Automatic Ice Thickness Estimation from Polar Subsurface Radar Imagery
Christopher M.
1
Gifford ,
Gladys
1
Finyom ,
Michael
1
Jefferson ,
MyAsia
1
Reid ,
Eric L.
2
Akers ,
Arvin
1
Agah
1Center
for Remote Sensing of Ice Sheets, University of Kansas, Lawrence, KS
2Mathematics and Computer Science Department, Elizabeth City State University, Elizabeth City, NC
I. INTRODUCTION
III. CHALLENGES OF PROCESSING RADAR DATA
VI. EDGE DETECTION AND FOLLOWING APPROACH
Remote sensing methods:
• Automated processing and extraction of high level information
from radar imagery is challenging
Introduction:
CReSIS uses radar and seismic to acquire
subsurface data from a remote location (i.e.,
surface, air, or space)
Radar and seismic sensors:
Edge detection, thresholding, edge connecting and following
AUTOMATIC SURFACE & BOTTOM LAYER SELECTION
VIII. EXPERIMENTAL SETUP
• Clutter contributes to incomprehensible image regions
[Assumption] Surface is max value in each trace
Surface Selection:
• Both methods implemented in Matlab
• Bed topography varies from trace to trace due to rough bedrock
interfaces from extended flight segments
[Assumption] Bedrock is deepest contiguous layer in image
• A strong surface reflection can be repeated with an identical
shape in an image, called a surface multiple, due to energy
reflecting off the ice sheet surface and back again
Used to gather data about the internal and
bottom layers of ice sheets, from the surface
Surfaced and airborne radio echo sounding
of Greenland and Antarctica ice sheets:
• Faint or non-existent bedrock reflections occur from:
Determine ice sheets thickness
Bedrock topography (smooth, rough)
• Rough surface and bedrock topography
Mass balance of large bodies of ice
• Presence of water on top of or internal to the ice sheet
Rough surface interface
Stages of melting (surface, internal)
Variations of ice thickness and topography
Processing subsurface radar data:
Ultimately used for scientific community
Goal:
Automating task of estimating ice thickness
Figure shows CReSIS picking software, the surface return is fully picked, while bedrock return is partially picked.
Accurately selecting ice sheet’s surface, and
interface between the ice and bedrock
IV. ICE THICKNESS ESTIMATION FROM RADAR
• Ice thickness is needed for scientists to:
Knowing surface, bedrock in radar imagery:
Helps compute the ice thickness
• Study mass balance
Helps ice sheet studies, their volume, and how
they contribute to climate change
• Sea level rise
Four outlet Glaciers studied by CReSIS researchers.
Leigh Stearns
•
•
•
•
•
•
•
•
•
•
Radars transmit energy in form of a pulse from an antenna,
energy reflects off of targets, and is received by an antenna
• Previous manual selection method took ~45
minutes per file with ~7500 columns per file
Add the edge cost image and upward force image
• Automated edge-based method takes ~15
seconds per file
• Active contour (snake) method takes ~2.5
minutes per file
Traces processed in bottom-up fashion until strong edge is found
AUTOMATIC SURFACE & BOTTOM LAYER SELECTION
IX. EXPERIMENTAL RESULTS
Surface Selection:
• Assumed human selections 100% accurate
• Automatic selection is considered correct if it is
within 5% of the human selection
Figure: Edge cost image, enforcing low cost for
strong edges and high cost for noise regions
Figure: Combined edge cost and upward cost images
• Contour initialization procedure
Preprocessed by:
• The contour is allowed to adapt until it reaches equilibrium
EDGE-BASED METHOD
Detrending
• 2N+1 window (N = 50 pixels) is maintained
Low-pass filtering
• Window utilized for computing local stiffness to instill continuity
and smoothness during adjustment
• This method differs slightly from the active
contour results even though both used general
gradient magnitude technique
• Determine lowest cost (0) pixels and highest cost (1) pixels
• No continuity aspect causes method to suffer
Contrast adjustment
Figure: Echogram that has been
preprocessed using detrending, lowpass filter, and contrast enhancement
Figure: Normalized echogram gradient
magnitude, showing the image edges
• Allows contour to fit to the bedrock layer and bridge faint gaps
• Drawback: takes longer to process images
• Environmental and human impacts
• Surface selected based on the first and largest reflection return
Ground Penetrating Radar able to observe properties of
subsurface, ranging from soil, sand, rock, snow, and ice
• Experts tend to skip traces (e.g., 40 between selections) to
speed up the process
• Bedrock more challenging due to being possibly buried in noise
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
• Smooth and continuous aspects beneficial
Figure: 2D derivative of Gaussian convolution kernels (1.5 ) for computing vertical
(left) and horizontal (right) image gradients.
