A Compact, High Resolution Hyperspectral Imager for Remote Sensing of Soil Moisture

Measurement of soil moisture content is a key challenge across a variety of fields, ranging from civil engineering through to defence and agriculture. While dedicated satellite platforms like SMAP and SMOS provide high spatial coverage, their low spatial resolution limits their application to larger regional studies. The advent of compact, high lift capacity UAVs has enabled small scale surveys of specific farmland cites. This thesis presents work on the development of a compact, high spatial and spectral resolution hyperspectral imager, designed for remote measurement of soil moisture content. The optical design of the system incorporates a bespoke freeform blazed diffraction grating, providing higher optical performance at a similar aperture to conventional Offner-Chrisp designs. The key challenges of UAV-borne hyperspectral imaging relate to using only solar illumination, with both intermittent cloud cover and atmospheric water absorption creating challenges in obtaining accurate reflectance measurements. A hardware based calibration channel for mitigating cloud cover effects is introduced, along with a comparison of methods for recovering soil moisture content from reflectance data under varying illumination conditions. The data processing pipeline required to process the raw pushbroom data into georectified images is also discussed. Finally, preliminary work on applying soil moisture techniques to leaf imaging are presented

Characterization of Hyperspectral Imagers

Hyperspectral imaging (HSI) is a technology that captures three dimensions of information, two spatial dimensions and a spectral dimension. A focal plane, which is used to collect the intensity information of a scene, is only two-dimensional. In a conventional camera this is not a problem as it collects only two-dimensional images, but since hyperspectral imagers collect three-dimensional images this presents a problem. How do you collect these three-dimensional images? There have been clever schemes to use the focal plane to collect three-dimensional data as well as different capture methods which make either a spatial or spectral dimension the third dimension that is collected with subsequent scans. The next logical question is how to characterize and calibrate these systems. A radiance calibration is described and results in a low error calibration. For most systems, established methods will not work therefore, new metrics need to be developed and new merits created to fully measure and calibrate these systems. For line scan systems, a stationary measurement of a knife edge is not possible and movement is necessary for capturing the target. Two methods, stare step and continuous, were used and the results were similar to each other. In this paper, 3D noise methodology is considered for hyperspectral imagers (HSI), but there are not three dimensions to evaluate for an HSI as the spectral dimension cannot be used. Therefore, the concepts from 3D noise are used to discover that the spatial noise of the imager is the main source of noise in the sensor

FINCH: A Blueprint for Accessible and Scientifically Valuable Remote Sensing Satellite Missions

Satellite remote sensing missions have grown in popularity over the past fifteen years due to their ability to cover large swaths of land at regular time intervals, making them suitable for monitoring environmental trends such as greenhouse gas emissions and agricultural practices. As environmental monitoring becomes central in global efforts to combat climate change, accessible platforms for contributing to this research are critical. Many remote sensing missions demand high performance of payloads, restricting research and development to organizations with sufficient resources to address these challenges. Atmospheric remote sensing missions, for example, require extremely high spatial and spectral resolutions to generate scientifically useful results. As an undergraduate-led design team, the University of Toronto Aerospace Team’s Space Systems Division has performed an extensive mission selection process to find a feasible and impactful mission focusing on crop residue mapping. This mission profile provides the data needed to improve crop residue retention practices and reduce greenhouse gas emissions from soil, while relaxing performance requirements relative to many active atmospheric sensing missions. This is accompanied by the design of FINCH, a 3U CubeSat with a hyperspectral camera composed of custom and commercial off-the-shelf components. The team’s custom composite payload, the FINCH Eye, strives to advance performance achieved at this form factor by leveraging novel technologies while keeping design feasibility for a student team a priority. Optical and mechanical design decisions and performance are detailed, as well as assembly, integration, and testing considerations. Beyond its design, the FINCH Eye is examined from operational, timeline, and financial perspectives, and a discussion of the supporting firmware, data processing, and attitude control systems is included. Insight is provided into open-source tools that the team has developed to aid in the design process, including a linear error analysis tool for assessing scientific performance, an optical system tradeoff analysis tool, and data processing algorithms. Ultimately, the team presents a comprehensive case study of an accessible and impactful satellite optical payload design process, in hopes of serving as a blueprint for future design teams seeking to contribute to remote sensing research

