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.

Center for Remote Sensing of Ice SheetsCenter for Remote Sensing of Ice Sheetswww.cresis.ku.eduwww.cresis.ku.edu

Automatic Ice Thickness Estimation from Polar Subsurface Radar ImageryChristopher M. Gifford1, Gladys Finyom1, Michael Jefferson1, MyAsia Reid1, Eric L. Akers2, Arvin Agah1

1Center for Remote Sensing of Ice Sheets, University of Kansas, Lawrence, KS2Mathematics and Computer Science Department, Elizabeth City State University, Elizabeth City, NC

Greenland ice sheet

NASA/Rob Simmon

I. INTRODUCTIONRemote sensing methods:

CReSIS uses radar and seismic to acquire subsurface data from a remote location (i.e., surface, air, or space)

Radar and seismic sensors: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:

Determine ice sheets thicknessBedrock topography (smooth, rough)Mass balance of large bodies of ice

Challenges in radar sounding ice sheets:Rough surface interfaceStages of melting (surface, internal)Variations of ice thickness and topography

Processing subsurface radar data:Requires knowledge about sensing mediumUltimately used for scientific community

Goal: Automating task of estimating ice thickness

Process:Accurately selecting ice sheet’s surface, and interface between the ice and bedrock

Knowing surface, bedrock in radar imagery:Helps compute the ice thicknessHelps ice sheet studies, their volume, and how they contribute to climate change

Four outlet Glaciers studied by CReSIS researchers. Leigh Stearns

II. OVERVIEW OF RADAR REMOTE SENSING• Radars transmit energy in form of a pulse from an antenna,

energy reflects off of targets, and is received by an antenna• Distance measured based on energy travel time back from

targets (target reflection intensity and depth information)• Ground Penetrating Radar able to observe properties of

subsurface, ranging from soil, sand, rock, snow, and ice• 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 strong reflecting interfaces

• Targets are internal layering in the ice sheets, and a strong echo return from bedrock beneath the ice

• 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

• Each measurement is called a radar trace, consisting of signals representing energy due to time (larger time deeper reflections)

• In an image, a trace is an entire column of pixels, where each pixel represents a depth

• Each row corresponds to a depth and time for a measurement, as the depth increases further down

• 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)

• A pixel width represents the track distance between traces, and depends on the speed of the aircraft during the survey

Pixel

Column

Reflection intensities are strongest at the surface and weaker because of

depth. Depth increases from left to right.

Figure shows radar echogram over an ice sheet, illustrating the reflection of internal layers and the bedrock interface beneath the ice sheet.

EXAMPLE RADAR ECHOGRAM: GREENLAND 05/28/2006

III. CHALLENGES OF PROCESSING RADAR DATA• Automated processing and extraction of high level information

from radar imagery is challenging• Clutter contributes to incomprehensible image regions• Bed topography varies from trace to trace due to rough bedrock

interfaces from extended flight segments• 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

• Faint or non-existent bedrock reflections occur from:• Specific radar settings• Rough surface and bedrock topography• Presence of water on top of or internal to the ice sheet

• These aspects produce gaps in the bedrock reflection layer which must be connected to construct a continuous layer for complete ice thickness estimation

IV. ICE THICKNESS ESTIMATION FROM RADAR• Ice thickness is needed for scientists to:

• Study mass balance• Sea level rise• Environmental and human impacts

• Ice thickness is computed by selecting the surface and bedrock reflections in pixel/depth coordinates, for each trace, and subtracting their corresponding depths

• Several experts were utilized to manually select layers (time)• Surface selected based on the first and largest reflection return• Bedrock more challenging due to being possibly buried in noise• Experts tend to skip traces (e.g., 40 between selections) to

speed up the process• Causes errors and inconsistencies which vary over time• Thousands of images manual approach becomes impractical

Figure shows CReSIS picking software, the surface return is fully picked, while bedrock return is partially picked.

