Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm

Abstract

Radar sounders are unique instruments for subsurface investigation in both terrestrial and space applications. They are widely employed for monitoring changes to the polar ice sheets and for the study of planetary bodies (e.g., Mars). The analysis of the very large amount of data produced by such systems requires the development of automatic techniques for an objective, accurate, and fast extraction of relevant information from radargrams. In this paper, we propose a novel technique for the automatic detection of layer boundaries based on a local scale hidden Markov model (HMM), which models the radar response in the presence of a layer boundary, and the Viterbi algorithm (VA, which performs the inference step). The proposed technique is based on a divide and conquer strategy that executes the VA using the observation data and the HMM to infer the most likely layer boundary location within a small radargram portion. Finally, a detection strategy is defined to chain together the inferred local layer locations. Furthermore, a novel radargram enhancement and denoising technique tailored to support the detection step is presented. The effectiveness of the proposed technique has been confirmed by testing it on different radargrams acquired by shallow radar over the north pole of Mars. The results obtained point out the superiority of the proposed method in retrieving the position of each layer boundary (and thus of the related intensity and geometric properties) with respect to the state-of-the-art techniques.