Recognition of landslides in lunar impact craters

Landslides have been observed on several planets and minor bodies of the solar System, including the Moon. Notwithstanding different types of slope failures have been studied on the Moon, a detailed lunar landslide inventory is still pending. Undoubtedly, such will be in a benefit for future geological and morphological studies, as well in hazard, risk and suscept- ibility assessments. A preliminary survey of lunar landslides in impact craters has been done using visual inspection on images and digital elevation model (DEM) (Brunetti et al. 2015) but this method suffers from subjective interpretation. A new methodology based on polynomial interpolation of crater cross-sections extracted from global lunar DEMs is presented in this paper. Because of their properties, Chebyshev polynomials were already exploited for para- metric classification of different crater morphologies (Mahanti et al., 2014). Here, their use has been extended to the discrimination of slumps in simple impact craters. Two criteria for recognition have provided the best results: one based on fixing an empirical absolute thresholding and a second based on statistical adaptive thresholding. The application of both criteria to a data set made up of 204 lunar craters’ cross-sections has demonstrated that the former criterion provides the best recognition

Mapping Terrestrial Impact Craters with the TanDEM-X Digital Elevation Model

The TanDEM-X mission generates a global digital elevation model (DEM) with unprecedented properties. We use it for mapping confirmed terrestrial impact craters as listed in the Earth Impact Database. Both for simple and complex craters detailed investigations of the morphology of the particular structure and of the surrounding terrain can be performed

Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods

Determining when an impact crater formed is a complex and tedious task. However, this knowledge is crucial to understanding the geological history of planetary bodies and, more specifically, gives information on erosion rate measurements, meteorite ejection location, impact flux evolution and the loss of a magnetic field. The derivation of an individual crater's age is currently performed through manual counting. Because crater size scales as a power law, this method is limited to small (and/or young) surface areas and, in the case of the derivation of crater emplacement age, to a small set of impact craters. Here, we used a Crater Detection Algorithm, specifically retrained to detect small impact craters on large‐ and high‐resolution imagery data set to solve this issue. We applied it to a global, 5 m/pixel resolution mosaic of Mars. Here, we test the use of this data set to date 10 large impact craters. We developed a cluster analysis tool in order to distinguish potential secondary crater clusters from the primary crater population. We then use this, filtered, crater population to date each large impact crater and evaluate our results against literature ages. We found that automated counting filtered through clustering analysis produced similar model ages to manual counts. This technique can now be expanded to much wider crater dating surveys, and by extension to any other kind of Martian surface. We anticipate that this new tool will considerably expand our knowledge of the geological events that have shaped the surface of Mars, their timing and duration

Automatic Feature Extraction from Planetary Images

With the launch of several planetary missions in the last decade, a large amount of planetary images has already been acquired and much more will be available for analysis in the coming years. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to planetary data that often present low contrast and uneven illumination characteristics. Different methods have already been presented for crater extraction from planetary images, but the detection of other types of planetary features has not been addressed yet. Here, we propose a new unsupervised method for the extraction of different features from the surface of the analyzed planet, based on the combination of several image processing techniques, including a watershed segmentation and the generalized Hough Transform. The method has many applications, among which image registration and can be applied to arbitrary planetary images

ExoMars Raman Laser Spectrometer RLS, a tool for the potential recognition of wet target craters on Mars

In the present work, NIR, LIBS, Raman and XRD techniques have been complementarily used to carry out a comprehensive characterization of a terrestrial analogue selected from the Chesapeake Bay Impact Structure (CBIS). The obtained data clearly highlight the key role of Raman spectroscopy in the detection of minor and trace compounds, through which inferences about geological processes occurred in the CBIS can be extrapolated. Beside the use of commercial systems, further Raman analyses were performed by the Raman Laser Spectrometer (RLS) ExoMars Simulator. This instrument represents the most reliable tool to effectively predict the scientific capabilities of the ExoMars/Raman system that will be deployed on Mars in 2021. By emulating the analytical procedures and operational restrictions established by the ExoMars mission rover design, it was proved that the RLS ExoMars Simulator is able to detect the amorphization of quartz, which constitutes an analytical clue of the impact origin of craters. On the other hand, the detection of barite and siderite, compounds crystallizing under hydrothermal conditions, helps to indirectly confirm the presence of water in impact targets. Furthermore, the RLS ExoMars Simulator capability of performing smart molecular mappings was also evaluated. According to the obtained results, the algorithms developed for its operation provide a great analytical advantage over most of the automatic analysis systems employed by commercial Raman instruments, encouraging its application for many additional scientific and commercial purposes

Unsupervised Detection of Planetary Craters by a Marked Point Process

With the launch of several planetary missions in the last decade, a large amount of planetary images is being acquired. Preferably, automatic and robust processing techniques need to be used for data analysis because of the huge amount of the acquired data. Here, the aim is to achieve a robust and general methodology for crater detection. A novel technique based on a marked point process is proposed. First, the contours in the image are extracted. The object boundaries are modeled as a configuration of an unknown number of random ellipses, i.e., the contour image is considered as a realization of a marked point process. Then, an energy function is defined, containing both an a priori energy and a likelihood term. The global minimum of this function is estimated by using reversible jump Monte-Carlo Markov chain dynamics and a simulated annealing scheme. The main idea behind marked point processes is to model objects within a stochastic framework: Marked point processes represent a very promising current approach in the stochastic image modeling and provide a powerful and methodologically rigorous framework to efficiently map and detect objects and structures in an image with an excellent robustness to noise. The proposed method for crater detection has several feasible applications. One such application area is image registration by matching the extracted features