Hyperspectral and Hypertemporal Longwave Infrared Data Characterization

The Army Research Lab conducted a persistent imaging experiment called the Spectral and Polarimetric Imagery Collection Experiment (SPICE) in 2012 and 2013 which focused on collecting and exploiting long wave infrared hyperspectral and polarimetric imagery. A part of this dataset was made for public release for research and development purposes. This thesis investigated the hyperspectral portion of this released dataset through data characterization and scene characterization of man-made and natural objects. First, the data were contrasted with MODerate resolution atmospheric TRANsmission (MODTRAN) results and found to be comparable. Instrument noise was characterized using an in-scene black panel, and was found to be comparable with the sensor manufacturer\u27s specication. The temporal and spatial variation of certain objects in the scene were characterized. Temporal target detection was conducted on man-made objects in the scene using three target detection algorithms: spectral angle mapper (SAM), spectral matched lter (SMF) and adaptive coherence/cosine estimator (ACE). SMF produced the best results for detecting the targets when the training and testing data originated from different time periods, with a time index percentage result of 52.9%. Unsupervised and supervised classication were conducted using spectral and temporal target signatures. Temporal target signatures produced better visual classication than spectral target signature for unsupervised classication. Supervised classication yielded better results using the spectral target signatures, with a highest weighted accuracy of 99% for 7-class reference image. Four emissivity retrieval algorithms were applied on this dataset. However, the retrieved emissivities from all four methods did not represent true material emissivity and could not be used for analysis. This spectrally and temporally rich dataset enabled to conduct analysis that was not possible with other data collections. Regarding future work, applying noise-reduction techniques before applying temperature-emissivity retrieval algorithms may produce more realistic emissivity values, which could be used for target detection and material identification

Application of an Imaging Fourier-Transform Spectrometer for the Means of Combustion Diagnostics

A passive remote sensing technique for accurately monitoring the combustion efficiency of petrochemical flares is greatly desired. A Phase II DOE-funded SBIR lead by Spectral Sciences, Inc. is underway to develop such a method. This paper presents an overview of the progress of AFIT\u27s contribution. A Telops Hyper-Cam Mid-wave infrared imaging Fourier-transform spectrometer is used to examine a flame produced by a Hencken burner. Ethylene fuel was burned at four different equivalency ratios φ = 0:80; 0:91; 1:0 and 1:25. Presented is qualitative spectrally-resolved visualization of a Hencken burner flame and the spatial distribution of combustion by-products. The flame spectra were characterized by structured emissions from CO2, H2O and CO. A single-layer model is developed to estimate the temperature and H2O and CO2 concentrations using spectrally-resolved flame emissions between 3100 cm-1 ≤ ν ≤ 3500 cm-1. At the flame center 10 mm above the burner, temperature was estimated as T = 2172 ± 28 K, this compares favorably with recently reported OH-absorption measurements (T = 2226 ± 112 K) and equilibrium calculations (T = 2302 K). H2O and CO2 mole fractions at the same height of 10 mm were measured to be 13:7 ± 0:6% and 15:5 ± 0:8%, respectively

Remote Quantification of Smokestack Total Effluent Mass Flow Rates Using Imaging Fourier-Transform Spectroscopy

A Telops Hyper-Cam midwave infrared (1.5 − 5.5μm) imaging Fourier-transform spectrometer (IFTS) was used to estimate industrial smokestack total effluent mass flow rates by combining spectrally-determined species concentrations with flow rates estimated via analysis of sequential images in the raw interferogram cube. Strong emissions from H2O, CO2, CO, SO2, and NO were observed in the spectrum. A previously established plume radiative transfer model was used to estimate gas concentrations, and a simple temporal cross-correlation analysis of sequential imagery enabled an estimation of the flow velocity. Final effluent mass flow rates for CO2 and SO2 of 13.5 ± 3.78 kg/s and 71.3 ± 19.3 g/s were in good agreement with in situ rates of 11.6 ± 0.07 kg/s and 67.8 ± 0.52 g/s. NO was estimated at 16.1 ± 4.19 g/s, which did not compare well to the total NOx (NO + NO2) reported value of 11.2 ± 0.16 g/s. Unmonitored H2O, HCl, and CO were also estimated at 7.76 ± 2.25 kg/s, 7.40 ± 2.00 g/s, and 15.0 ± 4.05 g/s respectively

Airborne Forward-Looking Interferometer for the Detection of Terminal-Area Hazards

