In the radar sounder literature, extracting subsurface geological information relies on supervised deep learning with large labeled datasets. While some methods reduce the need for extensive labels through weak supervision, there remains a gap in the availability of unsupervised segmentation techniques. This paper proposes a novel method for unsupervised radargram segmentation based on incremental learning (IL). The method involves the prior geophysical modeling of the cryosphere subsurface targets into a class hierarchy. Through several IL steps, a network is trained to progressively extract semantically meaningful features that are analyzed to compute the segmentation map. Each step refines the segmentation map by considering the new targets from the following level of the class hierarchy that details the targets at the previous level. To enhance the training process, contrastive learning is incorporated, along with techniques for distilling information from prior iterations to recall the network the properties of previously seen classes. To validate the effectiveness of the proposed method, we conducted successful experiments on MCoRDS-3 data acquired in Greenland.

Radar sounder (RS) profiles are essential for imaging the subsurface of planetary bodies and the Earth as they provide valuable geological insights. However, the limited availability of high-resolution radargrams poses challenges. This article proposes a novel method based on generative models to super-resolve radargrams. Our approach addresses the ill-posed and ill-conditioned nature of the super-resolution problem by training a neural network to learn the correlation between radargrams at different scales. The network learns a proxy for the mapping function between ambiguous low-resolution radargrams and more detailed high-resolution ones, considering the data’s geological and statistical properties. The mapping function enables the super-resolution of previously unseen low-resolution radargrams acquired in comparable conditions to those in the training and imaging similar underlying geology. To achieve this, we adopt a cycle generative adversarial network (CycleGAN), explicitly designed to match properties between low- and high-resolution radargrams, accounting for variations in dimensions and radiometric properties. Furthermore, we enhance the network performance by incorporating skip connections, a ResNet module, and attention mechanisms. The proposed method is validated using MCoRDS3 radargrams acquired in Greenland and Antarctica as high-resolution data. As low-resolution data, we used simulated radargrams representing what is expected by an Earth-orbiting low-resolution RS to have a controlled experiment. The results are evaluated qualitatively and quantitatively, focusing on the areas with reflections with complex shapes that may generate artifacts and unrealistic geological features.

We present the results of a preliminary statistical analysis and classification of solar radio burst candidates detected by the Mars Reconnaissance Orbiter (MRO) Shallow Radar (SHARAD). We first analyze the histograms of the MRO SHARAD burst candidates as a function of MRO-STEREO true anomaly difference and received peak power. We then show the results of performing logistic regression to classify the MRO SHARAD burst candidates. Our results highlight the need for additional burst data to further refine the classifier, additional parameters to determine if bursts are present, and potentially explore a different classification technique to assign burst candidates with improved accuracy. Analyzing SRBs detected by MRO/SHARAD (as a potential additional solar radio-observatory) would enhance our understanding of solar radio burst propagation physics and behavior. We conclude by discussing the potential application, and challenges, of using these bursts as a source for subsurface radio sounding for future terrestrial and Mars missions.

Analyzing radar sounder (RS) profiles allows the retrieval of critical information on subsurface geology. However, radar-grams suffer from several noise contributions, adversely affecting the data quality and reliability. In the remote sensing literature, there are no methods for denoising radargrams, and those for optical data denoising and SAR and GPR data de-speckling are based on assumptions that are not valid in the RS domain. This paper analyses the statistical distributions of the noisy contributions in radargrams at different levels of processing. It proposes a novel method to denoise complex raw radargrams using a generative network (diffusion probabilistic model) that learns the noise statistical properties. The model is iteratively trained to learn the information loss as a function of the noise level increment in the data. By reversing the process, the network estimates the noise statistical properties and denoises unseen radargrams. The method has been successfully validated on the raw Experiment Data Record (EDR) radargrams of Mars that the Shallow Radar (SHARAD) acquired.

Airborne Radar Sounders (RSs) are active sensors that acquire subsurface data for Earth observation. RS data (radargrams) provide information on buried geology by imaging subsurface dielectric discontinuities. Recently, several automatic RS target identification techniques have been proposed, being convolutional neural network (CNN)-based methods the most promising. However, they require numerous labeled data that are hard to retrieve in the subsurface environment targeted by RS. Further, they are not designed to effectively deal with problems showing unbalanced classes like RS segmentation. We introduce newer cryosphere subsurface targets in the inland and coastal areas that can have a very low probability. To deal with the higher complexity and variability than previous works, we propose a transfer learning framework for RS data to mitigate the need for a large amount of labeled data and handle extremely unbalanced target classes. Herewith, we propose two transfer learning-based mechanisms for radargram segmentation. The first uses a lightweight architecture whose pre-training is supervised with a large labeled dataset from other domains. The second mechanism uses a deep architecture pre-trained in the RS domain, considering the pretest task of radargram reconstruction. The architectures are modified to deal with the characteristics of RS data and the radargram segmentation task. Finally, both methods are fine-tuned with a few labeled radargrams to learn radargram features useful for segmentation. We reveal experimental results on radargrams acquired in Antarctica by MCoRDS-1 and MCoRDS-3. The results demonstrate the effectiveness of transfer learning for radargram segmentation.

