Visible and Near Infrared imaging spectroscopy and the exploration of small scale hydrothermally altered and hydrated environments on Earth and Mars

The use of Visible and Near Infrared (VNIR) imaging spectroscopy is a cornerstone of planetary exploration. This work shall present an investigation into the limitations of scale, both spectral and spatial, in the utility of VNIR images for identifying small scale hydrothermal and potential hydrated environments on Mars, and regions of the Earth that can serve as martian analogues. Such settings represent possible habitable environments; important locations for astrobiological research. The ESA/Roscosmos ExoMars rover PanCam captures spectrally coarse but spatially high resolution VNIR images. This instrument is still in development and the first field trial of an emulator fitted with the final set of geological filters is presented here. Efficient image analysis techniques are explored and the ability to accurately characterise a hydrothermally altered region using PanCam data products is established. The CRISM orbital instrument has been returning hyperspectral VNIR images with an 18 m2 pixel resolution since 2006. The extraction of sub-pixel information from CRISM pixels using Spectral Mixture Analysis (SMA) algorithms is explored. Using synthetic datasets a full SMA pipeline consisting of publically available Matlab algorithms and optimised for investigation of mineralogically complex hydrothermal suites is developed for the first time. This is validated using data from Námafjall in Iceland, the region used to field trial the PanCam prototype. The pipeline is applied to CRISM images covering four regions on Mars identified as having potentially undergone hydrothermal alteration in their past. A second novel use of SMA to extract a unique spectral signature for the potentially hydrated Recurring Slope Lineae features on Mars is presented. The specific methodology presented shows promise and future improvements are suggested. The importance of combining different scales of data and recognising their limitations is discussed based on the results presented and ways in which to take the results presented in this thesis forward are given

Hyperspectral benthic mapping from underwater robotic platforms

We live on a planet of vast oceans; 70% of the Earth's surface is covered in water. They are integral to supporting life, providing 99% of the inhabitable space on Earth. Our oceans and the habitats within them are under threat due to a variety of factors. To understand the impacts and possible solutions, the monitoring of marine habitats is critically important. Optical imaging as a method for monitoring can provide a vast array of information however imaging through water is complex. To compensate for the selective attenuation of light in water, this thesis presents a novel light propagation model and illustrates how it can improve optical imaging performance. An in-situ hyperspectral system is designed which comprised of two upward looking spectrometers at different positions in the water column. The downwelling light in the water column is continuously sampled by the system which allows for the generation of a dynamic water model. In addition to the two upward looking spectrometers the in-situ system contains an imaging module which can be used for imaging of the seafloor. It consists of a hyperspectral sensor and a trichromatic stereo camera. New calibration methods are presented for the spatial and spectral co-registration of the two optical sensors. The water model is used to create image data which is invariant to the changing optical properties of the water and changing environmental conditions. In this thesis the in-situ optical system is mounted onboard an Autonomous Underwater Vehicle. Data from the imaging module is also used to classify seafloor materials. The classified seafloor patches are integrated into a high resolution 3D benthic map of the surveyed site. Given the limited imaging resolution of the hyperspectral sensor used in this work, a new method is also presented that uses information from the co-registered colour images to inform a new spectral unmixing method to resolve subpixel materials

Trying to break new ground in aerial archaeology

Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth’s atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research: the bias we impart to the visual record as a result of selective coverage. While the total coverage approach outlined here may not altogether eliminate survey bias, it can vastly increase the amount of useful information captured during a single reconnaissance flight while mitigating the discriminating effects of observer-based, on-the-fly target selection. Furthermore, the information contained in this paper should make it clear that with current technology it is feasible to do so. This can radically alter the basis for aerial prospection and move landscape archaeology forward, beyond the inherently biased patterns that are currently created by airborne archaeological prospection

Exploring the use of image processing to survey and quantitatively assess historic buildings

Before architectural conservation takes place, a survey is conducted to assess the condition of the building and estimate the cost of the work. For façades, scaffolding is erected so that experts can access the building’s whole extent and gather data for analysis. This paper presents the results of a collaborative and cross-disciplinary research project aiming to automate data capture and analysis techniques for conservation of stone façades. Our research demonstrates the feasibility of a new methodology for the survey and assessment of historic buildings and will facilitate frequent surveys with minimal disruption to the general public in cities. The project has embedded architects’ expert knowledge into intelligent algorithms for automatically analysing images of facades. The combination of technologies allows for an efficient data capture while minimising the requirement for manual data analysis as well as more accurate estimates of its cost

