Possibility of Wide Area Monitoring ofSlope Failure Disaster Using PRISM:
Accuracy Verification with Geometric Distortion
Takahiro NAKAGAWA(1), Kyaw Sann Oo(2) and Masataka TAKAGI(3)Department of Infrastructure Systems Engineering
Kochi University of Technology
Tosayamada, Kochi, Japan(1)

E-mail:115114y@gs.kochi-tech.ac.jp
E-mail:108007u@gs.kochi-tech.ac.jp(3)E-mail: takagi.masataka@kochi-tech.ac.jp(2)

Abstract
Shikoku Island in Japan has many slope failure disasters by the torrential rain every year. The slope failures induce otherserious disaster such as landslide, mudflow and debris flow. Therefore, hidden slope failure must be found immediately.
PRISM sensor mounted on ALOS has three line scanner of ground resolution 2.5m and it can be observed at the sametime from same orbit. Then, 3 dimensional measurements can be carried out in 2.5m spatial resolution. The 3D
measurement using PRISM has a potential to detect hidden slope failure.
In the present research work, accuracy verification of 3D measurement using PRISM was carried out and the possibilityof wide area monitoring of slope failure disaster was concluded. The result showed less than two pixels accuracy atvalidation point in the transformation of 3D perspective projection. Accuracy of 3D measurement showed less than 10m.
Then, at least 10m width of slope failure might be detect by PRISM using 3D perspective projection.
Keywords: RPC model, DLT, 3Dmeasurement, verification
1.

INTRODUCTION

Shikoku Island faces a series of typhoons in every year
and it brings heavy rain promoting many disasters asmudflows, landslides, slope-failures and so on. Those
disasters cause huge damages. For this reason, it wasimperative to predict the disasters before it happen. Thesolution could be gained by detecting mass movement ofland. The objective of this study is to develop atechnique to detect mass movement and to conduct
verification of the possibility for monitoring wide area
slope failure disaster using PRISM images. The method
extract slope displacement is divided into the following 4parts as main branches.
1. Geometric Transformation must be established.
Usually, RPC model was used for 3D measurement.
RPC model of PRISM is a pay product fromRESTEC. Therefore, 3D projective transformation
should be evaluated using accurate GCPs. When 3D
projective transform has enough accuracy, easy 3D
measurement system can be developed.
2. Automated extraction of correspondence point fromthe stereo images by the image matching must be
established. Least square matching is powerful
method for image matching. However, initialcoordinates must be given. Then, determination
method of initial coordinates must be developed.
3. Three-dimensional measurement using obtainedcorrespondence point must be established. PRISM
has three line scanners. And triplet images can beacquired. Therefore, calculation method using triplet
image must be solved. And this process makes DSM
data.
4. Final part is to extract displacement of slope using
time series DSM. DSM is initially generated as point
cloud model. Then, point to point comparison can
not be carried out because location of the point isslightly different. Therefore, area comparison method
using point group must be developed.
This study mainly focused on establishment oftransformation and three-dimensional measurements.

2. USING DATA
2.1. Test field
Test field is selected around landslide area “Choja” inShikoku Island. Figure1 depict geological map in
Shikoku. Red area and yellow area showed a dangerous
region of landslide because of fracture by geologicaltectonic line.

at intersection of road and the center of a bridge. In thisstudy, 14 points were used as GCP and 8 points wereused as validation points.
Observed GCP points on satellite image were selected byvisual recognition. An accuracy of visual observation
showed within 1pixel.

GCP 14
Validation 8Figure 1. Geological Map in SHIKOKU
Choja is located on a mountainous area and effected bylandslide is many times. TAKAGI Geo-informatics
laboratory in Kochi University of Technology isobserving that area every year. Figure 2 represents a
PRISM image acquired on April 2007. Level 1B1 and1B2 was used for the purpose of this research. In theimage the red spot showed landslide area.

Figure 3. Distribution of GCP and Validation points
3.

ACCURACY VERIFICATION OF
TRANSFORM MODEL3.1. RPC modelAt first, accuracy of RPC model was evaluated. This
RPC model was produced by RESTEC. RPC model canconverts ground coordinates (X, Y, Z) to image
coordinates (column, row). Table 2 showed residual error
between converted image coordinate of GCP and
selected GCP on the image. In case of nadir residualerror showed about 1 pixel, but in case of forward andbackward big residual error occurred in row line.
Table 2. Residual error around GCP
Column(pix)Row(pix)
Forward
1.558
1.303
Nadir
0.540
8.484
Backward
1.646
7.153

Figure 2. PRISM image in Choja area
2.2. GPS-VRS observation
Figure 3 showed distribution of GCP and validation
points. This October, 22 points were observed using
GPS-VRS observation. GPS-VRS observation canmeasure a few centimeter accuracy. GCPs were observed

