Multiscale Detection and

doutfanaticalMechanics

Nov 14, 2013 (3 years and 9 months ago)

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Multiscale Detection and
Characterisation of CMEs

J. P. Byrne
1
, P. T. Gallagher
1
, C. A. Young
2

and

R. T. J. McAteer
3


1

Astrophysics Research Group, School of Physics, Trinity College Dublin, Dublin 2, Ireland.

2

ADNET Systems Inc., NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.

3

Catholic University of America, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.

STEREO/Cor1 24
-
Jan
-
07

Overview



CME Models



Image Processing:
Multiscale methods



CME Morphology &
Kinematics



Application to LASCO
and STEREO/SECCHI

LASCO/C3 27
-
Feb
-
00

CME Models


Magnetic Flux
-
Rope:



Magnetic Break
-
out:

-
Forbes & Priest, 1990

-
Chen & Krall, 2003

-
Antiochos et al. 1999

-
Lynch et al. 2004

Image Pre
-
Processing



Normalisation


-

exposure time


-

CCD bias


-

data dropouts



Background
subtraction



Median filtering


(de
-
noising)

LASCO/C2 18
-
Apr
-
00

Finding the CME Front

Edge Detection:

Our Algorithm

1) Multiscale Decomposition

Vector
-
Arrow Field

3) Spatio
-
Temporal Filter

4) Non
-
Maxima Suppression

5) CME Front Characterisation

Kinematics & Morphology

Image Pre
-
Processing

2) Gradient Space Information

Our Algorithm

1) Multiscale Decomposition

CME Vector Flow Field

2) Spatio
-
Temporal Filter

3) Non
-
Maxima Suppression

4) CME Front Characterisation

Kinematics & Morphology

Image Pre
-
Processing

Wavelets:


-
Scaling / dilation factor (
a
)

-
Shifting / translation factor (
b
)

-
Suppress noise




1) Multiscale Decomposition

LASCO/C2 24
-
Jan
-
07

Low pass: Approximation

High pass: Detail

Input: f

1) Multiscale Decomposition

Vertical Direction:

Horizontal Direction:

Scale 1

Scale 1

Scale 3

Scale 5

Scale 3

Scale 5

1) Multiscale Decomposition

2) Gradient Space Information

Consider the Gradient of an image:

(which points in the direction of most rapid change)

We have the Detail at a scale (N+1) resulting from the
directional Derivative
-
of
-
Gaussian convolved with the
Approximation at scale (N):

The gradient specifies:

2) Direction:

1) Magnitude:

So too can the Magnitude and Direction be taken
from the multiscale decomposition as illustrated…

Original:

Magnitude:

Angle:

(McAteer et al. 2007)

2) Gradient Space Information

Vectors with magnitude:

and inclination angle:

2) Gradient Space Information

Our Algorithm

1) Multiscale Decomposition

CME Vector Flow Field

2) Spatio
-
Temporal Filter

3) Non
-
Maxima Suppression

4) CME Front Characterisation

Kinematics & Morphology

Image Pre
-
Processing

2) Spatio
-
Temporal Analysis

Degrees of Freedom:


Scale, Magnitude & Angle … in Space & Time

Our Algorithm

1) Multiscale Decomposition

CME Vector Flow Field

2) Spatio
-
Temporal Filter

3) Non
-
Maxima Suppression

4) CME Front Characterisation

Kinematics & Morphology

Image Pre
-
Processing

3) Non
-
Maxima Suppression


1)
Nearest
-
neighbour info.


2)
Criteria of angle and
magnitude from gradients.


3)
Pixels chained along edges.

(Image by C.A.Young)

Our Algorithm

1) Multiscale Decomposition

CME Vector Flow Field

2) Spatio
-
Temporal Filter

3) Non
-
Maxima Suppression

4) CME Front Characterisation

Kinematics & Morphology

Image Pre
-
Processing


Ellipse fit


Height, Width, Curvature,
Orientation

4) CME Front Characterisation

STEREO/Cor1 24
-
Jan
-
07

(H.E. Schrank, 1961)

SOHO/LASCO CMEs

C2 & C3 18
-
Jan
-
00

C2 & C3 19
-
Apr
-
00

SOHO/LASCO CMEs

C2 & C3 23
-
Apr
-
01

SOHO/LASCO CMEs

STEREO/Cor1 CMEs

SECCHI
-
A 9
-
Feb
-
07

STEREO/Cor1 CMEs

SECCHI
-
A 24
-
Jan
-
07

STEREO/Cor1 CMEs

SECCHI
-
B & SECCHI
-
A 24
-
Jan
-
07

STEREO/Cor1 CMEs

SECCHI
-
A 24
-
Jan
-
07

LASCO/C2 24
-
Jan
-
07

CME Kinematics

Next Steps…



More data; distribution of
CME kinematics.



Multiple view points
(STEREO); triangulation /
projection effects.



Automated front detection;
space weather forecasting.


STEREO illustration

Thank You

NRL:


Angelos Vourlidas, Simon Plunkett.



This work is supported by grants from Science
Foundation Ireland & NASA’s Living with a Star
Program.

jbyrne6@gmail.com

Acknowledgments

C2 & C3 23
-
Apr
-
01

SOHO/LASCO CMEs

2) Spatio
-
Temporal Analysis

Degrees of Freedom:


Scale, Magnitude & Angle … in Space & Time

Vector Flow Field

Vectors with magnitude:

and inclination angle:

Normalizing Radial Graded Filter


Radially the coronagraph
image intensity drops off
steeply.



The intensity is
normalized by
subtracting the mean
and dividing by the
standard deviation.

(Huw et al. 2006)

remove

Scale Chaining / Masks

Degrees of Freedom:


Scale, Magnitude & Angle … in Space & Time


=>
Spatio
-
Temporal Image Processing & Thresholding

remove

Image preprocessing

Multiscale decomposition
?

scale chaining (denoising masks)

Spatio
-
temporal filter

(noisy masks)

Combine scale chain & spatiotemp => filter masks

Non
-
maxima suppression

Ellipse characterization

Gradient space

vector field (angle & magnitude)

Method FlowChart

NRGF: radial filter

Movie Scripts


http://www.maths.tcd.ie/~jaydog/Solar/canny_atrous/automation/20000118_arrows_combined_
rebin.html



http://www.maths.tcd.ie/~jaydog/Solar/canny_atrous/automation/20000418_arrows_combined_
rebin.html



http://www.maths.tcd.ie/~jaydog/Solar/canny_atrous/automation/20040401_arrows_combined_
rebin.html



http://www.maths.tcd.ie/~jaydog/Solar/CME_ellipse_movies/24jan07/C2_movie_ell.html



http://www.maths.tcd.ie/~jaydog/Solar/CME_ellipse_movies/24jan07/C3_movie_ell.html



www.maths.tcd.ie/~jaydog/Solar/STEREO/pb/24jan07/Graphs_plots.html

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