Lunar Mapping Research Articles

Artificial intelligence in remote sensing geomorphology—a critical study

Planetary geomorphological maps over a wide range of spatial and temporal scales provide important information on landforms and their evolution. The process of producing a geomorphological map is extremely time-consuming and maps are often difficult to reproduce. The success of deep learning and machine learning promises to drastically reduce the cost of producing these maps and also to increase their reproducibility. However, deep learning methods strongly rely on having sufficient ground truth data to recognize the wanted surface features. In this study, we investigate the results from an artificial intelligence (AI)–based workflow to recognize lunar boulders on images taken from a lunar orbiter to produce a global lunar map showing all boulders that have left a track in the lunar regolith. We compare the findings from the AI study with the results found by a human analyst (HA) who was handed an identical database of images to identify boulders with tracks on the images. The comparison involved 181 lunar craters from all over the lunar surface. Our results show that the AI workflow used grossly underestimates the number of identified boulders on the images that were used. The AI approach found less than one fifth of all boulders identified by the HA. The purpose of this work is not to quantify the absolute sensitivities of the two approaches but to identify the cause and origin for the differences that the two approaches deliver and make recommendations as to how the machine learning approach under the given constraints can be improved. Our research makes the case that despite the increasing ease with which deep learning methods can be applied to existing data sets, a more thorough and critical assessment of the AI results is required to ensure that future network architectures can produce the reliable geomorphological maps that these methods are capable of delivering.

REALMS: Resilient exploration and lunar mapping system.

Space resource utilisation is opening a new space era. The scientific proof of the presence of water ice on the south pole of the Moon, the recent advances in oxygen extraction from lunar regolith, and its use as a material to build shelters are positioning the Moon, again, at the centre of important space programs. These worldwide programs, led by ARTEMIS, expect robotics to be the disrupting technology enabling humankind's next giant leap. However, Moon robots require a high level of autonomy to perform lunar exploration tasks more efficiently without being constantly controlled from Earth. Furthermore, having more than one robotic system will increase the resilience and robustness of the global system, improving its success rate, as well as providing additional redundancy. This paper introduces the Resilient Exploration and Lunar Mapping System, developed with a scalable architecture for semi-autonomous lunar mapping. It leverages Visual Simultaneous Localization and Mapping techniques on multiple rovers to map large lunar environments. Several resilience mechanisms are implemented, such as two-agent redundancy, delay invariant communications, a multi-master architecture different control modes. This study presents the experimental results of REALMS with two robots and its potential to be scaled to a larger number of robots, increasing the map coverage and system redundancy. The system's performance is verified and validated in a lunar analogue facility, and a larger lunar environment during the European Space Agency (ESA)-European Space Resources Innovation Centre Space Resources Challenge. The results of the different experiments show the efficiency of REALMS and the benefits of using semi-autonomous systems.

Integral equation modeling for dielectric retrieval of the lunar surface using Chandrayaan-2 fully-Polarimetric L-band dual frequency SAR (DFSAR) data

Our closest neighbor (Moon) is full of wonders and potentials that can help us learn more about the solar system and provide a habitat for space colonies. The lunar surface contains several potential volatiles implanted by the solar wind that can be used for In-Situ Resource Utilization (ISRU). Also by characterizing the lunar surface we can comprehend many solar system phenomenon. Radar has been proven to be very effective in characterizing and understanding the physical properties of the lunar surface. The success of Chandrayaan-2 (CH-2) in 2019 opened numerous doors to better understanding the lunar surface. The Dual Frequency Synthetic Aperture Radar (DFSAR), is the first L-band SAR ever used for lunar mapping. We approximated the dielectric values for the Permanently Shadowed Regions (PSRs) by combining the Integral Equation Model (IEM) and Artificial Neural Network (ANN). The two-dimensional Gaussian model proved to be more accurate when correlated with in-situ measurements made by the Apollo 17 mission out of four different configurations, but the validation process has its limitations, thus we analyzed the result from all the models. The real part of complex dielectric values of unknown craters near Malapert crater (PSRs SP 83260 0090770 and SP 841420 0059530) and Faustini crater (PSR SP 871460 0840750) were high in certain sections of the region and moderate in others. The high dielectric values of 11.2 and 10.9 were estimated by the two-dimensional Gaussian and Exponential model for the right-side walls of the PSRs SP 83260 0090770 and SP 841420 0059530, respectively. For three-dimensional Gaussian and Exponential models, the same region but with fewer pixels showed high dielectric values of approximately. However, the estimated dielectric value for the left side wall was around 3.4 for two-dimensional models and 2.3 for three-dimensional models. On the left side of the region, the PSR SP 871460 0840750 had high dielectric values of 10.6 and 10.2, as calculated by the two-dimensional Gaussian and Exponential models and approximately 13 for the three-dimensional models. The right side of the region showed approximately 3.6 dielectric values for two-dimensional models and 4 for three-dimensional models. We noticed that some pixel values showed ambiguous dielectric values (<1) during the retrieval and validation process, notably in three-dimensional models, which could be linked to incidence angle variability and model sensitivity. This indicates the model's limitation and is treated as outliers, which we rectified by optimizing the threshold. Overall, the research brought attention to the intriguing dielectric constant variation in PSRs.

