Materials and Surface Processes at Gale Crater and the Moons of Mars Derived from High Spatial and Spectral Resolution Orbital Datasets

This thesis is a collection of studies that use orbital remote sensing data to investigate the composition and geologic histories of Mars\u27 moons, Phobos and Deimos, and Mt. Sharp, the destination for the Curiosity Mars rover. A final chapter focuses on Curiosity data and terrestrial analog studies to supplement orbital predictions about Mt. Sharp. Disk-resolved hyperspectral observations of Phobos acquired at a range of lighting and viewing geometries are fit with a Hapke photometric function to solve for the single particle phase function and single scattering albedos of Phobos and also disk-resolved hyperspectral observations of Deimos. Fe2+ electronic absorptions diagnostic of olivine and pyroxene are not detected. A broad absorption centered on 0.65 µm within the red spectral units of both moons is seen, and this feature is also evident in telescopic, Pathfinder, and Phobos-2 observations of Phobos. A 2.8 μm metal-OH combination absorption on both moons is also detected, and this absorption is shallower in the Phobos blue unit than in the Phobos red unit and Deimos. The strength, position, and shape of both absorptions are similar to features seen on low-albedo primitive asteroids. Two end-member hypotheses could explain these spectral features: the presence of highly desiccated Fe-phyllosilicate minerals indigenous to the bodies, or Rayleigh scattering and absorption of small iron particles formed by exogenic space weathering processing, coupled with implantation of H from solar wind. Phobos\u27 and Deimos\u27 low reflectances, lack of mafic absorption features, and red spectral slopes are incompatible with even highly space weathered chondritic or basaltic compositions. These results, coupled with similarities to laboratory spectra of Tagish Lake (possible D-type asteroid analog) and CM carbonaceous chondrite meteorites, show that Phobos and Deimos have primitive compositions. If the moons formed in situ rather than by capture of primitive bodies, primitive materials must have been added to the Martian system during accretion or a late stage impact. Oversampled visible/near-infrared hyperspectral data over Mt. Sharp in Gale Crater are used to generate spatially sharpened maps of the location of red crystalline hematite within the uppermost stratum of a ~6.5 km long ridge on the mound\u27s northern flank. Emplacement of the hematite is hypothesized to result either from exposure of anoxic Fe+2-rich groundwater to an oxidizing environment or from in place weathering of precursor silicate materials under oxidizing conditions. Although at the time of writing the rover is still ~6 km north of the ridge, high resolution color imaging and low resolution spectral remote sensing data of the ridge collected by Curiosity are consistent with orbital observations. When Curiosity does arrive at the ridge, it is well equipped to distinguish between predicted end-member textural scenarios for ridge materials, which will be essential to understand its formation and evolution

Simulation and Measurement of Multispectral Space Debris Light Curves

The accumulation of space debris has become one of the greatest threats facing the space industry to date. Through an increasing amount of objects deposited in Earth's orbit, such as rocket bodies, defunct satellites and general debris fragments, space missions are exposed to a growing risk of collisions. Moreover, the recent surge in commercial space applications is expected to further contribute to the problem. At the Institute of Technical Physics of Deutsches Zentrum für Luft- und Raumfahrt (DLR) in Stuttgart, resident space objects are monitored using a number of telescopes through active laser and passive sunlight illumination. Due to the high altitude and relatively small size of the objects they generally appear as unresolved points in photometric images. An object's temporal variation in brightness is referred to as a light curve and implies key information concerning the object's shape, material composition and rotation. Recovering these parameters from light signals is not trivial and it is anticipated that additional information provided by multispectral observations will contribute to a more reliable characterization of space debris. This research covers the development of a physically based simulation to model multispectral light reflections from space debris. The software is targeted towards ground-based observations and is expected to form an integral part in facilitating future strategies for comprehensive collision avoidance and space debris removal. Both passive light curves and laser ranging measurements are simulated using three-dimensional satellite models. To improve the accuracy of simulations, spectral lab measurements of common space materials are incorporated into the render. Further, the process of gathering reference measurements using the DLR's 43 cm telescope at the Uhlandshöhe Forschungsobservatorium is presented. For the comparison between synthetic and empirical light curves, a detailed calibration of the optical system is performed. The validity of the light curve simulator is confirmed the on the basis of recordings obtained from radar calibration targets. Finally, simulated data is used to study benefits of multispectral observations for characterization and parameter estimation from space debris

Snapshot hyperspectral imaging : near-infrared image replicating imaging spectrometer and achromatisation of Wollaston prisms

