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.

中文翻译：

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基于无监督随机游走和用户引导标签传播的雷达探测器数据语义分割方法


雷达探测器 (RS) 用于分析地球和其他行星的地下。可以处理从 RS 获取的数据以获得雷达图，雷达图是二维阵列，包含雷达在向表面发送脉冲后接收到的反向散射回波功率。雷达图的研究为行星历史的地质解释和冰川地区冰层的监测提供了重要的见解。深度学习（DL）已成为雷达图自动特征提取和分析的强大工具；然而，它们仍然被视为传统图像。我们提出了一种基于两步方法的遥感数据语义分割的新颖方法。这种方法的基本原理是利用雷达图特征之间存在的空间水平相关性，这是区分这些数据与标准图像的重要属性。第一步，以无监督的方式训练编码器，利用随机游走来学习雷达图中序列特征的有意义的表示。在第二步中，很少的参考标记样本允许模型将标签传播到完整的雷达图。我们还引入了一种度量来量化特征之间的水平相关程度，并用它来查找极地沿海雷达图中的接地区域。我们在多通道相干雷达测深仪 (MCoRDS) RS 获得的两个数据集和轨道 RS 浅雷达 (SHARAD) 的数据集上测试了我们的方法，并讨论了非常有希望的结果。