An Approach to Semantic Segmentation of Radar Sounder Data Based on Unsupervised Random Walks and User-Guided Label Propagation

Radar sounders (RSs) are utilized for the analysis of subsurface of Earth and other planets. Data acquired from RS can be processed to obtain radargrams, which are 2-D arrays containing the backscattered echo power received by the radar after sending pulses toward the surface. The study of radargrams offers crucial insights for the geological interpretation of the history of planets and for the monitoring of ice layers in glacial regions. Deep learning (DL) has emerged as a powerful tool for the automatic feature extraction and analysis of radargrams; yet, they are still treated as conventional images. We propose a novel methodology for the semantic segmentation of RS data based on a two-step approach. The rationale of this methodology is exploiting the spatial horizontal correlation that exists among radargram features, which is an important property that distinguishes these data from standard images. In the first step, an encoder is trained in an unsupervised way, exploiting random walks to learn meaningful representations of sequential features within radargrams. In the second step, few reference labeled samples allows the model to propagate the labels to the full radargram. We also introduce a metric to quantify the degree of horizontal correlation among features, and we use it to find the grounding zone in coastal radargrams of polar areas. We test our methodology on two datasets obtained by the multichannel coherent radar depth sounder (MCoRDS) RS and a dataset from the orbital RS shallow radar (SHARAD) and we discuss the very promising results.