Conventional hyperspectral imaging (HSI) techniques are time-sequential and rely on temporal scanning to capture hyperspectral images. This temporal constraint can limit the application of HSI to static scenes and platforms, where transient and dynamic events are not expected during data capture. The Near-Infrared Image Replicating Imaging Spectrometer (N-IRIS) sensor described in this thesis enables snapshot HSI in the short-wave infrared (SWIR), without the requirement for scanning and operates without rejection in polarised light. It operates in eight wavebands from 1.1μm to 1.7μm with a 2.0° diagonal field-of-view. N-IRIS produces spectral images directly, without the need for prior topographic or image reconstruction. Additional benefits include compactness, robustness, static operation, lower processing overheads, higher signal-to-noise ratio and higher optical throughput with respect to other HSI snapshot sensors generally. This thesis covers the IRIS design process from theoretical concepts to quantitative modelling, culminating in the N-IRIS prototype designed for SWIR imaging. This effort formed the logical step in advancing from peer efforts, which focussed upon the visible wavelengths. After acceptance testing to verify optical parameters, empirical laboratory trials were carried out. This testing focussed on discriminating between common materials within a controlled environment as proof-of-concept. Significance tests were used to provide an initial test of N-IRIS capability in distinguishing materials with respect to using a conventional SWIR broadband sensor. Motivated by the design and assembly of a cost-effective visible IRIS, an innovative solution was developed for the problem of chromatic variation in the splitting angle (CVSA) of Wollaston prisms. CVSA introduces spectral blurring of images. Analytical theory is presented and is illustrated with an example N-IRIS application where a sixfold reduction in dispersion is achieved for wavelengths in the region 400nm to 1.7μm, although the principle is applicable from ultraviolet to thermal-IR wavelengths. Experimental proof of concept is demonstrated and the spectral smearing of an achromatised N-IRIS is shown to be reduced by an order of magnitude. These achromatised prisms can provide benefits to areas beyond hyperspectral imaging, such as microscopy, laser pulse control and spectrometry

Towards Accurate and Automated Detection and Quantification of Localized Methane Point Sources on a Global Scale

Methane (CH4) is the second most important anthropogenic greenhouse gas with a significant impact on radiative forcing, tropospheric air quality, and stratospheric water vapor. Because methane has a much shorter lifetime compared to carbon dioxide (CO2), reduction in methane emission is deemed a key target for climate mitigation strategies in upcoming decades. One crucial step in emission reduction is determining the location and emission rate of localized methane sources. Remote-sensing instruments using absorption spectroscopy have emerged as one promising solution for measuring atmospheric CH4 concentration over large geographical areas. However, the identification and quantification of local point sources based on the observed methane column enhancement distribution has proven challenging due to uncertainties in the knowledge of local wind speed and retrieval errors arising from surface spectral interferences and instrument noise. In this thesis, it is shown how plume morphology based on a 2-D image of methane column enhancement can be used to quantify the source emission rate directly without relying on any ancillary data such as local wind speed measurements. Large eddies simulations (LES) are utilized to create realistic synthetic plume observations under various atmospheric conditions. Using this data, a deep learning model named MethaNet is trained to predict emission rates directly from 2-D methane plume images. The model achieves a level of performance for quantifying methane emission rates that is state-of-the-art for a method that does not rely on wind speed information. Obtaining methane column measurements with low precision error and bias is a key step for separating real plume enhancements from artefacts and enhancing the quantification performance. Here an instrument tradeoff analysis is presented to assess the effect of changing instrument specifications and retrieval parameters. It is shown how the retrieval errors can be mitigated with optimal spectral resolutions and a larger polynomial degree to approximate surface albedo variations in the retrieval process. The results in this thesis contribute towards building an enhanced monitoring system that can measure CH4 enhancement fields and determine methane sources accurately and efficiently at scale.</p

Systems evaluation for computer graphics rendering of the total appearance of paintings

One of the challenges when imaging paintings is recording total appearance, that is, the object\u27s color, surface microstructure (gloss), and surface macrostructure (topography). In this thesis, various systems were used to achieve this task, and a psychophysical paired comparison experiment was conducted to evaluate their performance. A pair of strobe lights arranged at 60° from the normal on either side of the painting captured color information where the strobes produced either directional or diffuse illumination geometry. By adding a third strobe, arranging them 120° apart annularly, and cross polarizing, diffuse color and surface normal maps were measured. A fourth strobe was added and the four lights were rearranged 90° apart annularly, capturing similar data. This system was augmented by two scanning linear light sources arranged perpendicularly, facilitating the measurement of spatially varying BRDF and specular maps. A laser scanner was used to capture surface macrostructure and was combined with the diffuse color maps from the four-light configuration. Finally, a dome illumination system was used with software developed by Conservation Heritage Imaging to produce color maps. In all, eight different configurations were achieved and used to image three small paintings with a range of appearance attributes. Twenty-five naive observers compared computer-graphic renderings to the actual painting and judged similarity in terms of total appearance, gloss/shininess, texture, and color. Although the rankings varied with painting, two general trends emerged. First, the four-light configuration with or without the independent laser scanning produced images visually equivalent to conventional strobe illumination. Second, diffuse illumination was always ranked lowest