CLEANED EDGE IMAGE & RESULT IMAGE
• Causes errors and inconsistencies which vary over time
Figure: contour stiffness cost window during processing (left) for the contour’s configuration during the 75th iteration
(right), illustrating how the contour is encouraged to make smooth transitions from trace-to-trace.
• Thousands of images  manual approach becomes impractical
Column
CONTOUR ADJUSTMENT
V. RELATED WORK
TotalCostWindow(t) = EdgeCosts(s) +
Finding / Following Ice Sheet Internal Layers:
Targets are internal layering in the ice sheets, and a strong
echo return from bedrock beneath the ice
α x UpwardCosts(t) +
• Predicts depth in certain layers
β x ContourStiffnessCosts(t)
• Focus on the Eemian Layer in Greenland ice sheet
Interface below the surface (3.5 km or deeper) requires
great transmit power and sensitive receive equipment
because of energy loss within ice and with depth
• Utilized Monte Carlo Inversion flow model to estimate unknown
parameters guided by internal layers
Each measurement is called a radar trace, consisting of
signals representing energy due to time (larger time 
deeper reflections)
• Layers, contours, and curves are discovered using image
processing and computer vision methods
• Adaptive contour (snake) fitting, where an image is a cost grid
and the contour properties are measured as energy
Each row corresponds to a depth and time for a
measurement, as the depth increases further down
• Medical imagery (MRIs and CAT scans)
A flight segment , called an echogram, consist of a
collection of traces which represent all the columns of the
image, from the beginning (left) to the end (right)
EDGE METHOD VS. CONTOUR METHOD
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.
VII. ACTIVE CONTOUR, COST MINIMIZATION
Similar Work:
Edge / Layer / Contour Identification:
In an image, a trace is an entire column of pixels, where
each pixel represents a depth
ACTIVE CONTOUR METHOD
• Method outperforms edge-based method
CONTOUR COST WINDOW
• Several experts were utilized to manually select layers (time)
Pixel
• There are several drawbacks with the manual
approach (e.g., tired, inconsistent, interpolate)
Bottom Selection:
2D DERIVATIVE OF GAUSSIAN KERNEL
Distance measured based on energy travel time back from
targets (target reflection intensity and depth information)
Water surrounded by the ice, and frozen ice against the
bedrock both represent strong reflecting interfaces
Seed regions reach image edge or threshold  horizon found
Create Image Gradient for upward force
• Ice thickness is computed by selecting the surface and bedrock
reflections in pixel/depth coordinates, for each trace, and
subtracting their corresponding depths
II. OVERVIEW OF RADAR REMOTE SENSING
•
EdgeCosts = 1/√(1+Gradient Magnitude)
The depth corresponding to the max value of each trace is
selected as location of surface reflection
Greenland ice sheet
Requires knowledge about sensing medium
•
Grow “seeds” from low variance sections in image sky regions
Extracting the location of the ice sheet surface
NASA/Rob Simmon
Process:
• Range from 800-3000 rows and 1750-14500
columns (traces)
Data preprocessing
Skyline detector and segmentation:
Our Approach:
• These aspects produce gaps in the bedrock reflection layer which
must be connected to construct a continuous layer for complete
ice thickness estimation
Challenges in radar sounding ice sheets:
Bottom Selection:
Similar Work:
Identify week clouds in Mars Exploration Rover sky imagery
• Specific radar settings
• Data were 15 random subsets of 75 extended
flights from Greenland (May and June 2006)
Same as Edge Detection method
Mars Exploration Rovers (MER) automatic sky segmentation system
Image segmentation (watershed and level set methods)
Our Approach:
Adaptive contour technique to fit a continuous contour to the bedrock
layer using image and contour properties as costs
Figure: Echogram with overlaid automatically selected surface (top, red) and bedrock
(middle, blue) layers using the active contour method. Green is the initial contour.
ACTIVE CONTOUR CONFIGURATION
The lowest cost pixel location at each trace is
selected as the contour’s next configuration
If 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
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
HUMAN EXPERT VS.
EDGE METHOD VS. ACTIVE CONTOUR METHOD
Plotted points above the bedrock
Active Contour method rids the echogram of
non-continuous plotted pixels
Edge-detection method works better
Artifact/noise in the bedrock layer
A pixel width represents the track distance between traces,
and depends on the speed of the aircraft during the survey
EXAMPLE RADAR ECHOGRAM: GREENLAND 05/28/2006
Reflection intensities are strongest at
the surface and weaker because of
depth. Depth increases from left to right.
Zoomed Section
Gap in bedrock
Figure shows radar echogram over an ice sheet, illustrating the reflection of internal layers and the bedrock interface
beneath the ice sheet.
Contour method bridges the gap
Plotted pixels below actual bedrock
Center for Remote Sensing of Ice Sheets
www.cresis.ku.edu
This material is based upon work supported by the National Science Foundation under Grant No. ANT-0424589. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the author (s) and do not necessarily reflect the views of the National Science Foundation.