Radar sounders (RSs) are widely used to image profiles (radargrams) of the subsurface of planetary bodies and the Earth. However, despite the huge scientific return from radargram analyses, their horizontal and vertical resolutions are limited by technical factors. Even if methods exist for improving the resolution, these are still limited by technical factors and introduce artifacts. This paper proposes an unsupervised deep-learning method that synthesizes accurate super-resolved radargrams overcoming these limitations. The method adopts the Cycle-Consistent Adversarial Network (CyleGAN) that learns the mapping function between the low- and high-resolution data distributions. The network is adapted to match the low- and high-resolution radargram characteristics, including the differences in dimensions and radiometric properties. The proposed method was successfully validated on airborne data at higher resolution and simulated data with lower resolution.

During the last decades, radar sounders provided direct measurements (radargrams) of the Earth’s polar caps’ subsurface. Radargrams are of critical importance for a better understanding of glaciologic structures and processes of the ice sheet in the framework of climate change. This article aims to automatically extract information on basal boundary conditions given their substantial relevance for modeling the ice-sheet processes, such as the sliding. We introduce a novel automatic method based on deep learning to detect the basal layer and basal units in radargrams acquired in the inland of icy areas. Radargrams are segmented into englacial layers, bedrock, basal units, and noise-limited regions; the latter includes the echo-free zone (EFZ), thermal noise, and signal perturbation. The network is a U-Net with attention gates and the Atrous Spatial Pyramid Pooling (ASPP) module that automatically extract semantically meaningful features at different scales. Experimental results on two datasets acquired in north Greenland and west Antarctica by the Multichannel Coherent Radar Depth Sounder (MCoRDS3) indicate a high overall segmentation accuracy. The accuracy of basal ice and signal perturbation detection is high, and that of the other classes is comparable with the literature techniques based on handcrafted features. The results show the effectiveness of the proposed method in automatically extracting semantically meaningful features to segment radargrams and map the basal layer and basal units.

Lava tubes are buried channels that transport thermally insulated lava. Nowadays, lava tubes on the Moon are believed to be empty and thus indicated as potential habitats for humankind. In recent years, several studies investigated possible lava tube locations, considering the gravity anomaly distribution and surficial volcanic features. This article proposes a novel and unsupervised method to map candidate buried empty lava tubes in radar sounder data (radargrams) and extract their physical properties. The approach relies on a model that describes the geometrical and electromagnetic (EM) properties of lava tubes in radargrams. According to this model, reflections in radargrams are automatically detected and analyzed with a fuzzy system to identify those associated with lava tube boundaries and reject the others. The fuzzy rules consider the EM and geometrical properties of lava tubes, and thus, their appearance in radargrams. The proposed method can address the complex task of identifying candidate lava tubes on a large number of radargrams in an automatic, fast, and objective way. The final decision on candidate lava tubes should be taken in postprocessing by expert planetologists. The proposed method is tested on both a real and a simulated data set of radargrams acquired on the Moon by the Lunar Radar Sounder (LRS). Identified candidate lava tubes are processed to extract geometrical parameters, such as the depth and the thickness of the crust (roof).

A satellite mission onboard a radar sounder for the observation of the earth’s polar regions can greatly support the monitoring of the cryosphere and climate change analyses. Several studies are in progress proposing the design and demonstrating the performance of such an earth-orbiting radar sounder (EORS). However, one critical aspect of the cryospheric targets that are often ignored and simplified in these studies is the complex geoelectrical nature of the polar ice. In this article, we present a performance assessment of the polar ice target detectability by focusing on their realistic representation. This is obtained by simulating the orbital radargrams corresponding to different regions of the polar cryosphere by leveraging the data available from airborne campaigns in Antarctica and Greenland. We propose novel performance metrics to analyze the detectability of the internal reflecting horizons (IRHs), the basal interface, and to analyze the nature of the basal interface. This performance assessment strategy can be applied to guide the design of the signal-to-noise ratio (SNR) budget at the surface, which can further support the selection of the main orbital instrument parameters, such as the transmitted power, the two-way antenna gain, and the processing gains.

