Application of the Computer Vision Hough Transform for
Automated Tropical Cyclone Center-Fixing from Satellite Data Mark
DeMaria, NOAA/NCEP/NHC Robert DeMaria, CIRA/CSU NCAR-CSU Tropical
Cyclone Workshop January 8, 2014 Boulder, CO
Slide 2
Outline Tropical cyclone center fixing from satellites The
circular Hough transform Application to tropical cyclones
Preliminary results Improvement through multi-spectral
analysis
Slide 3
Only west Atlantic and around Hawaii have routine aircraft
center fixes Satellite data used subjectively to find centers
across the globe Improvements to accuracy in real-time highly
desirable Aircraft Data Availability
Slide 4
Center location (fixing) Center Location = surface center
Center of circulation Usually close to the lowest sea-level
pressure Visible and IR methods Dvorak Eye Distinct and inferred
center with shear pattern and low-level clouds Spiral bands and
curved cloud lines Wedge method Using animation Low-level cloud
motions Deep layer cloud motions Ignore cirrus layer cloud motions
Mid-level centers tilted from surface center Using microwave images
Thick cirrus clouds in visible and IR images obscure features
below, used for center location Thick cirrus clouds in microwave
images are more transparent, and the microwave images may often
provide better views of features, for improved center locations
Using 3.9-micrometer images at night New Day-Night band from
VIIRS
Slide 5
Center Location Nearly all methods subjective Exception is
CIMSS ARCHER method that fits spiral patterns to microwave imagery
from LEO satellites Many more geostationary images than center
fixes Automated methods would allow use of high temporal resolution
of IR and visible data 5
Slide 6
Circular Hough Transform Hough transform developed for computer
vision applications to detect features Originally developed for
lines and edges Later generalized to shapes Circular Hough
transform applied to accurately detecting centers of breeder
reactors Application to finding tropical cyclone centers 6
Slide 7
Circular Hough Transform for case with known radius
Slide 8
Generalization for Unknown Radius Estimate range of possible
radii Perform CHT for range of test radii Calculate 2-D
accumulation matrix for each test radius Scale accumulation
matrices by radius Average scaled matrices Center is point with the
most votes or some weighted average around the maximum Can be
generalized to multiple circles Automated coin identification
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Slide 9
Application to TC Center Fixing from IR Data Apply cold
threshold to IR image to isolate cold clouds Apply edge detection
method Take Laplacian of IR brightness temperature Apply threshold
to |Laplacian| Perform CHT for a range of radii 10 to 300 km in 1
km intervals Use combined accumulation matrix to find the center
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Slide 10
Hurricane Katrina Example
Slide 11
Accumulation Matrix for Radii from 45 to 112 Pixels
Slide 12
Charley 2004 Very small but intense hurricane Katrina 2005
Classic large, intense hurricane Ericka 2009 Very disorganized weak
tropical cyclone, did not make it to hurricane strength Earl 2010
Strong hurricane in higher latitudes Sandy 2012 Unusually large but
only moderate strength, non-classical hurricane structure IR images
every 6 hr for lifecycle of each storm 135 images Tropical Cyclone
Cases
Mean CHT error: 91 km Storms with eyes: 54 km Bias X: 6 km Bias
Y: 8.5 km Bias Explained by Parallax Results
Slide 15
Results by Storm:
Slide 16
Primary Error Sources Sheared storms Circulation center
displaced from cold cloud shield Storms with eyes Radii on scale of
outer cloud shield gets more votes than radii on the eye scale
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Slide 17
Tropical Storm Erika 17 X
Slide 18
Eye Center Out-Voted
Slide 19
Next Steps Accumulation matrices may be useful for eye
detection Multiple solutions for centers Use CHT from IR data as
first guess to visible algorithm Combine with other information
Shear vector Microwave imagery, day-night band Time continuity of
displacement 19