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Registration of Geophysical Images. Alexandra A. Karamitrou Laboratory of Exploration Geophysics Aristotle University of Thessaloniki, Greece,. [email protected] Maria Petrou Informatics & Telematics Institute, CERTH, Thessaloniki, Greece. [email protected] Gregory N. Tsokas

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slide1

Registration of Geophysical Images

Alexandra A. Karamitrou

Laboratory of Exploration Geophysics Aristotle University of Thessaloniki, Greece,

[email protected]

Maria Petrou

Informatics & Telematics Institute, CERTH, Thessaloniki, Greece

[email protected]

Gregory N. Tsokas

Laboratory of Exploration Geophysics Aristotle University of Thessaloniki, Greece

[email protected]

ARISTOTLE UNIVERSITY OF THESSALONIKI FACULTY OF SCIENCES

slide2

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.

slide3

Magnetic method

Instrument: Gradiometer

sensors

Detect magnetic anomalies produced by the existence of buried features

slide4

Electrical method

Determines the underground resistivity anomalies

Electrodes that measure the electric potential

Electrodes that induce electric current

slide5

Archaeological area of Kampana

(Maronia-NE Greece)

Ceramic objects

Ruins from the temple of Dionisos (323 - 146 B.C)

Mosaic floor from an aristocratic house

(323 - 146 B.C)

Ancient Theater (323 - 146 B.C)

slide6

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

slide7

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)

slide8

Archaeological area of Argos-Orestiko

(West Greece)

Tsokas et al., 2006

Vertical Gradient of the local magnetic field

Magnetic method

Apparent Resistivity

Electrical method

slide9

Archaeological area of Argos-Orestiko

(West Greece)

Tsokas et al., 2006

Vertical Gradient of the local magnetic field

Magnetic method

Apparent Resistivity

Electrical method

slide10

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

slide11

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
slide12

Training set

Left column

Vertical Gradient of the local magnetic field

(magnetic method)

Right column

Apparent Resistivity

(electrical method)

Test data

slide13

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

Mutual Information 0.5431

slide14

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

Preliminary registration of test data

slide15

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.

Rotation

Original Image

Scaling

Shearing

slide16

Proposed Methodology

START

Apply the affine transformation while we check the effect on the Mutual Information

Randomly selected parameters for the affine transformation

Registered Images

(with the exhaustive search method)

Randomly selected area

Transformation is saved and the transformed image is updated

Does the termination criterion has met ?

STOP

YES

NO

Is there improvement on the Mutual Information?

slide17

(2M+3)x(2M+3)

Μ=1

(2M+1)x(2M+1)

Μ=1

25 pixels

9pixels

+

+

+

+

+

+

+

o

o

o

x

x

x

o

o

o

+

+

x

x

x

+

+

x

o

o

x

o

x

+

+

+

+

+

For the pixels at the places of the window with the maximum distortion,

Parameter is calculated as,

“continuity” parameter

Selecting ,

the pixels at the periphery

do not move much.

The Delaunay triangulation method (Delaunay B., 1934) was used.

slide18

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.

slide19

Windows that succeed to increase the Mutual information

Windows that fail to increase the Mutual information

slide20

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

Different values of mutual information for the two testing pair of images

0.57 0.76

0.8 1.46

Argos Orestiko 1st case

Argos Orestiko 2nd case

slide21

Transformed Images Results

Archaeological area of Kampana

Archaeological area of

Argos Orestiko

slide22

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.

slide23

Thank you for your attention !

Alexandra A. Karamitrou

Laboratory of Exploration Geophysics Aristotle University of Thessaloniki, Greece,

[email protected]

Maria Petrou

Informatics & Telematics Institute, CERTH, Thessaloniki, Greece

[email protected]

Gregory N. Tsokas

Laboratory of Exploration Geophysics Aristotle University of Thessaloniki, Greece

[email protected]

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