Climate models that predict polar ice sheet behavior require accurate measurements of the bedrock-ice and ice-air boundaries in ground-penetrating radar imagery. Identifying these features is typically performed by hand, which can be tedious and error prone. We propose an approach for automatically estimating layer boundaries by viewing this task as a probabilistic inference problem. Our solution uses Markov-Chain Monte Carlo to sample from the joint distribution over all possible layers conditioned on an image. Layer boundaries can then be estimated from the expectation over this distribution, and confidence intervals can be estimated from the variance of the samples. We evaluate the method on 560 echograms collected in Antarctica, and compare to a state-ofthe-art technique with respect to hand-labeled images. These experiments show an approximately 50% reduction in error for tracing both bedrock and surface layers.

The near surface layer signatures in polar firn are preserved from the glaciological behaviors of past climate and are important to understanding the rapidly changing polar ice sheets. Identifying and tracing near surface internal layers in snow radar echograms can be used to produce high-resolution accumulation maps. Layers, however, are manually traced in large data volumes requiring time-consuming, dense handselection and interpolation between sections.We have developed an approach for semi-automatically estimating near surface internal layers in snow radar echograms acquired from Antarctica. Our solution utilizes an active contour model (called snakes) to find high-intensity edges likely to correspond to layer boundaries, while simultaneously imposing constraints on smoothness of layer depth and parallelism among layers.

The dynamic responses of the polar ice sheets in Greenland and Antarctica can have substantial impacts on sea level rise. Understanding the mass balance requires accurate assessments of the bedrock and surface layers, but identifying each layer is performed subjectively by time-consuming, dense hand selection. We have developed an approach for semi-automatically estimating bedrock and surface layers from radar depth sounder imagery acquired from Antarctica. Our solution utilizes an active contours method ("level sets") to propagate an initial estimation of a layer's position based upon curvature and image intensity gradients. This allows the initial curve to gravitate with topological changes while providing smooth boundaries for discriminating between bedrock and surface layers. We evaluated the proposed semi-automatic method on 20 images with respect to hand labeled ground-truth. Compared to an automatic technique, our approach reduced labeling error by factors of 5 and 3.5 for tracing bedrock and surface layers, respectively.

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