Mineral exploration of rock wastes from sulfide mining using airborne hyperspectral imaging

Open-pit sulfide mining produces large quantities of waste rock that may contain materials of economic interest. The exposure of sulfides accumulation may also pose a hazard to the environment by causing phenomena such as acid mine drainage. This Master Thesis aims to map and provide a geological characterization of the rock wastes of Corta Atalaya open pit in Río Tinto, Spain. For this purpose, different hyperspectral imaging technologies that have already demonstrated their effectiveness in mineral detection such as airborne remote sensing in the VNIR and SWIR domain are used. This study is complemented with the incorporation of an innovative hyperspectral method, the airborne LWIR. Our approach makes use of a set of different spectral methods, and established image processing routines, such as band ratios, and minimum wavelength maps. Supervised classifications are also employed as a mean to extrapolate mapped rock types to larger unmapped areas, spectral angle maps, and to identify high abundances of endmember lithologies, spectral unmixing techniques. Furthermore, this study will lay the foundations and pave the way for possible future lines of research regarding the Corta Atalaya rock wastes.Universidad de Granada. Máster en Geofísica y Meteorología (GEOMET). Curso 2019-2020European Union’s Horizon 2020 research and innovation programme under grant agreement nº 77648

HYPERSPECTRAL IMAGING USING SILICON MICROMACHINED VIBRATORY GRATING SCANNERS

Ph.DDOCTOR OF PHILOSOPH

A comprehensive approach for the efficient acquisition and processing of hyperspectral images and sequence

Programa Oficial de Doctorado en Computación. 5009P01[Abstract] Despite the scientific and technological developments achieved during the last two decades in the hyperspectral field, some methodological, operational and conceptual issues have restricted the progress, promotion and popular dissemination of this technology. These shortcomings include the specialized knowledge required for the acquisition of hyperspectral images, the shortage of publicly accessible hyperspectral image repositories with reliable ground truth images or the lack of methodologies that allow for the adaptation of algorithms to particular user or application processing needs. The work presented here has the objective of contributing to the hyperspectral field with procedures for the automatic acquisition of hyperspectral scenes, including the hardware adaptation of our own imagers and the development of methods for the calibration and correction of the hyperspectral datacubes, the creation of a publicly available hyperspectral repository of well categorized and labeled images and the design and implementation of novel computational intelligence based processing techniques that solve typical issues related to the segmentation and denoising of hyperspectral images as well as sequences of them taking into account their temporal evolution.[Resumen] A pesar de los desarrollos tecnológicos y científicos logrados en el campo hiperespectral durante las dos últimas décadas, alg\mas limitaciones de tipo metodológico, operacional y conceptual han restringido el progreso, difusión y popularización de esta tecnología, entre ellas, el conocimiento especializado requerido en la adquisición de imágenes hiperespectrales, la carencia de repositorios de imágenes hiperespectrales con etiquetados fiables y de acceso público o la falta de metodologías que posibiliten la adaptación de algoritmos a usuarios o necesidades de procesamiento concretas. Este trabajo doctoral tiene el objetivo de contribuir al campo hiperespectral con procedimientos para la adquisición automática de escenas hiperespectrales, incluyendo la adaptación hardware de cámaras hiperespectrales propias y el desarrollo de métodos para la calibración y corrección de cubos de datos hiperespectrales; la creación de un repositorio hiperespectral de acceso público con imágenes categorizadas y con verdades de terreno fiables; y el diseño e implementación de técnicas de procesamiento basadas en inteligencia computacional para la resolución de problemas típicamente relacionados con las tareas de segmentación y eliminación de ruido en imágenes estáticas y secuencias de imágenes hiperespectrales teniendo en consideración su evolución temporal.[Resumo] A pesar dos desenvolvementos tecnolóxicos e científicos logrados no campo hiperespectral durante as dúas últimas décadas, algunhas lirrútacións de tipo metodolóxico¡ operacional e conceptual restrinxiron o progreso) difusión e popularización desta tecnoloxía, entre elas, o coñecemento especializado requirido na adquisición de imaxes hiperespectrales¡ a carencia de repositorios de irnaxes hiperespectrales con etiquetaxes fiables e de acceso público ou a falta de metodoloxías que posibiliten a adaptación de algoritmos a usuarios ou necesidades de procesamento concretas. Este traballo doutoral ten o obxectívo de contribuir ao campo hiperespectral con procedementos para a adquisición automática de eicenas hiperespectrais, incluíndo a adaptación hardware de cámaras hiperespectrales propias e o desenvolvemento de métodos para a calibración e corrección de cubos de datos hiperespectrais; a creación dun repositorio hiperespectral de acceso público con imaxes categorizadas e con verdades de terreo fiables; e o deseño e implementación de técnicas de procesamento baseadas en intelixencia computacional para a resolución de problemas tipicamente relacionado~ coas tarefas de segmentación e eliminación de ruído en imaxes estáticas e secuencias de imaxes hiperespectrai~ tendo en consideración a súa evolución temporal