A Generative Adversarial Network for Pixel-Scale Lunar DEM Generation from High-Resolution Monocular Imagery and Low-Resolution DEM

Digital elevation models (DEMs) provide fundamental data for scientific and engineering applications in lunar exploration missions. Lunar DEMs have been mainly generated by laser altimetry and stereophotogrammetry. Complementarity to stereo photogrammetry, reflection-based surface reconstruction methods, such as shape from shading (SFS), have been studied and applied in lunar DEM reconstruction from a single image. However, this method often suffers from solution ambiguity and instability. In this paper, we propose a generative adversarial network (GAN)-based method that is able to generate high-resolution pixel-scale DEMs from a single image aided by a low-resolution DEM. We have evaluated the accuracy of the reconstructed high-resolution DEMs from 25 LROC NAC images of four regions using LROC NAC DEMs (2 m/pixel) as ground truth. The experimental results demonstrate good accuracy and adaptability to changes in illumination conditions. The root mean square error (RMSE) can reach about 2 m in areas where the degree of elevation variation is less than 100 m, and the RMSE value ranges from around 3 m to 10 m without considering the degree of the elevation variation in large-area reconstruction. As high-resolution monocular images and low-resolution DEMs are available for the entire lunar surface, the proposed GAN-based method has great potential in high-resolution lunar DEM reconstruction for lunar mapping applications.

The Moon in the Microwave: Shedding New Light on the Lunar Farside

AbstractA new lunar map was produced by subtracting the nocturnal microwave radiance data from two frequency channels with different penetrative potential. Chang’E−2 microwave radiometer (MRM) data show areas of anomalous high differences relating to both known and unknown young impact craters, most of which are located on the lunar farside. Thirty‐eight Features Of Interest (FOI) were selected and investigated by comparison with soil maturity maps (OMAT) and infrared‐derived data (H–Parameter), a parameter relating to thermal inertia. In some cases, the surface area of the microwave feature reaches distances of over 40 units of crater radii from the rim before abruptly dropping to background level within less than 10 km. The origin of such microwave variations remains unclear but is likely related to terrain roughness and fine ejecta materials which produce higher signal loss (“loss tangent”) at the uppermost surface layer. The observed discrepancy from the other datasets is due to the higher penetrative potential of microwave radiation, thus potentially carrying additional information on buried materials. The microwave‐derived FOIs appear to be transient within a geological timeframe and, in principle, a relationship between temperature differences at night in the two microwave channels, along with infrared data analysis, could be employed to derive a formation time of the crater. However, the low spatial resolution of the available microwave data precludes a reliable quantitative derivation at present.

Coupling Complementary Strategy to U-Net Based Convolution Neural Network for Detecting Lunar Impact Craters

Lunar crater detection plays an important role in lunar exploration, while machine learning (ML) exhibits promising advantages in the field. However, previous ML works almost all used a single type of lunar map, such as an elevation map (DEM) or orthographic projection map (WAC), to extract crater features; the two types of images have individual limitations on reflecting the crater features, which lead to insufficient feature information, in turn influencing the detection performance. To address this limitation, we, in this work, propose feature complementary of the two types of images and accordingly explore an advanced dual-path convolutional neural network (Dual-Path) based on a U-NET structure to effectively conduct feature integration. Dual-Path consists of a contracting path, bridging path, and expanding path. The contracting path separately extracts features from DEM and WAC images by means of two independent input branches, while the bridging layer integrates the two types of features by 1 × 1 convolution. Finally, the expanding path, coupled with the attention mechanism, further learns and optimizes the feature information. In addition, a special deep convolution block with a residual module is introduced to avoid network degradation and gradient disappearance. The ablation experiment and the comparison of four competitive models only using DEM features confirm that the feature complementary can effectively improve the detection performance and speed. Our model is further verified by different regions of the whole moon, exhibiting high robustness and potential in practical applications.