3D Reconstruction of Small Solar System Bodies using Rendered and Compressed Images

Synthetic image generation and reconstruction of Small Solar System Bodies and the influence of compression is becoming an important study topic because of the advent of small spacecraft in deep space missions. Most of these missions are fly-by scenarios, for example in the Comet Interceptor mission. Due to limited data budgets of small satellite missions, maximising scientific return requires investigating effects of lossy compression. A preliminary simulation pipeline had been developed that uses physics-based rendering in combination with procedural terrain generation to overcome limitations of currently used methods for image rendering like the Hapke model. The rendered Small Solar System Body images are combined with a star background and photometrically calibrated to represent realistic imagery. Subsequently, a Structure-from-Motion pipeline reconstructs three-dimensional models from the rendered images. In this work, the preliminary simulation pipeline was developed further into the Space Imaging Simulator for Proximity Operations software package and a compression package was added. The compression package was used to investigate effects of lossy compression on reconstructed models and the possible amount of data reduction of lossy compression to lossless compression. Several scenarios with varying fly-by distances ranging from 50 km to 400 km and body sizes of 1 km and 10 km were simulated and compressed with lossless and several quality levels of lossy compression using PNG and JPEG 2000 respectively. It was found that low compression ratios introduce artefacts resembling random noise while high compression ratios remove surface features. The random noise artefacts introduced by low compression ratios frequently increased the number of vertices and faces of the reconstructed three-dimensional model

BxDF material acquisition, representation, and rendering for VR and design

Photorealistic and physically-based rendering of real-world environments with high fidelity materials is important to a range of applications, including special effects, architectural modelling, cultural heritage, computer games, automotive design, and virtual reality (VR). Our perception of the world depends on lighting and surface material characteristics, which determine how the light is reflected, scattered, and absorbed. In order to reproduce appearance, we must therefore understand all the ways objects interact with light, and the acquisition and representation of materials has thus been an important part of computer graphics from early days. Nevertheless, no material model nor acquisition setup is without limitations in terms of the variety of materials represented, and different approaches vary widely in terms of compatibility and ease of use. In this course, we describe the state of the art in material appearance acquisition and modelling, ranging from mathematical BSDFs to data-driven capture and representation of anisotropic materials, and volumetric/thread models for patterned fabrics. We further address the problem of material appearance constancy across different rendering platforms. We present two case studies in architectural and interior design. The first study demonstrates Yulio, a new platform for the creation, delivery, and visualization of acquired material models and reverse engineered cloth models in immersive VR experiences. The second study shows an end-to-end process of capture and data-driven BSDF representation using the physically-based Radiance system for lighting simulation and rendering

TOWARDS CASUAL APPEARANCE CAPTURE BY REFLECTANCE SYMMETRY

Ph.DDOCTOR OF PHILOSOPH

Models of Visual Appearance for Analyzing and Editing Images and Videos

The visual appearance of an image is a complex function of factors such as scene geometry, material reflectances and textures, illumination, and the properties of the camera used to capture the image. Understanding how these factors interact to produce an image is a fundamental problem in computer vision and graphics. This dissertation examines two aspects of this problem: models of visual appearance that allow us to recover scene properties from images and videos, and tools that allow users to manipulate visual appearance in images and videos in intuitive ways. In particular, we look at these problems in three different applications. First, we propose techniques for compositing images that differ significantly in their appearance. Our framework transfers appearance between images by manipulating the different levels of a multi-scale decomposition of the image. This allows users to create realistic composites with minimal interaction in a number of different scenarios. We also discuss techniques for compositing and replacing facial performances in videos. Second, we look at the problem of creating high-quality still images from low-quality video clips. Traditional multi-image enhancement techniques accomplish this by inverting the camera’s imaging process. Our system incorporates feature weights into these image models to create results that have better resolution, noise, and blur characteristics, and summarize the activity in the video. Finally, we analyze variations in scene appearance caused by changes in lighting. We develop a model for outdoor scene appearance that allows us to recover radiometric and geometric infor- mation about the scene from images. We apply this model to a variety of visual tasks, including color-constancy, background subtraction, shadow detection, scene reconstruction, and camera geo-location. We also show that the appearance of a Lambertian scene can be modeled as a combi- nation of distinct three-dimensional illumination subspaces — a result that leads to novel bounds on scene appearance, and a robust uncalibrated photometric stereo method.Engineering and Applied Science