Radar sounders mounted on airborne platforms have acquired data of the subsurface of the Earth's icy areas over the last decades. These data, called radargrams, contain information on the dielectric discontinuities in the ice-sheets, and thus on the buried geological structures and the related processes. Conventionally, these structures have been characterized and mapped by visually inspecting the radargrams. However, visual inspection is subjective and time-consuming and can lead to misinterpretations. Recently, state-of-the-art automatic techniques are proposed to map the position of the bedrock, the ice layering, and the noise in the radargram. However, there are no automatic techniques for mapping the basal refreezing, which is an important ice target that controls the rate of sea-ward ow of the ice-sheets. This paper proposes an automatic method to map the refreezing ice in radargrams. We model the refreezing ice considering its geophysical and radiometric properties. Then, we design a set of features considering this model to perform a classification of the radargrams into four classes, i.e., ice layering, echo-free zone (EFZ) and thermal noise, bedrock, and the refreezing ice. We applied the proposed method to radargrams acquired in the north Greenland by Multichannel Coherent Radar Depth Sounder (MCoRDS3), a radar sounder designed by the Center for Remote Sensing of Ice Sheets (CReSIS). The results indicate a good overall accuracy. The accuracy of refreezing ice is high, while that of the other classes is comparable with the state-of-the-art techniques. The results indicate the effectiveness of the proposed features in mapping the refreezing ice.
Subsurface investigation of the Jovian icy moons is expected to disclose interesting information on the Jovian system. The Radar for Icy Moon Exploration (RIME) is the instrument in charge of characterizing the subsurface of the three icy moons Ganymede, Europa and Callisto. To provide a key for interpretation for the real acquired data, simulations of different possible scenarios on Ganymede are presented in this work. In this paper, we present an approach to performance analysis of RIME based on the 3D modelling and electromagnetic simulations of selected icy moon targets. These simulations are carried out using the Finite-Difference Time-Domain (FDTD) technique, which has been used in recent years to support radar sounder applications. In this work, we analyze in detail some interesting targets: 1) the dark terrain regolith, 2) the bright terrain dielectric profile, and 3) the grooved bright terrain. Our analysis is performed in two levels. First, the contribution of individual features is analyzed, varying their geometry and composition to understand how the measured fields vary accordingly. Second, a more realistic geological arrangement of a combination of subsurface features is considered. The results are very promising and indicate that the subsurface response is detectable in most of the cases.
Thermal Infrared (TIR) remote sensing measures emitted radiation of Earth in the thermal region of electromagnetic spectrum. This information can be useful in studying sub-surface features such as buried palaeochannels, which are ancient river systems that have dried up over time and are now buried under soil cover or overlying sediments in the present landscape. Therefore they have little or no expression on the surface topography. Study of these paleo channels has wide applications in the ﬁelds of uranium exploration and ground water hydrology. Identifying paleo channels using remote sensing technique is a cost-eﬀective means of narrowing down search areas and thereby aids in ground exploration. The diﬀerence in thermal properties between the paleo channel-ﬁll sediments and the surrounding bed-rock is the key to demarcate these channels. This study uses ﬁve TIR bands of day-time Advanced Spaceborne Thermal Emission and Reﬂection Radiometer (ASTER) L1A data for delineation of paleo-systems in the DeGrussa area of the Capricorn Orogen in Western Australia. The temperature-emissivity separation algorithm is applied to obtain kinetic temperature and emissivity images. Sharp contrasts in kinetic temperature and emissivity values are used to demarcate the channel boundaries. Proﬁles of topographic elevation, temperature and emissivity values are plotted for diﬀerent sections of the interpreted channels and compared to distinguish the surface channels from sub-surface channels, and also to interpret the thickness and nature of the paleo channel-ﬁll sediments. The results are validated using core-drilling litho logs and ﬁeld exploration data.
Experimentations with applications of machine learning algorithms such as random forest (RF), support vector machines (SVM) and fuzzy inference system (FIS) to lithological classification of multispectral datasets are described. The input dataset such as LANDSAT-8 and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) in conjunction with Shuttle Radar Topography Mission (SRTM) digital elevation are used. The training data included image pixels with known lithoclasses as well as the laboratory spectra of field samples of the major lithoclasses. The study area is a part of Ajmer and Pali Districts, Western Rajasthan, India. The main lithoclasses exposed in the area are amphibolite, granite, calc-silicates, mica-schist, pegmatite and carbonates. In a parallel implementation, spectral parameters derived from the continuum-removed laboratory spectra of the field samples (e.g., band depth) were used in spectral matching algorithms to generate geological maps from the LANDSAT-8 and ASTER data. The classification results indicate that, as compared to the SVM, the RF algorithm provides higher accuracy for the minority class, while for the rest of the classes the two algorithms are comparable. The RF algorithm effectively deals with outliers and also ranks the input spectral bands based on their importance in classification. The FIS approach provides an efficient expert-driven system for lithological classification. It based on matching the image spectral features with the absorption features of the laboratory spectra of the field samples, and returns comparable results for some lithoclasses. The study also establishes spectral parameters of amphibolite, granite, calc-silicates, mica-schist, pegmatite and carbonates that can be used in generating geological maps from multispectral data using spectral matching algorithms.
Spectroscopic analysis is carried out for lithological discrimination in a study area in the Pali and Ajmer districts of Rajasthan, western India using laboratory-based, field-based and space-borne data. We first explored the feasibility of the Landsat-8 (Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS)) and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) imagery for lithological mapping. Laboratory spectra of the samples of rocks exposed in the area were collected using FieldSpec3 spectroradiometer in the VNIR-SWIR region and resampled to the LANDSAT 8 and ASTER spectral bands. The spectral angle mapper (SAM) algorithm was used to map the lithologies using the resampled laboratory spectra as references. The resulting map was validated based on field geological mapping. Fourier Transform Infra-Red (FTIR) spectroscopy of selected rock samples was carried out to correlate spectral absorption features in the TIR-IR regions with the vibrational energy.