Centimeter-resolution topographic modeling and fine-scale analysis of craters and rocks at the Chang'E-4 landing site

Chang'E-4 was the first lunar mission to successfully land on the far side of the Moon. The stereo images collected by the panoramic camera (Pancam) mounted on the Jade Rabbit-2 rover have provided the most detailed in situ observations of the landing site along the rover traverse. This paper presents our efforts in centimeter-resolution topographic modeling and fine-scale analysis of craters and rocks at the Chang'E-4 landing site using the Pancam images. We first used an integrated photogrammetric and photoclinometric approach to generate a digital elevation model (DEM) and an orthoimage mosaic of the landing site and a 3D model of the Chang'E-4 lander itself, all with spatial resolutions of 1 cm/pixel. Craters with diameters of decimeters to meters and rocks with sizes of decimeters were then extracted and analyzed. The results indicate that Chang'E-4 landed on a terrain slope of 4° toward 72.3° southwest, with one foot sunk in the regolith 1.5 cm deeper than the other foot on the front side of the lander. Fine-scale analysis of the small craters at the Chang'E-4 landing site showed overall a similar power-law trend as large craters of hundreds of meters, but there is a clear gap between their size-frequency distributions, indicating the accumulated impact flux appears to be different for these two crater populations. The average depth-to-diameter ratio of the small craters around the landing site was 0.061, obviously less than those of craters on the kilometer scale. Fine-scale analysis of rocks revealed that the Chang'E-4 landing site has a rock abundance of 0.21%, which agrees well with previous results based on orbital data. Joint analysis of craters and rocks indicates that more rocks are located inside craters with greater depth-to-diameter ratios on such a fine scale. The 3D topographic modeling approach and fine-scale analysis of craters and rocks presented in this paper provide new insights into lunar mapping and geological study.

Observational Strategy for KPLO/PolCam Measurements of the Lunar Surface from Orbit

PolCam, a wide-angle polarimetric camera, will be onboard the Korea Pathfinder Lunar Orbiter to investigate the lunar surface based on polarimetry. PolCam will construct a global lunar map of polarimetric parameters, such as maximal polarization (Pmax) and titanium distribution using three color bands centered at 320, 430, and 750 nm. PolCam's twin cameras are mounted at 45° tilt angles from the nadir across the orbital track in opposite directions. PolCam will obtain polarimetric measurements of sunlight scattered by the lunar surface at various phase angles up to ∼140° to achieve the scientific goals during the one-year mission. Since degree of linear polarization is a function of phase angle, it is essential to perform several measurements at various phase angles to properly retrieve, e.g., Pmax, Pmin, and the inversion angle, αinv. In this work, we show PolCam's phase-angle coverage as a function of selenographic longitudes from the equator to latitudes up to 70°N. Decreasing tilt angle to 25° and 0° limits the high end of phase-angle coverage, as well as the observational chances at high latitudes.

Maps of the Moon

Abstract When does a depiction of the moon become a lunar map? This essay addresses this question from theoretical and historical standpoints. It is argued that moon maps are of crucial importance to the history of cartography, for they challenge established notions of what is a map, how it functions, what are its purposes, and what kind of power it embodies and performs. The work also shows how terrestrial cartography has shaped the history of lunar mapping since the seventeenth century, through visual and nomenclature conventions, the cultural currency of maps, mapmakers’ social standing, and data-gathering and projection practices. It further demonstrates that lunar cartography has also been organized by an internal principle that is born of the fundamental problem of how to create static map spaces capable of representing a referent that is constantly changing to our eyes, as is the visible face of the moon. It is suggested that moon maps may be classed on three broad categories, according to the kinds of solutions for this representational problem that have been devised over the last 400 years.