Table 3 showed residual error around validation points.
Same as a GCP big residual error occurred forward andbackward. This error will make value is predictable
adverse effect for 3D measurement. For accurate
measurement, bias correction of RPC model must be
carried out.
Table 3. Residual error around validation points
Column(pix)Row(pix)
Forward
2.125
1.529
Nadir
1.276
8.262
Backward
2.007
7.034

3.2. Three dimensional perspective projection
Next 3D-perspective-projection was also taken into
account as geometric transformation. Table 4 showed
residual error. In the case of 3D perspective projection,in all three scanning images, accuracy showed within
1pixel around GCP.
Table 4. Residual error around GCP
Column(pix)Row(pix)
Forward
0.293
0.257
Nadir
0.249
0.210
Backward
0.267
0.261
Table 5 showed residual error around validation points.
In whole number increase compared with GCP. But
obtainable result was stable, so 3D-perspectiveprojection has a potential to carry out 3D measurement.
Table 5. Residual error around validation points
Column(pix)Row(pix)
Forward
1.248
0.947
Nadir
1.586
1.284
Backward
1.905
0.899
4.

METHOD OF 3D MEASUREMENT

In this research, nadir and backward data were used for3D measurement. Equation 1 showed 3D projective
transformation.
columnrow=b1 X + b 2 Y + b 3 Z + b 4
b 9 X + b10 Y + b11 Z + 1=

b5 X + b 6Y + b 7 Z + b8
b 9 X + b10 Y + b11 Z + 1(1)(X ,Y , Z ): Ground Coordinate(column , row)b1 ~ b11: Image Coordinate: Coefficien t

5. ACCURACY VERIFICATION OF
3D MEASUREMENT
In this study, coefficients of 3D-perspective-projection
were calculated from 14 GCPs. The coordinates ofcorrespondence point in stereo pair image at GCP wereselected by visual interpretation. And 3D measurement
was carried out. Table 6 showed error between observed
GCP coordinate and calculated ground coordinate fromDLT. RMSE showed almost 1pixel in X and Y axis, butZ axis showed 2 pixels.
Table 6. Error of 3D measurementUsing DLT Method
X(m)
Y(m)
Z(m)
max
4.374
5.302
9.882
min
0.805
0.037
0.128
mean
2.023
1.778
3.353
RMSE
2.436
2.474
4.639
6.

DISCUSSION AND CONCLUSIONSIn this study the accuracy of 3D measurement from DLT
in case of 14 GCP in 35km * 35km area can get within
2pixel was predicted. This accuracy was not enough formonitoring landslide because of amount of displacement
is few cm/year. However, it is useful to specify
dangerous area of mudflow that is a secondary disasterof rapid slope failure displacement from heavy rain andearthquake and also useful to observe the occurred slope
failure displacement without being seen. It is proposedfor future work that automated image matching to selectthe correspondence point must be developed. It is alsosuggested that more and more validation points foraccurate evaluation is needed.
References[1] Jong Hyeok JEONG and Masataka TAKAGI, “Anadapted Method for Removing Change Detection Errors
due to Pointing Direction Shifts of a Satellite Sensor,”
Journal of JSPRS, Vol. 45, No.2, pp16 - 23, 2006

Previously, coefficients (b1, b2,…,b9) of transformationand image coordinate from each image were calculated.
When the column and row of the correspondence point instereo pair image are input, unknown values are groundcoordinates (X, Y, Z). The number of equations is two ineach image. Then the total number of equations becomes
four in the case of stereo imagery. Therefore, three
unknown values (X, Y, Z) can be solved by least square
method. Here in after this method called DLT (DirectLinear Transform).

[2]Jong Hyeok JEONG and Masataka TAKAGI,“Empirical Evaluation of Data Acquisition Methods for
Disaster Monitoring, Proceedings of International
Symposium on Management Systems for DisasterPrevention,” JAPAN, Session G6, 2006

The advantage of DLT is needless of initial value forreason that line shape. Disadvantage point is there aremany unknown parameters which are 11. Therefore, atleast 6 GCPs are required to establish the transformation.

[4]Takesi MIYATA ・ Jong Hyeok JEONG and
Masataka TAKAGI, “Suitable Type of Ground Control
Point for High Resolution Satellite Imagery,”
Proceedings of the 26th Asian Conference on RemoteSensing, Hanoi[3]Takeshi MIYATA ・ Jong Hyeok JEONG andMasataka TAKAGI, “Extracting Method of Mudflow
Disaster by using Arial Photography,” Proceedings ofInternational Symposium on Management Systems forDisaster Prevention, JAPAN, Session B6, 2006