Full text: The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
Yukio KOSUGI*, Munenori FUKUNISHI*, 
Mitsuteru SAKAMATO**, Wei LU** and Takeshi DOIHARA** 
*Tokyo Institute of Technology (kosuqi@pms.titech.ac.jp ) 
**Asia Air Survey Co., Ltd. R&D Department (ta.doihara@aiiko.co.jp ) 
We propose a method of detecting dislocation and slope failures by evaluating a pair of high-resolution aerial photographs, 
taken before and after the earthquake. In our approach, we compared the two images in an adaptive nonlinear mapping 
mechanism, to find out the discontinuous distribution of shifting vectors required for adjusting the two images. 
1. Introduction: 
For analyzing and prediction of dislocation earthquakes, as 
well as for preventing landslip disasters, it is important to 
detect automatically the geographical changes such as 
dislocation and slope failures, from a pair of high-resolution 
images taken before and after the earthquake [1-2]. Simple 
subtraction of two images may give some information 
regarding the land dislocations. In most cases, however, it 
is difficult to get a pair of images of the same photographic 
conditions, even after the geometric compensation using 
affined transformation. 
In our system, we nonlinearly map an aerial photo image to 
another one observed at different time, of the same area 
[1]. During the nonlinear mapping, we generated a set of 
shifting vectors require for the nonlinear mapping and 
evaluated the discontinuity in the vector distribution, to find 
out the dislocation and landslips. 
2. Nonlinear Mapping Algorithm: 
As shown in Fig. 1, our nonlinear mapping algorithm 
consists of the competition procedure to find out the best 
matched shift of sub-areas and the consensus operation 
among shifting vectors of near-by sub-areas for 
maintaining continuous transformation. After the consensus 
operation, image A will be deformed a little bit according to 
the set of shifting vectors. After several times of iteration, a 
nonlinear mapping from A to B will be formed, and the 
spatial derivatives of shifting vectors will demarcate the 
3. Choice of Consensus Operations: 
In the algorithm mentioned above, the neighborhood zone 
shape in the consensus operation significantly influences 
the failure detection ability. In our former approach [1], we 
defined a squared neighborhood, as indicated in dotted 
line of Fig.2. However, when the zone of that shape 
covers both sides of the sheer border S1-S2, the 
consensus operation dilutes the difference of shifting 
vectors at the boundary to be detected in the spatial 
a) Strip zone approach: In our first trial, we defined a 
strip in parallel with the border, as illustrated in double 
solid line in Fig.2. In the strip zone approach, however, 
we have to hypothesize the sheer border orientation, or 
repeat the whole algorithm for several possible 
orientations to find out the most clear result. 
Imaae A 
Competition among shifting candidates 
Consensus among shifting vectors 
Deformation of image A 
According to the shifting vectors 
Spatial derivation 
Indication of discontinuous shifts 
Fig.1 Sheer detection algorithm 
* * 
X *i * 
■ ^ ^ 
X * 
S 2 
Fig.2 Neighborhood zone shapes 
b) Bi-cluster approach: If we can classify all the shifting 
vectors within an area into two groups according to 
their vector orientations, we can make the consensus 
operation to each of the groups. For doing this 
approach, we plotted the shifting vectors on the Px-Py 
2D space, where Px and Py are horizontal and

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