This paper presents the SaTellite RAdar sounder for earTh sUbsurface Sensing (STRATUS), which is a satellite mission for Earth Observation (EO) with an onboard instrument capable of probing the Earth's subsurface in polar and arid regions. STRATUS is based on an innovative distributed radar sounder (RS) with the unique capability to obtain continuous and large-scale subsurface measurements, with homogeneous and consistent quality in two of the least characterized and crucial frontiers of Earth: globally on the polar ice sheets, i.e., Greenland and Antarctica (primary objective), and regionally on the arid areas and deserts. STRATUS is a ground-breaking exploratory mission addressing crucial scientific questions. It provides new fundamental data that have not been acquired by any other past or present remote sensing mission on the Earth, with an expected high and genuine scientific return enabling the assessment of the climate change signature in the Earth subsurface.

Radar sounder data are widely used for investigating geological structures and processes in the subsurface of icy and arid areas. Visual interpretation is one of the main techniques used in the literature to extract information from radargrams. There exist some automatic approaches but mostly supervised. However, no methods exploit deep learning in an unsupervised way. Here, we propose an automatic and unsupervised technique for extracting information on the subsurface geological targets. The technique is built upon three steps: i) generation of a coarse segmentation map based on the radargram statistical properties, ii) refinement of the coarse map with deep learning to detect target reflections, and iii) analysis of the deep features to identify buried targets. We tested the proposed method on MARSIS radar data acquired near the South Pole of Mars. The experimental results prove the effectiveness of the proposed method.

Radar sounders (RSs) provide information on the subsurface of the cryosphere through the use of electromagnetic (EM) signals by producing radargrams. Radargrams are used to detect and analyze relevant targets in the subsurface of icy regions. Up to now, studies of the subsurface structure of the cryosphere with radargrams have been conducted manually or applying semiautomatic techniques. However, these techniques present efficiency and adaptability disadvantages. To overcome these issues, we propose automatic analysis techniques for radargrams of icy regions based on deep learning (DL). Experimental analysis is conducted for the automatic segmentation of areas of interest in radargrams of ice shelves of coastal areas acquired by the radar sounder MCoRDS2.

This paper presents the Subsurface Radar Sounder (SRS) instrument onboard European Space Agency's (ESA) EnVision mission. EnVision is one of the three candidates selected for the Cosmic Vision 2015-2025 M5 medium-class missions. It is aimed at exploring the activity, the geologic history and the atmosphere of Venus. SRS is an orbital ground-penetrating radar with the unique science objectives of understanding the evolution of Venus' surface by searching for subsurface dielectric interfaces in the top hundreds of metres of the crust. In the paper, we describe the main science objectives of SRS, the performance evaluation under expected target conditions, the instrument design and the acquisition strategy that maximize the scientific returns.

Radar sounders (RS) can be used to acquire data on ice sheets and provide direct evidence of the structures in the subsurface. Many acquisitions are available from airborne RS in the Antarctica and Greenland. However, airborne data are costly, have limited spatial coverage, and nonhomogeneous characteristics. To overcome these limitations, a potential satellite-mounted RS could provide uniform coverage and consistent data quality at the cost of lower resolution and higher path loss. In this paper, we assess the performance of a possible Earth-orbiting RS by simulating and analyzing its radargrams. The simulation approach reprocesses existing airborne RS to match the orbital RS characteristics. The simulated radargrams are analyzed to estimate the losses and understand the detection performance of icy targets using state-of-the-art data analysis techniques. The preliminary analysis of the simulated radargrams indicates that, under the simplified assumptions, an orbiting RS will be capable of imaging the investigated subsurface targets.

The Italian Space Agency (ASI) has recently entrusted CO.RI.S.T.A. with the development of a radar system that can be mounted onboard small airplanes or helicopters and may operate, at different frequencies belonging to the P-Band, either as Synthetic Aperture Radar (SAR) or as Sounder. In this work, we present preliminary results of the helicopter-borne desert campaign carried out with this system in 2018 over the Erfoud area, Morocco, in the frame of a project that has involved different public Italian Research Institutes and Universities.

Lunar lava tubes are buried channels that contained thermally insulated lava during the volcanic period of the Moon. Nowadays, they are believed to be empty and thus, identified as potential habitats for humans. In recent years, numerous studies investigated the possible locations of these tubes by taking into account the distribution of gravity anomalies and the volcanic features of the surface. In this paper, we model lava tubes according to their electromagnetic behavior, and we propose a novel approach to locate lava tubes and estimate their physical properties. The method analyzes the subsurface reflections stored in radargrams to extract the desired features automatically. Then, these features and their relationships are processed by a fuzzy rule-based system to detect the presence or absence of lava tubes. The strategy was implemented and successfully tested on simulated radargrams with various surface properties and tunnel dimensions.