Browsing by Subject "Mars"

Now showing 1 - 5 of 5
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    Challenges in the Search for Perchlorate and Other Hydrated Minerals With 2.1-μm Absorptions on Mars
    (American Geophysical Union, 2018) Leask, E. K.; Ehlmann, B. L.; Dundar, M. M.; Murchie, S. L.; Seelos, F. P.; Computer and Information sciences, School of Science
    A previously unidentified artifact has been found in Compact Reconnaissance Imaging Spectrometer for Mars targeted I/F data. It exists in a small fraction (<0.05%) of pixels within 90% of images investigated and occurs in regions of high spectral/spatial variance. This artifact mimics real mineral absorptions in width and depth and occurs most often at 1.9 and 2.1 μm, thus interfering in the search for some mineral phases, including alunite, kieserite, serpentine, and perchlorate. A filtering step in the data processing pipeline, between radiance and I/F versions of the data, convolves narrow artifacts (“spikes”) with real atmospheric absorptions in these wavelength regions to create spurious absorption-like features. The majority of previous orbital detections of alunite, kieserite, and serpentine we investigated can be confirmed using radiance and raw data, but few to none of the perchlorate detections reported in published literature remain robust over the 1.0- to 2.65-μm wavelength range.
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    Evidence for Chemically Distinct Waters Forming Sulphates and Chlorides in Terra Sirenum, Mars
    (USRA, 2019) Leask, E. K.; Ehlmann, B. L.; Dundar, M. M.; Computer and Information Science, School of Science
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    A machine learning toolkit for CRISM image analysis
    (Elsevier, 2022-04) Plebani, Emanuele; Ehlmann, Bethany L.; Leask, Ellen K.; Fox, Valerie K.; Dundar, M. Murat; Computer and Information Science, School of Science
    Hyperspectral images collected by remote sensing have played a significant role in the discovery of aqueous alteration minerals, which in turn have important implications for our understanding of the changing habitability on Mars. Traditional spectral analyzes based on summary parameters have been helpful in converting hyperspectral cubes into readily visualizable three channel maps highlighting high-level mineral composition of the Martian terrain. These maps have been used as a starting point in the search for specific mineral phases in images. Although the amount of labor needed to verify the presence of a mineral phase in an image is quite limited for phases that emerge with high abundance, manual processing becomes laborious when the task involves determining the spatial extent of detected phases or identifying small outcrops of secondary phases that appear in only a few pixels within an image. Thanks to extensive use of remote sensing data and rover expeditions, significant domain knowledge has accumulated over the years about mineral composition of several regions of interest on Mars, which allow us to collect reliable labeled data required to train machine learning algorithms. In this study we demonstrate the utility of machine learning in two essential tasks for hyperspectral data analysis: nonlinear noise removal and mineral classification. We develop a simple yet effective hierarchical Bayesian model for estimating distributions of spectral patterns and extensively validate this model for mineral classification on several test images. Our results demonstrate that machine learning can be highly effective in exposing tiny outcrops of specific phases in orbital data that are not uncovered by traditional spectral analysis. We package implemented scripts, documentation illustrating use cases, and pixel-scale training data collected from dozens of well-characterized images into a new toolkit. We hope that this new toolkit will provide advanced and effective processing tools and improve community’s ability to map compositional units in remote sensing data quickly, accurately, and at scale.
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    Mini Mars Rover
    (2020-12-11) Alanzi, Nafa; Tan, Chad; Walden, Dae’Shaun; Freije, Elizabeth; McNeely, Andrew
    The customer, Andrew McNeely, would like us to construct and design a miniature-sized version of the existing Mars Rover robot. The robot will be controlled through an Android Application that we have designed, that will control the motor movements and command the robot to collect five data points from the environment. What we are given that is out of our scope for operation is an existing robotics kit that we will grab components from, the battery, frame, wheels, and motors. Our In-Scope of operation is to design a buck converter, power supply, and a transistor circuit that will transfer a low voltage output from a higher voltage input, a battery. We also designed a board layout for the motor control and designed a code for the Android Application and the microcontroller.
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    Partially-observed models for classifying minerals on Mars
    (IEEE, 2013) Dundar, Murat; Li, Lin; Rajwa, Bartek; Earth Sciences, School of Science
    The identification of phyllosilicates by NASA's CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) strongly suggests the presence of water-related geological processes. A variety of water-bearing phyllosilicate minerals have already been identified by several research groups utilizing spectral enrichment techniques and matching phyllosilicate-rich regions on the Martian surface to known spectra of minerals found on earth. However, fully automated analysis of the CRISM data remains a challenge for two main reasons. First, there is significant variability in the spectral signature of the same mineral obtained from different regions on the Martian surface. Second, the list of mineral confirmed to date constituting the set of training classes is not exhaustive. Thus, when classifying new regions, using a classifier trained with selected minerals and chemicals, one must consider the potential presence of unknown materials not represented in the training library. We made an initial attempt to study these problems in the context of our recent work on partially-observed classification models and present results that show the utility of such models in identifying spectra of unknown minerals while simultaneously recognizing spectra of known minerals.