SIMULATIONS-GUIDED DESIGN OF PROCESS ANALYTICAL SENSOR USING MOLECULAR FACTOR COMPUTING

Many areas of science now generate huge volumes of data that present visualization, modeling, and interpretation challenges. Methods for effectively representing the original data in a reduced coordinate space are therefore receiving much attention. The purpose of this research is to test the hypothesis that molecular computing of vectors for transformation matrices enables spectra to be represented in any arbitrary coordinate system. New coordinate systems are selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a spectrometer. A novel integrated sensing and processing system, termed Molecular Factor Computing (MFC) based near infrared (NIR) spectrometer, is proposed in this dissertation. In an MFC -based NIR spectrometer, spectral features are encoded by the transmission spectrum of MFC filters which effectively compute the calibration function or the discriminant functions by weighing the signals received from a broad wavelength band. Compared with the conventional spectrometers, the novel NIR analyzer proposed in this work is orders of magnitude faster and more rugged than traditional spectroscopy instruments without sacrificing the accuracy that makes it an ideal analytical tool for process analysis. Two different MFC filter-generating algorithms are developed and tested for searching a near-infrared spectral library to select molecular filters for MFC-based spectroscopy. One using genetic algorithms coupled with predictive modeling methods to select MFC filters from a spectral library for quantitative prediction is firstly described. The second filter-generating algorithm designed to select MFC filters for qualitative classification purpose is then presented. The concept of molecular factor computing (MFC)-based predictive spectroscopy is demonstrated with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument

The Secret Lives of Battery Materials: Core-Level Spectroscopy as a Probe of Compositional and Electronic Structure Inhomogeneities

The invention of rechargeable batteries has dramatically changed our landscapes and lives, underpinning the explosive worldwide growth of consumer electronics, ushering in an unprecedented era of electric vehicles, and potentially paving the way for a much greener energy future. Unfortunately, current battery technologies suffer from a number of challenges, e.g., capacity loss and failure upon prolonged cycling, limited ion diffusion kinetics, and a rather sparse palette of high-performing electrode materials. This dissertation will focus on elucidation of the influence of electronic structure on intercalation phenomena. Mechanistic understanding of compositional and electronic structure heterogeneities spanning from atomistic to mesoscale dimensions is imperative to facilitate the rational design of novel electrode chemistries and architectures. First, this dissertation provides an introduction to the fundamental science challenges involved in electrode design utilizing Vv2Ov5 as a model system to review means of defining ionic and electronic conduction pathways. Subsequently, the oxidative chemistry of graphite, a canonical anode material, is evaluated with the purpose of understanding the spatial localization and connectivity of functional groups in graphene oxide, which is of utmost relevance to the design of high-performing electrode composites. Furthermore, scanning transmission X-ray microscopy (STXM) observations indicate the formation of lithiation gradients in individual nanowires of layered orthorhombic Vv2Ov5 that arise from electron localization and local structural distortions. Electrons localized in the Vv2Ov5 framework couple to a local structural distortion, giving rise to small polarons, which are observed to be trap Li-ions and are found to represent a major impediment to Li-ion diffusion. In addition, this dissertation presents the first direct visualization of patterns of compositional inhomogeneities within cathode materials during electrochemical discharge. Two distinct patterns are evidenced: core—shell separation and striping modulations of Li-rich and Li-poor domains within individual particles. 3D compositional maps have been developed and translated to stress and strain maps, providing a hitherto unprecedented direct visualization of stress and strain inhomogeneities. Finally, a cluster of interlaced LivxVv2Ov5 nanoparticles is evaluated by scanning transmission X-ray microscopy. Increased heterogeneity at the interface between particles suggests the exchange of Li-ions, implying a “winner-takes-all” behavior (corresponding to particle-by-particle lithiation of an ensemble of particles). Such behavior portends the creation of localized hot-spots and provides insight into a possible origin of failure of Li-ion batteries