V. RELATED WORKFinding / Following Ice Sheet Internal Layers:• Predicts depth in certain layers• Focus on the Eemian Layer in Greenland ice sheet• Utilized Monte Carlo Inversion flow model to estimate unknown

parameters guided by internal layers

Edge / Layer / Contour Identification:• 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• Medical imagery (MRIs and CAT scans)

VI. EDGE DETECTION AND FOLLOWING APPROACHIntroduction:

Edge detection, thresholding, edge connecting and following[Assumption] Surface is max value in each trace[Assumption] Bedrock is deepest contiguous layer in image

Similar Work:Skyline detector and segmentation:

Grow “seeds” from low variance sections in image sky regions

Seed regions reach image edge or threshold horizon foundIdentify week clouds in Mars Exploration Rover sky imagery

Our Approach:Traces processed in bottom-up fashion until strong edge is foundAUTOMATIC SURFACE & BOTTOM LAYER SELECTION

Surface Selection:Extracting the location of the ice sheet surfaceThe depth corresponding to the max value of each trace is selected as location of surface reflection

Bottom Selection:Preprocessed by:

DetrendingLow-pass filteringContrast adjustment Figure: Echogram that has been

preprocessed using detrending, low-pass filter, and contrast enhancement

Figure: Normalized echogram gradient magnitude, showing the image edges

Figure: 2D derivative of Gaussian convolution kernels (1.5 ) for computing vertical (left) and horizontal (right) image gradients.

2D DERIVATIVE OF GAUSSIAN KERNEL

Figure: Echogram with overlaid automatically selected surface (top, red) and bedrock (middle, blue) layers using the edge-based method.

Figure: Cleaned edge image following thresholding, morphological closing and thinning operations

CLEANED EDGE IMAGE & RESULT IMAGE

VII. ACTIVE CONTOUR, COST MINIMIZATIONSimilar Work:

Mars Exploration Rovers (MER) automatic sky segmentation systemImage 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

AUTOMATIC SURFACE & BOTTOM LAYER SELECTIONSurface Selection:

Same as Edge Detection methodBottom Selection: Data preprocessing

EdgeCosts = 1/√(1+Gradient Magnitude)Create Image Gradient for upward forceAdd the edge cost image and upward force image

Figure: Edge cost image, enforcing low cost for strong edges and high cost for noise regions

• Contour initialization procedure• 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• Determine lowest cost (0) pixels and highest cost (1) pixels• Allows contour to fit to the bedrock layer and bridge faint gaps

Figure: Combined edge cost and upward cost images

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.

CONTOUR COST WINDOW

CONTOUR ADJUSTMENTTotalCostWindow(t) = EdgeCosts(s) + α x UpwardCosts(t) + β x ContourStiffnessCosts(t)

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: Echogram with overlaid automatically selected surface (top, red) and bedrock (middle, blue) layers using the active contour method. Green is the initial contour.

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

ACTIVE CONTOUR CONFIGURATION

VIII. EXPERIMENTAL SETUP• Both methods implemented in Matlab• Data were 15 random subsets of 75 extended

flights from Greenland (May and June 2006)• Range from 800-3000 rows and 1750-14500

columns (traces)• Previous manual selection method took ~45

minutes per file with ~7500 columns per file• Automated edge-based method takes ~15

seconds per file• Active contour (snake) method takes ~2.5

minutes per file

IX. EXPERIMENTAL RESULTS • Assumed human selections 100% accurate• Automatic selection is considered correct if it is

within 5% of the human selection• There are several drawbacks with the manual

approach (e.g., tired, inconsistent, interpolate)

EDGE-BASED METHOD• This method differs slightly from the active

contour results even though both used general gradient magnitude technique

• No continuity aspect causes method to suffer

ACTIVE CONTOUR METHOD• Method outperforms edge-based method • Drawback: takes longer to process images• Smooth and continuous aspects beneficial

EDGE METHOD VS. CONTOUR METHOD

Gap in bedrock Contour method bridges the gap

Plotted points above the bedrock

Plotted pixels below actual bedrock

Active Contour method rids the echogram of non-continuous plotted pixels Edge-detection method works better Artifact/noise in the bedrock layer

HUMAN EXPERT VS.EDGE METHOD VS. ACTIVE CONTOUR METHOD

Zoomed Section