The Forward Looking Interferometer (FLI) program was a multi-year cooperative research effort to investigate the use of imaging radiometers with high spectral resolution, using both modeling/simulation and field experiments, along with sophisticated data analysis techniques that were originally developed for analysis of data from space-based radiometers and hyperspectral imagers. This investigation has advanced the state of knowledge in this technical area, and the FLI program developed a greatly improved understanding of the radiometric signal strength of aviation hazards in a wide range of scenarios, in addition to a much better understanding of the real-world functionality requirements for hazard detection instruments. The project conducted field experiments on three hazards (turbulence, runway conditions, and wake vortices) and analytical studies on several others including volcanic ash, reduced visibility conditions, in flight icing conditions, and volcanic ash

Mineral identification using data-mining in hyperspectral infrared imagery

Les applications de l’imagerie infrarouge dans le domaine de la géologie sont principalement des applications hyperspectrales. Elles permettent entre autre l’identification minérale, la cartographie, ainsi que l’estimation de la portée. Le plus souvent, ces acquisitions sont réalisées in-situ soit à l’aide de capteurs aéroportés, soit à l’aide de dispositifs portatifs. La découverte de minéraux indicateurs a permis d’améliorer grandement l’exploration minérale. Ceci est en partie dû à l’utilisation d’instruments portatifs. Dans ce contexte le développement de systèmes automatisés permettrait d’augmenter à la fois la qualité de l’exploration et la précision de la détection des indicateurs. C’est dans ce cadre que s’inscrit le travail mené dans ce doctorat. Le sujet consistait en l’utilisation de méthodes d’apprentissage automatique appliquées à l’analyse (au traitement) d’images hyperspectrales prises dans les longueurs d’onde infrarouge. L’objectif recherché étant l’identification de grains minéraux de petites tailles utilisés comme indicateurs minéral -ogiques. Une application potentielle de cette recherche serait le développement d’un outil logiciel d’assistance pour l’analyse des échantillons lors de l’exploration minérale. Les expériences ont été menées en laboratoire dans la gamme relative à l’infrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m à 11.8 m. Ces essais ont permis de proposer une méthode pour calculer l’annulation du continuum. La méthode utilisée lors de ces essais utilise la factorisation matricielle non négative (NMF). En utlisant une factorisation du premier ordre on peut déduire le rayonnement de pénétration, lequel peut ensuite être comparé et analysé par rapport à d’autres méthodes plus communes. L’analyse des résultats spectraux en comparaison avec plusieurs bibliothèques existantes de données a permis de mettre en évidence la suppression du continuum. Les expérience ayant menés à ce résultat ont été conduites en utilisant une plaque Infragold ainsi qu’un objectif macro LWIR. L’identification automatique de grains de différents matériaux tels que la pyrope, l’olivine et le quartz a commencé. Lors d’une phase de comparaison entre des approches supervisées et non supervisées, cette dernière s’est montrée plus approprié en raison du comportement indépendant par rapport à l’étape d’entraînement. Afin de confirmer la qualité de ces résultats quatre expériences ont été menées. Lors d’une première expérience deux algorithmes ont été évalués pour application de regroupements en utilisant l’approche FCC (False Colour Composite). Cet essai a permis d’observer une vitesse de convergence, jusqu’a vingt fois plus rapide, ainsi qu’une efficacité significativement accrue concernant l’identification en comparaison des résultats de la littérature. Cependant des essais effectués sur des données LWIR ont montré un manque de prédiction de la surface du grain lorsque les grains étaient irréguliers avec présence d’agrégats minéraux. La seconde expérience a consisté, en une analyse quantitaive comparative entre deux bases de données de Ground Truth (GT), nommée rigid-GT et observed-GT (rigide-GT: étiquet manuel de la région, observée-GT:étiquetage manuel les pixels). La précision des résultats était 1.5 fois meilleur lorsque l’on a utlisé la base de données observed-GT que rigid-GT. Pour les deux dernières epxérience, des données venant d’un MEB (Microscope Électronique à Balayage) ainsi que d’un microscopie à fluorescence (XRF) ont été ajoutées. Ces données ont permis d’introduire des informations relatives tant aux agrégats minéraux qu’à la surface des grains. Les résultats ont été comparés par des techniques d’identification automatique des minéraux, utilisant ArcGIS. Cette dernière a montré une performance prometteuse quand à l’identification automatique et à aussi été utilisée pour la GT de validation. Dans l’ensemble, les quatre méthodes de cette thèse représentent des méthodologies bénéfiques pour l’identification des minéraux. Ces méthodes présentent l’avantage d’être non-destructives, relativement précises et d’avoir un faible coût en temps calcul ce qui pourrait les qualifier pour être utilisée dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7μm to 11.8μm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grain’s surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grain’s surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field