The Need for Accurate Pre-processing and Data Integration for the Application of Hyperspectral Imaging in Mineral Exploration

Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften.Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications

SIMBIO-SYS : Scientific Cameras and Spectrometer for the BepiColombo Mission

The SIMBIO-SYS (Spectrometer and Imaging for MPO BepiColombo Integrated Observatory SYStem) is a complex instrument suite part of the scientific payload of the Mercury Planetary Orbiter for the BepiColombo mission, the last of the cornerstone missions of the European Space Agency (ESA) Horizon + science program. The SIMBIO-SYS instrument will provide all the science imaging capability of the BepiColombo MPO spacecraft. It consists of three channels: the STereo imaging Channel (STC), with a broad spectral band in the 400-950 nm range and medium spatial resolution (at best 58 m/px), that will provide Digital Terrain Model of the entire surface of the planet with an accuracy better than 80 m; the High Resolution Imaging Channel (HRIC), with broad spectral bands in the 400-900 nm range and high spatial resolution (at best 6 m/px), that will provide high-resolution images of about 20% of the surface, and the Visible and near-Infrared Hyperspectral Imaging channel (VIHI), with high spectral resolution (6 nm at finest) in the 400-2000 nm range and spatial resolution reaching 120 m/px, it will provide global coverage at 480 m/px with the spectral information, assuming the first orbit around Mercury with periherm at 480 km from the surface. SIMBIO-SYS will provide high-resolution images, the Digital Terrain Model of the entire surface, and the surface composition using a wide spectral range, as for instance detecting sulphides or material derived by sulphur and carbon oxidation, at resolutions and coverage higher than the MESSENGER mission with a full co-alignment of the three channels. All the data that will be acquired will allow to cover a wide range of scientific objectives, from the surface processes and cartography up to the internal structure, contributing to the libration experiment, and the surface-exosphere interaction. The global 3D and spectral mapping will allow to study the morphology and the composition of any surface feature. In this work, we describe the on-ground calibrations and the results obtained, providing an important overview of the instrument performances. The calibrations have been performed at channel and at system levels, utilizing specific setup in most of the cases realized for SIMBIO-SYS. In the case of the stereo camera (STC), it has been necessary to have a validation of the new stereo concept adopted, based on the push-frame. This work describes also the results of the Near-Earth Commissioning Phase performed few weeks after the Launch (20 October 2018). According to the calibration results and the first commissioning the three channels are working very well.Peer reviewe