Registration of Geophysical Images
Alexandra A. Karamitrou
Laboratory of Exploration Geophysics
Aristotle University of Thessaloniki, Greece,
alexakara@geo.auth.gr
Maria Petrou
Informatics & Telematics Institute, CERTH,
Thessaloniki, Greece
petrou@iti.gr
Gregory N. Tsokas
Laboratory of Exploration Geophysics
Aristotle University of Thessaloniki, Greece
gtsokas@geo.auth.gr
ARISTOTLE UNIVERSITY OF THESSALONIKI
FACULTY OF SCIENCES
1
Archaeology
Geophysical methods
Brizzolari et al., 1992a
Garrison, 2003
Piro et al., 1998
Tsokas et al., 1994
The target is to increase the information
obtained from the 2 original images
independently.
Magnetic method
Instrument: Gradiometer
sensors
Detect magnetic anomalies produced by the
existence of buried features
Electrical method
Determines the underground resistivity anomalies
Electrodes that
measure the
electric potential
Electrodes that
induce electric
current
Archaeological area of Kampana
(Maronia-NE Greece)
Ceramic objects
Ruins from the temple of Dionisos (323 - 146 B.C)
Ancient Theater (323 - 146 B.C)
Mosaic floor from an aristocratic house
(323 - 146 B.C)
Archaeological area of Kampana
(Maronia-NE Greece)
Tsokas G. et al., 2004
Vertical Gradient of the local magnetic field
Magnetic method
Apparent Resistivity
Electrical method
6
Archaeological area of Argos-Orestiko
(West Greece)
Aero photography by Κ. Κiriagos
Ancient temple of Roman period (63 B.C – 476 A.D) and
an old Christian church (450–600 A.D)
Archaeological area of Argos-Orestiko
(West Greece)
Tsokas et al., 2006
Vertical Gradient of the local magnetic field
Magnetic method
Apparent Resistivity
Electrical method
8
Archaeological area of Argos-Orestiko
(West Greece)
Tsokas et al., 2006
Vertical Gradient of the local magnetic field
Magnetic method
Apparent Resistivity
Electrical method
9
Need for Registration
 GPS have accuracies up to 5m, depending on the quality of the
receiver, number of satellites etc.
Measurements in fields with different obstacles
Hand held instruments
the data may have errors due to
inaccuracies during the measurements
Electrical instrument
Magnetic instrument
10
Image Preprocessing
Original image
Flagged image
Flagging all the non-chartered pixels with a non realistic pixel value
No rectangular images
Unchartered patches in the interior due to obstacles
11
Training set
Left column
Vertical Gradient of
the local magnetic
field
(magnetic method)
Right column
Apparent Resistivity
(electrical method)
Test data
12
Image Registration
The geophysical images are from different
modalities
Mutual Information was used as
a similarity measure
We used a simplified version of the cost function (Kovalev V. A. and Petrou M., 1998),
where exhaustive search is used to find the parameters of the global translation
that would maximize the mutual information between the pairs of images as well
as their overlapping area.
Mutual Information
0.1204
Mutual Information 0.2234
13
Mutual Information 0.5431
Registration Results
In all three cases the results
agreed exactly with the known
shift between the pairs of
images from their geographical
coordinates.
Preliminary registration of training set
14
Preliminary registration of test data
Affine Transformation
Affine transformation is a linear 2-D geometric transformation which maps
variables, through a linear combination of rotation, scaling and shearing
followed by a translation, into new variables.
 x '  cos   sin    x 
 '  
 
 y  sin  cos    y 
 x '  1 0   x 
 '  
 
 y   0 1  y 
Rotation
Original Image
 x'   S x k   x 
 '  
 
 y  0 S y   y 
 x'   s 0  x 
 '  
 
 y  0 s   y 
Scaling
Shearing
 x'  a b   x 
 '  
  y
c
d
y

 
 
15
Proposed Methodology
START
Registered Images
(with the exhaustive search method)
Does the
termination
criterion has
met ?
Transformation is
saved and the
transformed image is
updated
NO
YES
Is there
improvement
on the Mutual
Information ?
Randomly selected area
Randomly selected parameters for
the affine transformation
Apply the affine transformation
while we check the effect on the
Mutual Information
STOP
+
+
+
+
+
++ + +
o o o +
o o o +
o o o +
+ ++ +
(2M+3)x(2M+3)
Μ=1
25 pixels
(2M+1)x(2M+1)
Μ=1
9 pixels
For the pixels at the
places of the window with
the maximum distortion,
'
 x '' 

 x 
x
b x  x 
 D
 D  a
 
 ''   e  '      e 



c d   y  y
y 
 y   y
0.6  a, d  1
“continuity” parameter
0.2  b, c  0.2
The Delaunay triangulation method (Delaunay B., 1934) was used.
x  x0  y  y0  M
Parameter

is calculated as,
 ln g
M
 M
0
Selecting g  0.1 , e

the pixels at the periphery
do not move much.
17
The randomly selected central
pixel and the (2M+3)x(2M+3)
window are selected with the
condition that the whole
window does not contain
uncharted pixels.
18
Windows that succeed to increase the Mutual information
Windows that fail to increase the Mutual information
19
Mutual Information Results
Different values of mutual information for the
training pair of images (Maronia).
0.5 0.98
The algorithm was run without any change of
the parameters for the 2 testing pair of
images
0.57 0.76
Argos Orestiko 1st case
Different values of
mutual information for
the two testing pair of
images
0.8 1.46
Argos Orestiko 2nd case 20
Transformed Images Results
Archaeological
area of
Kampana
Archaeological
area of
Argos Orestiko
21
Conclusions
Registration method with rigid body translations succeeded to register the
geophysical images in agreement with the geographical coordinates.
Local inaccuracies (offsets) during the measurements degrade the overall
mutual information between the images.
We introduced a new efficient and effective semi-stochastic optimization
algorithm which applies randomly distortions with randomly selected
parameters, and accepts the changes only when they help increase the
mutual information between the images.
We proposed a method that applies local distortion while preserves the
continuity of the grid.
We selected the parameters of the algorithm by using a training pair of
images and then tested it, without changing these parameters on two other
sets of images.
In all cases the algorithm increased the mutual information between the
images beyond the benchmark value of rigid body registration.
22
Thank you for your attention !
Alexandra A. Karamitrou
Laboratory of Exploration Geophysics
Aristotle University of Thessaloniki, Greece,
alexakara@geo.auth.gr
Maria Petrou
Informatics & Telematics Institute, CERTH,
Thessaloniki, Greece
petrou@iti.gr
Gregory N. Tsokas
Laboratory of Exploration Geophysics
Aristotle University of Thessaloniki, Greece
gtsokas@geo.auth.gr
23