Descent trajectory reconstruction and landing site positioning of Chang\u2019E-4 on the lunar farside

Chang’E-4 (CE-4) was the first mission to accomplish the goal of a successful soft landing on the lunar farside. The landing trajectory and the location of the landing site can be effectively reconstructed and determined using series of images obtained during descent when there were no Earth-based radio tracking and the telemetry data. Here we reconstructed the powered descent trajectory of CE-4 using photogrammetrically processed images of the CE-4 landing camera, navigation camera, and terrain data of Chang’E-2. We confirmed that the precise location of the landing site is 177.5991°E, 45.4446°S with an elevation of −5935 m. The landing location was accurately identified with lunar imagery and terrain data with spatial resolutions of 7 m/p, 5 m/p, 1 m/p, 10 cm/p and 5 cm/p. These results will provide geodetic data for the study of lunar control points, high-precision lunar mapping, and subsequent lunar exploration, such as by the Yutu-2 rover.

Lunar mineral mapping in Sinus Iridum in consideration of mineral grain sizes

Lunar mineral mapping in Sinus Iridum in consideration of mineral grain sizes

Automatic stitching method for Chang’E-2 CCD images of the Moon

The lunar map is a product of primary scientific objectives of lunar exploration. Aiming at the characteristics of the Chang’E-2 CCD data, an automatic stitching method used for 2C level CCD data from Chang’E-2 lunar mission is proposed. Combining with the image registration technique and the characteristics of Chang’E CCD images, the fast method proposed not only can overcome the contradiction of the high spatial resolution of the CCD images and the low positioning accuracy of the location coordinates, but also can speed up the processing and minimize the utilization of human resources to produce lunar mosaic map. Meanwhile, a new lunar map from 70oN to 70oS with spatial resolution of less than 10 m has been completed by the proposed method. Its average relative location accuracy of the adjacent orbits CCD image data is less than 3 pixels.

THE ‘MOON MAPPING’ PROJECT TO PROMOTE COOPERATION BETWEEN STUDENTS OF ITALY AND CHINA

The research project ‘Moon Mapping’ has been established in 2014 between the Italian and Chinese Governments to promote cooperation and exchange between undergraduate students from both countries. The operational phase of the project started in early 2015, and will end in 2017, for a total length of three years. The main aim is to train new scholars to be able to work on different kinds of remotely-sensed data collected over the Moon surface by the Chinese space missions Chang’E-1/2. The project coordination has been assigned to the Italian Space Agency for the Italian side and to the Center of Space Exploration, China Ministry of Education, for the Chinese side. Several Chinese universities and Italian national research institutes and universities have been officially involved in this project. Six main research topics have been identified: (1) map of the solar wind ion; (2) geomorphological map of the Moon; (3) data preprocessing of Chang’E-1 mission; (4) map of element distribution; (5) establishment of 3D digital visualization system; and (6) compilation and publication of a tutorial on joint lunar mapping.

THE ‘MOON MAPPING’ PROJECT TO PROMOTE COOPERATION BETWEEN STUDENTS OF ITALY AND CHINA

Abstract. The research project ‘Moon Mapping’ has been established in 2014 between the Italian and Chinese Governments to promote cooperation and exchange between undergraduate students from both countries. The operational phase of the project started in early 2015, and will end in 2017, for a total length of three years. The main aim is to train new scholars to be able to work on different kinds of remotely-sensed data collected over the Moon surface by the Chinese space missions Chang’E-1/2. The project coordination has been assigned to the Italian Space Agency for the Italian side and to the Center of Space Exploration, China Ministry of Education, for the Chinese side. Several Chinese universities and Italian national research institutes and universities have been officially involved in this project. Six main research topics have been identified: (1) map of the solar wind ion; (2) geomorphological map of the Moon; (3) data preprocessing of Chang’E-1 mission; (4) map of element distribution; (5) establishment of 3D digital visualization system; and (6) compilation and publication of a tutorial on joint lunar mapping.

Review and prospect of lunar mapping using remote sensing data

Review and prospect of lunar mapping using remote sensing data

Setting Diverging Colors for a Large-Scale Hypsometric Lunar Map Based on Entropy

A hypsometric map is a type of map used to represent topographic characteristics by filling different map areas with diverging colors. The setting of appropriate diverging colors is essential for the map to reveal topographic details. When lunar real environmental exploration programs are performed, large-scale hypsometric maps with a high resolution and greater topographic detail are helpful. Compared to the situation on Earth, fewer lunar exploration objects are available, and the topographic waviness is smaller at a large scale, indicating that presenting the topographic details using traditional hypsometric map-making methods may be difficult. To solve this problem, we employed the Chang’E2 (CE2) topographic and imagery data with a resolution of 7 m and developed a new hypsometric map-making method by setting the diverging colors based on information entropy. The resulting map showed that this method is suitable for presenting the topographic details and might be useful for developing a better understanding of the environment of the lunar surface.

