Segmentation and Tracking of Ionospheric Storm Enhancements
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Transcript of Segmentation and Tracking of Ionospheric Storm Enhancements
Segmentation and Tracking of Ionospheric Storm Enhancements
Matthew P. Foster & Adrian N. EvansUniversity of Bath
SPIE Europe Remote Sensing 2008
Contents I
Basics Segmentation Tracking
•Aims and objectives
•The Ionosphere & geomagnetic storms
•Storm enhancements & data
Contents II
Basics Segmentation Tracking
•Attribute mathematical morphology
•Ground-truth generation & temporal feedback
•Splitting features & segmented outputs
Contents III
Basics Segmentation Tracking
•Motion from boundaries using shape contexts
•Depletion issues
•Vector outputs
Basics
Aims & Objectives•Geomagnetic storms
cause ionospheric enhancements
•We want to track these as they cross the northern polar region
•This is hard – the images are tiny!
http://www.flickr.com/photos/orvaratli
The Ionosphere•Atmospheric region from
50 to over 1000 km
•Electrons and ions can exist for short periods
•They form layers and affect radio propagation
•Electron density is largely determined by the Sun
Public domain image from WikiMedia Commons
Geomagnetic StormsB
ow
Shock
Mag
net
opau
se
Magnetotail
Reconnection
Reconnection
Solar wind
•When large amounts of material are ejected by the Sun
•Some can be injected into the Ionosphere
•This is a geomagnetic storm
Storm Enhancements
• The injected material increases the number of electrons in the ionosphere
• These increases are called storm enhanced density and form a tongue which moves over the northern polar cap
• It’s this we want to track
Data from GPS •Raw data from MIDAS
(tomographic imaging software)
•100˚ x 100˚ grid
•4˚ resolution
•25 x 25 pixels
•5 minutes between frames
Data from GPS •Raw data from MIDAS
(tomographic imaging software)
•100˚ x 100˚ grid
•4˚ resolution
•25 x 25 pixels
•5 minutes between frames
USA
Scandinavia
Russia
Greenland
Canada
Direction of Incident Solar Radiation
Tongue of Ionisation
Sunlight
Traditional Approaches
• Optical flow requires greater consistency
• Block matching requires texture
• Correspondence methods require feature detection (and filters are too large)
• So we decided to opt for a two-stage approach (segmentation & tracking)
Segmentation
Attribute Morphology•Greyscale mathematical
morphology – extended to support general attributes
•Such as area, contrast, moments etc…
•Contrast closing is useful in this case as area parameter is too sensitive
•Removes background
1
23
Attribute Morphology•Greyscale mathematical
morphology – extended to support general attributes
•Such as area, contrast, moments etc…
•Contrast closing is useful in this case as area parameter is too sensitive
•Removes background
1
23
Ground-truth Generation•A contrast value is
needed for the opening operation
•We hand-segmented the sequence
•And tested different values to choose the best overall (33)
Ground-truth Generation•A contrast value is
needed for the opening operation
•We hand-segmented the sequence
•And tested different values to choose the best overall (33)
Input
Increase
contrast
Segment
Check area
against
previous
Ouput
Decrease
contrast
Temporal Feedback
•The segmented results are fairly good
•But changes between frames mean that they lack consistency
•Temporal feedback helps here
Splitting Features
• Watershed segmentation can be used to split features joined by saddle shaped features
• Invert segmented images and apply watershed
Segmented Outputs• Outputs from feedback show better spatiotemporal
consistency
• Boundary tracing used to convert to smoothing splines
• Ready for estimating motion using shape correspondence
20:00 20:50 21:40
Tracking
Motion from Boundaries
• Motion can be estimated by calculating boundary correspondences
• This can be done by misusing shape contexts
• i.e. don’t compute similarity metrics
• Which map one boundary into another
• Subtracting coordinates of corresponding points gives motion vectors
Shape Contexts I
•For each boundary point
•Subtract coordinates from the others
•Convert to polar form
•Bin to create a 2-D histogram
Ref: S. Belongie, et al. Shape matching and object recognition using shape contexts. Transactions on Pattern Analysis and Machine Intelligence, 24(4):509–522, Apr 2002.
Context n
Boundary 1 Boundary 2
Overlaid Boundaries
Boundary Motion
Shape Contexts II•Compare stacks of 2-D
histograms using χ², EMD or diffusion distance
•Use bipartite matching to get correspondence
•Subtract corresponding coordinates to get vectors
20:00 20:05 20:10
Vectors & Depletion Issues• Gives good results, especially near beginning
• A few frames have vectors pointing the wrong way!
• This happens because of depletion
• Electron densities drop at night (top right)
20:00 20:05 20:10
Vectors & Depletion Issues• Gives good results, especially near beginning
• A few frames have vectors pointing the wrong way!
• This happens because of depletion
• Electron densities drop at night (top right)
21:25 21:30 21:35
Vector Outputs• The depletion problem can be fixed by
detecting the occurrences
• And replacing the vectors with the previous frames – interpolated to the correct positions.
20:00 20:50 21:40
• Vector fields can be made more useful by making them more uniformly available
• Interpolating / regularising them across the object increases usefulness
Increasing Vector Density
20:50 21:40 22:30
• Vector fields can be made more useful by making them more uniformly available
• Interpolating / regularising them across the object increases usefulness
Increasing Vector Density
20:50 21:40 22:30
Conclusions• Attribute morphology can be useful for
segmentation where area or others are too sensitive
• Shape context matching can be used to infer boundary motion
• This methodology allows some analysis despite very low image resolution
• Future work will focus on additional data and other storm events
Extra Slides
a ab
Apparent Boundary Motion
Actual Flow Direction
Extra Slides
Connected Component GraphArea 3 Opened Image
B ACE F
GIH J
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Set to be removed from output imageX
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Extra Slides
Connected Component GraphContrast 2 Opened Image
B ACE F
GIH J
D
Set to be removed from output imageX
J
H
E
B
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G
D
A
F
C