,

then we use the sift algorithm to extract feature

points, use the kd-tree and ransac algorithms to

extract match points.

We use the bundle adjustment to calculate the

accurate orientation parameters.

We used a SURE(Photogrammetric Surface

Reconstruction from Imagery) algorithm to generate

the dense matched point cloud. It applied the SGM

algorithm to match between image pairs, and then

fused the results to obtain the resulting point clouds.

The SGM algorithm aims to estimate disparities

across stereo pairs such that the global cost function

shown in equation (1) is minimized.

12

(D) ( (x , D(x ))) D(x ) D(x ) 1 D(x ) D(x ) 1

bN N

bb b N b N

xx x

EC PT PT







 



 

 

(1)

In this equation

D

represents the disparity image

holding estimates of all base image pixels

x

b

. T is

an operator that evaluates whether the subsequent

condition is true and is set to zero if false.

x

N

denotes the base image pixels in the neighborhood

of

x

b

. The global cost function,

E

, is composed of

a data term and two terms representing smooth

surfaces. The data term is computed using pixel-

wise similarity measures

(x , x )

bm

C . The penalty

parameters,

1

P and

2

P , control the gain of surface

smoothing (Rothermel et al., 2011; Rothermel et al.,

2012; Wenzel et al., 2013).

Figure 4: 2015 Dense matched cloud point

Figure 5: 2016 Dense matched cloud point

image GPS/POS

data preparation

match

b

undle adjustmen

t

dense match

registration of cloud poin

t

difference of DSM

Building Change Detection with UAV Images

565

After the DSM was calculated, then we match the two

DSM by using the ICP algorithm. after that, we calculate

the difference of the two DSM.

Figure 6: change of two DSM

Figure 7: change of DSM in 3D

Figure 8: difference of DSM in local area

Figure 9: two uav images in the change area

Figure 6 shows the result of difference of two

DSM. Figure 7 shows the 3D display of the

difference. By analysing the difference, we can find

where is the change area. In the figure 6, difference

ICECTT 2018 - 3rd International Conference on Electromechanical Control Technology and Transportation

566

is zero in most yellow area. the difference is big in

the red area and blue area, the difference is negative

value in blue area, difference is positive value in the

red area. Here, we should detect the unauthorized

construction, so we analyse the red area.

4 CONCLUSIONS

We use photogrammetry method, get the cloud point

of the research area. We detect change in the urban

area by the difference of DSM from different time.

We can detect change area in the difference image

successfully.

There are a lot of dense match error in the river,

forest. That is what we should do in the future.

ACKNOWLEDGEMENTS

This research was funded by the Project for Young

Scientist Fund sponsored by the Natural Science

Foundations of Guangdong province.

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