Search for a high‐altitude lunar dust exosphere using Clementine navigational star tracker measurements

AbstractDuring the 1994 Clementine lunar mapping mission, portions of 25 orbits were dedicated to a search for lunar horizon glow (LHG) using the spacecraft star tracker navigation cameras. Previous putative detections of LHG were believed to result from forward scattering of sunlight by exospheric dust grains with radii ≈ 0.1 µm, observable above the limb from within the shadow of the Moon near orbital sunrise or sunset. We have examined star tracker image sequences from five Clementine orbits in which the limb occulted the Sun, and was at least partially shadowed from earthshine, minimizing the chance of stray light contamination. No LHG appears in the image data, or in any of the net brightness images, after subtraction of a reference zodiacal light model. However, some of the images display faint excess limb brightness that appears to be solar streamer structure. Therefore, we derive upper limits for the amount of dust in the lunar exosphere that could be hidden by these brightness fluctuations using a dust‐scattering simulation code and simple exponential dust profiles defined by surface concentration n0 and scale height H. Simulations using grains of radius 0.1 µm show that fluctuations in the observed excess brightness can be matched by a dust exosphere with a vertical column abundance n0H of 5–30 cm−2 and overlying mass &lt;10−12 g cm−2. These dust upper limit estimates are highly dependent on assumed grain size due to the rapid increase in per‐grain brightness with grain radius.

Construction of a virtual lunar environment platform

Many of the world's powerful and wealthy nations, including China, have devoted both large amounts of funding and considerable promotion to lunar research and exploration. The launch of Chinese Chang'e-1 satellite and the construction of the scientific observation data platform created a favourable opportunity for research into the lunar geometrical, physical and chemical environment. Based on this background, a Wide Area Network (WAN) based virtual lunar environment was constructed for observation data sharing and further exploration. The systematic architecture and framework were introduced and then strategies of mass data (e.g. lunar digital elevation model, lunar digital orthophoto map and typical thematic lunar data) organisation, integration, management and scheduling were then set up to achieve the 3D visualisation of typical lunar geomorphic features. Furthermore, the integration method of 3D lunar data and the process model of impact craters were studied; thus, the whole lunar and celestial collision process could be dynamically simulated. The results indicate that the WAN-based virtual lunar platform can be used effectively for public information sharing, scientific exploration and further to promote the development of deep space exploration in China.

Interactive visualization of 3D lunar model with texture and labels, using Chang’E-1 data

A lunar model with real texture can be obtained by mapping texture onto the lunar mesh, but the convergence in the polar regions of lunar model is a problem. In this paper, we build a 3D lunar model and solve this problem by texture partitioning and transforming. The whole lunar map is divided into four images and the polar images are transformed to circular textures before mapped to the semi-regular (SR) lunar mesh which is obtained through denoising, triangulating, subdividing and resampling the laser altimetry (LAM) data. Hundreds of lunar labels are classed into three levels and added gradually to the lunar model considering the distance between the viewpoint and the moon center. Through some techniques such as mip-map and view-dependent, the lunar model with textures and labels can be interactively browsed on a personal computer (PC) in real time.

An algorithm for geometric correction of full pass TMC imagery of Chandrayaan-1

This paper describes the development of an algorithm for geometric correction of Terrain Mapping Camera (TMC) imagery of Chandrayaan-1 (CH-1). The correction is based on a rigorous sensor model. The algorithm incorporates the camera geometry model, satellite data and lunar control points in a rigorous bundle adjustment updating the satellite model parameters. The model is tested for different strips over the polar and equatorial regions and the results are presented. The planimetric control is identified from the 100m/pixel USGS Clementine base map mosaic and vertical control is derived from Lunar Orbiter Laser Altimeter (LOLA) data. RMS error of the order 200–300m in latitude, longitude and height with respect to the references could be achieved using the sensor model with few distributed controls over the strip. The model is used for the orientation of long strips with little or no degradation in orientation quality attainable with a short scene. The results are representative of the stability of the platform and potential of CH-1 for accurate lunar mapping. The algorithm for geometric correction described in this paper is a part of Lunar Mapping System (LMS), which will handle full-pass data for operational generation of Digital Elevation Models (DEM) and Ortho products from TMC images of CH-1.