Multiscale feature identification in the solar atmosphere
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Multiscale Feature Identification in the Solar Atmosphere. C. Alex Young, Dawn C. Myers, Peter T. Gallagher (L-3 Communications GIS) . SIRW at the ROB - Oct. 23-24, 2003. General Outline. Motivation An Aside An event and a standard approach A Multiscale approach

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Multiscale Feature Identification in the Solar Atmosphere

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Multiscale feature identification in the solar atmosphere

Multiscale Feature Identification in the Solar Atmosphere

  • C. Alex Young, Dawn C. Myers, Peter T. Gallagher (L-3 Communications GIS)

SIRW at the ROB - Oct. 23-24, 2003


General outline

General Outline

  • Motivation

  • An Aside

  • An event and a standard approach

  • A Multiscale approach

  • Some natural extensions - Beamlets, Ridgelets, Curvelets, and all that - Morphological Component Analysis.


Motivation

Motivation

To model a subjective feature detection method (an observer with a computer mouse, i.e. the point-and-click detector) with an objective detection method using a multiscale vision model. Try to track edges over multiple scales or resolutions using a wavelet based multiscale edge detector. This will then give us consistent, reproducible, and statistical features.


Multiscale feature identification in the solar atmosphere

The Solar Physics Community has only scratched the surface.

  • Tracking features with a mouse and a pen is only a start not science.

  • We need to develop an image processing tree in solarsoft.

  • Morphological transforms and wavelets are not cutting edge, they are simply a starting point and part of a basic toolset.


Multiscale feature identification in the solar atmosphere

  • General Multiscale Transforms

  • Neural Networks

  • Support Vector Machines

  • Markov Chain Monte Carlo Imaging

  • Texture Modeling

  • Multiscale Likelihood / Posterior Methods

  • Non-linear filtering

  • Maximum Entropy

  • Information Theory Approach (Shannon Information)


Multiscale feature identification in the solar atmosphere

The Event - April 21, 2002


Multiscale feature identification in the solar atmosphere

Images at the time of the first brightening in the TRACE movie. (Gallagher 2003)

Top panels: TRACE 195 Å difference images created by subtracting each image from a frame taken at 00:42:30 UT. Bottom panels: LASCO C2 and C3 images showing the similar morphology of the eruption as it propagates away from the solar surface.(Gallagher 2003)


A standard analysis

A Standard Analysis


Can we use a vision model to remove some of the subjectivity say multiscale edges

Can we use a vision model to remove some of the subjectivity? Say multiscale edges.


The method

The Method

  • A multiscale version of an edge detector is implemented by smoothing with dilated kernels θ. These kernels are B3 cubic splines which approximates a Gaussian. The detector is computed with two wavelets that are the first partial derivative of θ.

  • The wavelet transform components are proportional to the gradient vector smoothed by θ. So the wavelet transform maxima at each scale size give us multiscale edges corresponding to curves of structure at a particular scale.


Wavelets and multiscale edges for 4 scales

Wavelets and multiscale edges for 4 scales

TRACE 195 Å image at 00:58:58 UT

scale = 2

scale = 4

scale = 8

scale = 16


A set of edges from 00 46 34 ut to 01 01 58 ut at 2 scales

A set of edges from 00:46:34 UT to 01:01:58 UT at 2 scales

  • The top image shows the 30 multiscale edges at scale 8 (green) and 16 (red) over the image at 00:46:34 UT.

  • The bottom images zoom in on the fronts.


Following a front in trace

Following a front in TRACE.


The event in c2

The event in C2


At a fine and medium scale no background model

At a fine and medium scale(no background model)


C2 edges for one scale size

C2 edges for one scale size


Beamlets ridgelets curvelets and all that

Beamlets, Ridgelets, Curvelets and all that

  • Wavelets are generally isotropic and best suited for representing point structures.

  • If we extend the multiscale model from points to lines, curves and the like we can obtain a near optical representation.

  • Combining different multiscale morphologies gives us Morphological Component Analysis


Beamlet multiscale line segments

Beamlet (multiscale line-segments)


Ridgelet wavelets in radon space

Ridgelet (wavelets in Radon space)


Curvelet multiscale curves

Curvelet (multiscale curves)


Xmm image

XMM image


Morphological component analysis

Morphological Component Analysis

à trous wavelet encoding

ridgelet encoding


Multiscale feature identification in the solar atmosphere

ridgelet/curvelet encoding

HST image of A370

à trous wavelet encoding

sum of all three


Conclusions

Conclusions

  • The toolset for solar image processing needs to be made available to the community. (image processing branch in SSW - see Peter Gallagher)

  • Wavelet based transforms provide a robust representation of many structures in solar images.

  • The natural extension wavelet transforms is a general multiscale morphological method.

  • Apply machine learning, probabilistic methodologies etc. to the represented structures.


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