Automatic Ice Thickness
Estimation from Polar Subsurface
Radar Imagery
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Gladys Finyom
Michael Jefferson Jr.
MyAsia Reid
Christopher M. Gifford
Eric L. Akers
Arvin Agah
Overview
• Introduction
• Background/ Related Works
• Overview of Remote Sensing
• Challenges of Processing Radar Imagery
• Ice Thickness Estimation from Radar Data
• Methods
• Edge Detection and Following Approach
• Active Contour; Cost Minimization Approach
• Experimental Results
• Conclusion
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Introduction
Remote sensing methods:
• CReSIS uses Radar and Seismic/acoustic to
acquire subsurface data from a remote location.
(i.e., surface, air, or space).
Radar and Acoustic sensors:
• used to gather data about the internal and bottom
layers of ice sheets, from the surface.
Other Examples:
• Satellite-based imagery, and identification of
events.
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Introduction
Surfaced-based and Airborne radio echo sounding
of Greenland and Antarctica ice sheets:
• Determine ice sheets thickness
• Bedrock Topography (smooth, rough)
• Mass Balance of large bodies of ice.
Greenland's ice
Challenges in Radar Sounding:
• Rough surface interface
• Stages of melting (top, inside)
• Variations of ice thickness, topography
sheet
Processing Data:
• Requires knowledge about sensing medium
• Ultimately used for scientific community
NASA/Rob
Simmon
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Introduction
Goal:
• Focus on automating task of estimating ice
thickness.
Process:
• Identifying and accurately selecting of ice sheet’s
surface, interface between the ice, and the
bedrock.
Knowing the surface and bedrock in the radar images:
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helps compute the ice thickness.
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help studies relating to the ice sheets, their
volume, and how they contribute to climate change.
Four outlet Glaciers studied by
CReSIS researchers.
Leigh Stearns
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Overview of Radar Remote
Sensing
• Radars transmit energy in form of a pulse from an antenna, energy
reflects off of target(s), and is received by an antenna.
• Distance measured based on energy travel time back and forth from
the targets.
• Gives reflection intensity and depth information about the
targets.
• Ground Penetrating Radar (GPR) able to observe properties of
subsurface, ranging from soil, rock, sand and ice.
• When data is collected, the targets are internal layering in the ice
sheets which have a strong echo return from the bedrock beneath
the ice.
• Interface (3.5 km or >) below the surface.
• Requires great transmit power and sensitive receive equipment
because of energy loss within ice and with depth.
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Overview of Radar Remote
Sensing
• Each measurement is called a radar
trace, and consist of signals,
representing energy due to time. The
larger time correlates with deeper
reflections.
Column
• In an image, a trace is an entire column
of pixels, each pixel represents a depth.
• Each row corresponds to a depth and
time for a measurement, as the depth
increases further down.
Pixel
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Overview of Radar Remote
Sensing
• A flight segment consist of a
collection of traces which represent
all the columns of the image, from
the beginning (left) to the end (right)
during flight.
• A pixel width represents the track
distance between traces, and
depend on the speed of the aircraft
during the survey.
• The flight segment is called an
Echogram.
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Overview of Radar Remote
Sensing
• The Energy from the radar into the
ice changes in dielectric properties
(air to ice, ice to bed rock) and
causes the energy to reflect back.
• Water surrounded by the ice, and
frozen ice against the bedrock both
represent a strong reflecting
interface.
• To determine whether each is
present, it depends on the radar and
its setting.
Reflection intensities are strongest at
the surface and weaker because of
depth. Depth increases from left to
right.
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Example Radar Echogram:
Greenland 05/28/2006
Figure shows radar echogram
over an ice sheet, illustrating the
reflection of internal layers and the
bedrock interface beneath the ice sheet.
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Challenges of Processing Radar
Imagery
• Automated processing and extraction of high level information from
radar imagery is challenging.
• Noise is usually electromagnetic interference from other onboard
electronics.
• Low magnitude, faint, or non-existent bedrock reflections occur:
• Specific radar settings
• Rough surface/bed topography
• Presence of water on top/internal to the ice sheet.
• This produces gaps in the bedrock reflection layer which must be
connected to construct an adjacent layer for the completion of ice
thickness estimation.
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Challenges of Processing Radar
Imagery
• Backscatter introduce clutter and
contributes to regions of images
incomprehensible.
• In addition, bed topography varies from
trace to trace due to rough bedrock
interfaces from extended flight segments.
• Lastly, a strong surface reflection can be
repeated in an image, surface multiple due
to energy reflecting off of the ice sheet
surface and back again.
• If there is a time difference between the
first and second surface return, the
surface layer will repeat in an image, at a
lower magnitude with an identical shape.
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Ice Thickness Estimation from
Radar
• Along with raw data values, and GPS
location measurements, ice thickness
is needed for scientist to:
• study mass balance
• sea level rise
• Environmental/ human impacts
• Ice thickness is computed by
selecting the surface and bedrocks
reflections in pixel/depth coordinates,
for each trace, and subtracting their
corresponding depths.
• Several experts had select the layers
using custom software.
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Ice Thickness Estimation from
Radar
• The surface is selected based on the
first and largest reflection return.
• The bedrock, is more challenging due
to buried noises.
• Experts tend to skip traces to speed
up the process.
• This causes errors, and require more
time to estimate ice thickness in a
single file.
Figure shows CReSIS picking
software, the surface return is fully
picked, while bedrock return is
partially picked.
• Thousands of images need work, and
the manual approach is not sufficient.
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Related Work
Internal Layer:
• predicts depth in certain layers.
• depth and thickness of Eemain Layer in Greenland ice sheet
utilized Monte Carlo Inversion flow model to estimate unknown
parameters guarded by internal layers. 1,2
Identification:
• Layers, contours, and curves are done using image processing, and
computer methods.
