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.
Lee et al. REF present a more accurate technique that uses Gibbs sampling from a joint distribution over all possible layers.
Estimating bedrock and surface layer boundaries and confidence intervals in ice sheet radar imagery using MCMC
2014 IEEE International Conference on Image Processing (ICIP)
2014 IEEE International Conference on Image Processing (ICIP)
Computer Science