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Automatic Ice Thickness Estimation from Polar Subsurface Radar Imagery. 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
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Automatic Ice Thickness Estimation from Polar Subsurface Radar Imagery
Radar and Acoustic sensors:
Challenges in Radar Sounding:
Greenland's ice sheet
Knowing the surface and bedrock in the radar images:
Four outlet Glaciers studied by CReSIS researchers.
Overview of Radar Remote Sensing
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.
Figure shows CReSIS picking software, the surface return is fully picked, while bedrock return is partially picked.
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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.
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.
- Mars Exploration Rovers (MER)’s automatic sky segmentation system
- Further analysis of segmentation
Contour technique to fit a contour to the bottom layer
Same as Edge Detection technique
- 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
Bottom Layer Selection Continues…
Figure: Combined edge cost and upward cost images
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.
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.
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 Method
Gap in bedrock
Contour method bridges the gap
Plotted points above the bedrock
Active Contour method rids the echogram on non-continuous plotted pixels
Plotted pixels below actual bedrock
Edge-detection method works better
Artifact/Noise in the bedrock layer