• Adaptive contour snake fitting, where an image is a cost grid,
and represents a certain amount of energy. 3,4
• Such approaches have been used in medical imagery (such as
MRIs and CAT scans). 5
[1] S. L. Buchardt, D. Dahl-Jensen, Predicting the Depth and Thickness of
the Eemian Layer at NEEM Using a Flow Model and Internal Layers, in:
Geofysikdag, 2007.
[2] S. L. Buchardt, D. Dahl-Jensen, Estimating the Basal Melt Rate at
NorthGRIP using a Monte Carlo Technique, Annals of Glaciology 45 (1)
(2007) 137–142.
[3] T. F. Chan, L. A. Vese, Active Contours Without Edges, IEEE
Transactions on Image Processing 10 (2) (2001) 266–277.
[4] M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active Contour Models,
International Journal of Computer Vision (1988) 321–331.
[5] J. Kratky, J. Kybic, Three-Dimensional Segmentation of Bones from CT
and MRI using Fast Level Sets, in: Medical Imaging: Proceedings of the
SPIE, Vol. 6914, 2008, pp. 691447–691447–10.
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Edge Detection and Following
Approach
Introduction
• Edge Detection, Thresholding, Edge Following
• Surface should be max value in each trace
• Bedrock should be the deepest contiguous layer in image
Similar Work
• Skyline Detector
• Growing seeds in the sky
• Identify week clouds in sky imagery
Our Approach
• Trace processed from bottom-up fashion until a strong edge is encountered
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Automatic Surface and Bottom Layer
Selection
Surface Selection
• Extracting the location of the ice sheet
surface
• The depth corresponding to the max value
of each trace is selected as location of
surface reflection
Bottom Selection
Figure: Echogram that has been
preprocessed using detrending, low-pass
filter, and contrast enhancement
Preprocessed by:
• Detrending
• Low-pass filtering
• Contrast adjustment
Figure: Normalized
echogram gradient
magnitude, showing the
image edges
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2D Derivative of Gaussian Kernel
Figure: 2D derivative of Gaussian convolution kernels (1.5 ) for computing vertical
(left) and horizontal (right) image gradients.
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Cleaned Edge Image & Result Image
Figure: Cleaned edge image following thresholding,
morphological closing and thinning operations
Figure: Echogram with overlaid automatically
selected surface (top, red) and bedrock (middle,
blue) layers using the edge-based method.
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Active Contour & Cost Minimization
Approach
• Similar work
- Mars Exploration Rovers (MER)’s automatic sky segmentation
system
- Further analysis of segmentation
• Our Approach
Contour technique to fit a contour to the bottom layer
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Automatic Surface and Bottom Layer
Selection
• Surface Selection
Same as Edge Detection technique
• Bottom Selection
- Data preprocessing
- EdgeCosts = 1/√(1+Gradient Magnitude)
- Creating an Image Gradient for upward
force
- Adding the edge cost image and
upward force image
Figure: Edge cost image,
enforcing low cost for strong
edges and high cost for noise
regions
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Bottom Layer Selection Continues…
Initialization
 The contour is allowed to adapt
until it reaches equilibrium
 2N+1 window (N = 50 pixels) is
maintained
 Window utilized for computing
local stiffness to instill continuity and
smoothness during adjustment
 Determination of Lowest cost (0)
pixels and Highest cost pixels (1)
Figure: Combined edge cost and upward cost
images
 This technique allows the contour
to fit to the image and bridge gaps in
the bedrock layer
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Contour Cost Window & Active Contour
Figure: contour stiffness cost window during processing (left) for the contour’s configuration during the 75 th iteration
(right), illustrating how the contour is encouraged to make smooth transitions from trace-to-trace.
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Final Adjustments
 TotalCostWindow(t) = EdgeCosts(s) + α x
UpwardCosts(t) + β x ContourStiffnessCosts(t)
 The minimal pixel location at each trace is
selected as the contour’s starting configuration
 If the configuration does not change between
iterations, or 500 iterations have been
processed, the contour is determined to have
reached equilibrium
 Ice thickness is computed for each trace by
converting pixels for the bedrock selection to a
depth in meters and subtracting it from the
surface depth for each trace
Figure: Echogram with overlaid
automatically selected surface(top, red) and
bedrock (middle, blue) layers using the active
contour method. Green is the initial contour
configuration.
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Active Contour Configuration
Figure: Example contour adaptation sequence throughout processing, illustrating how the contour adapts to the
bedrock interface and fits itself to the most salient edge near the bottom of the image
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Experimental Results
• Processed in Matlab
• Data were 15 random subsets of 75 extended flights from
Greenland.
• Range from 800-3000 rows and 1750-14500 columns (traces).
• Previous manual selection method took roughly 45 minutes per file
with approx 7500 columns per file.
• Automated edge-based method takes 15 seconds per file.
• Active contour (snake) method takes 2.5 minutes per file.
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Results
• We assumed that the human approach was 100% accurate
• Selection is considered correct if it is within 5% of the human
selection
• There are drawbacks with the manual approach
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Edge-Based Method
• This method differs slightly from the active contour results even
though both used same technique
• No Continuity
Active Contour Method
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Method is able to outperform the edge-based method
Takes a little longer to process images
Smooth
Continuous
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Edge Method vs. Contour Method
Gap in bedrock
Contour method bridges
the gap
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Continued Example
Plotted points above the bedrock
Active Contour method rids the echogram
on non-continuous plotted pixels
Plotted pixels below actual
bedrock
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Continued Example
Edge-detection method works better
Artifact/Noise in the bedrock layer
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Human Expert vs. Edge Method
vs. Active Contour Method
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QUESTIONS / COMMENTS
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