alexandra karamitrou.ppt
TRANSCRIPT
Registration of Geophysical ImagesAlexandra A. Karamitrou
Laboratory of Exploration Geophysics Aristotle University of Thessaloniki, Greece,
Maria PetrouInformatics & Telematics Institute, CERTH,
Thessaloniki, Greece
Gregory N. TsokasLaboratory of Exploration Geophysics
Aristotle University of Thessaloniki, Greece
1ARISTOTLE UNIVERSITY OF THESSALONIKI FACULTY OF SCIENCES
Geophysical methods
The target is to increase the information obtained from the 2 original images independently.
Archaeology
Brizzolari et al., 1992aGarrison, 2003Piro et al., 1998Tsokas et al., 1994
Magnetic method
Detect magnetic anomalies produced by the existence of buried features
sensors
Instrument: Gradiometer
Electrodes that induce electric
current
Electrodes that measure the
electric potential
Electrical method
Determines the underground resistivity anomalies
Archaeological area of Kampana (Maronia-NE Greece)
Ancient Theater (323 - 146 B.C) Mosaic floor from an aristocratic house (323 - 146 B.C)
Ruins from the temple of Dionisos (323 - 146 B.C)
Ceramic objects
Vertical Gradient of the local magnetic fieldMagnetic method
Apparent ResistivityElectrical method
Archaeological area of Kampana (Maronia-NE Greece)
Tsokas G. et al., 2004
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Aero photography by Κ. Κiriagos
Archaeological area of Argos-Orestiko (West Greece)
Ancient temple of Roman period (63 B.C – 476 A.D) and an old Christian church (450–600 A.D)
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Archaeological area of Argos-Orestiko (West Greece)
Tsokas et al., 2006
Vertical Gradient of the local magnetic fieldMagnetic method
Apparent ResistivityElectrical method
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Vertical Gradient of the local magnetic fieldMagnetic method
Apparent ResistivityElectrical method
Archaeological area of Argos-Orestiko (West Greece)
Tsokas et al., 2006
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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
Electrical instrument Magnetic instrument
Hand held instruments the data may have errors due to
inaccuracies during the measurements
Flagging all the non-chartered pixels with a non realistic pixel value
No rectangular imagesUnchartered patches in the interior due to obstacles
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Image Preprocessing
Original image Flagged image
Left columnVertical Gradient of the local magnetic
field(magnetic method)
Right columnApparent Resistivity(electrical method)
Training set
Test data
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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 Information0.1204 Mutual Information 0.5431Mutual Information 0.2234 13
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
Preliminary registration of test data 14
Registration Results
Affine TransformationAffine 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.
Original Image Rotation
Scaling Shearing
'
'
x a b x
c d yy
=
'
'
0
0
x s x
s yy
=
'
' 0x
y
S kx x
S yy
=
'
'
1 0
0 1
x x
yy
=
'
'
cos sin
sin cos
x x
yy
θ θθ θ
− =
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Proposed Methodology
(2M+3)x(2M+3) Μ=1 25 pixels
(2M+1)x(2M+1) Μ=1 9 pixels
+ + + + +++++++++
+++
o
o
ooo
o
oo
o
x xxx
x
xxx x
'' '
'' '
D Dx x x a b x xe e
y c d y yy yε ε− −
= + = +
“continuity” parameter
The Delaunay triangulation method (Delaunay B., 1934) was used.
0.6 , 1a d≤ ≤
0.2 , 0.2b c− ≤ ≤
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For the pixels at the places of the window with the maximum distortion,
0 0x x y y M− = − =
ln g
Mε −=
Selecting , the pixels at the periphery
do not move much.
0.1g = 0Me ε− →
Parameter is calculated as,
ε
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.
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Windows that succeed to increase the Mutual information
Windows that fail to increase the Mutual information
Different values of mutual information for the training pair of images (Maronia).
Argos Orestiko 1st case Argos Orestiko 2nd case
Different values of mutual information for the two testing pair of
images
The algorithm was run without any change of the parameters for the 2 testing pair of images
0.5 0.98
0.57 0.76 0.8 1.46
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Mutual Information Results
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Transformed Images Results
Archaeological area of
Kampana
Archaeological area of
Argos Orestiko
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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 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.
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.
Alexandra A. KaramitrouLaboratory of Exploration Geophysics
Aristotle University of Thessaloniki, Greece,
Maria PetrouInformatics & Telematics Institute, CERTH,
Thessaloniki, Greece
Gregory N. TsokasLaboratory of Exploration Geophysics
Aristotle University of Thessaloniki, Greece
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Thank you for your attention !