Estimating Index of Refraction from Polarimetric Hyperspectral Imaging Measurements

Current material identification techniques rely on estimating reflectivity or emissivity which vary with viewing angle. As off-nadir remote sensing platforms become increasingly prevalent, techniques robust to changing viewing geometries are desired. A technique leveraging polarimetric hyperspectral imaging (P-HSI), to estimate complex index of refraction, N̂(ν̃), an inherent material property, is presented. The imaginary component of N̂(ν̃) is modeled using a small number of “knot” points and interpolation at in-between frequencies ν̃. The real component is derived via the Kramers-Kronig relationship. P-HSI measurements of blackbody radiation scattered off of a smooth quartz window show that N̂(ν̃) can be retrieved to within 0.08 RMS error between 875 cm−1 ≤ ν̃ ≤ 1250 cm−1. P-HSI emission measurements of a heated smooth Pyrex beaker also enable successful N̂(ν̃) estimates, which are also invariant to object temperature

Constraining industrial ammonia emissions using hyperspectral infrared imaging

Atmospheric emissions of reactive nitrogen in the form of nitrogen dioxide (NO) and ammonia (NH) worsen air quality and upon deposition, dramatically affect the environment. Recent infrared satellite measurements have revealed that NH emitted by industries are an important and underestimated emission source. Yet, to assess these emissions, current satellite sounders are severely limited by their spatial resolution. In this paper, we analyse measurement data recorded in a series of imaging surveys that were conducted over industries in the Greater Berlin area (Germany). On board the aircraft were the Telops Hyper-Cam LW, targeting NH measurements in the longwave infrared at a resolution of 4 m and the SWING+ spectrometer targeting NO measurements in the UV–Vis at a resolution of 180 m. Two flights were carried out over German’s largest production facility of synthetic NH , urea and other fertilizers. In both cases, a large NH plume was observed originating from the factory. Using a Gaussian plume model to take into account plume rise and dispersion, coupled with well-established radiative transfer and inverse methods, we retrieve vertical column densities. From these, we calculate NH emission fluxes using the integrated mass enhancement and cross-sectional flux methods, yielding consistent emissions of the order of 2200 t yr−1 for both flights, assuming constant fluxes across the year. These estimates are about five times larger than those reported in the European Pollutant Release and Transfer Register (E-PRTR) for this plant. In the second campaign, a co-emitted NO plume was measured, likely related to the production of nitric acid at the plant. A third flight was carried out over an area comprising the cities of Staßfurt and Bernburg. Several small NH plumes were seen, one over a production facility of mineral wool insulation, one over a sugar factory and two over the soda ash plants in Staßfurt and Bernburg. A fifth and much larger plume was seen to originate from the sedimentation basins associated with the soda ash plant in Staßfurt, indicating rapid volatilization of ammonium rich effluents. We use the different measurement campaigns to simulate measurements of Nitrosat, a potential future satellite sounder dedicated to the sounding of reactive nitrogen at a resolution of 500 m. We demonstrate that such measurements would allow accurately constraining emissions in a single overpass, overcoming a number of important drawbacks of current satellite sounders

Study of Laminar Flame 2-D Scalar Values at Various Fuel to Air Ratios Using an Imaging Fourier-Transform Spectrometer and 2-D CFD Analysis

This work furthers an ongoing effort to develop imaging Fourier-transform spectrometry (IFTS) for combustion diagnostics and to validate reactive-flow computational fluid dynamics (CFD) predictions. An ideal, laminar flame produced by an ethylene-fueled (C2H4) Hencken burner (25.4 x 25.4 mm2 burner) with N2 co-flow was studied using a Telops infrared IFTS featuring an Indium Antimonide (InSb), 1.5 to 5.5 µm, focal-plane array imaging the scene through a Michelson interferometer. Flame equivalency ratios of Φ = 0.81, 0.91, and 1.11 were imaged on a 128 x 200 pixel array with a 0.48 mm per pixel spatial resolution and 0.5 cm-1 spectral resolution. A single-layer radiative transfer model based on the Line-by-Line Radiative Transfer Model (LBLRTM) code and High Resolution Transmission (HITRAN) spectral database for high-temperature work (HITEMP) was used to simultaneously retrieve temperature (T) and concentrations of water (H2O) and carbon dioxide (CO2) from individual pixel spectra between 3100-3500 cm-1 spanning the flame at heights of 5 mm and 10 mm above the burner. CO2 values were not determined as reliably as H2O due to its smooth, unstructured spectral features in this window. At 5 mm height near flame center, spectrally-estimated T\u27s were 2150, 2200, & 2125 K for Φ = 0.81, 0.91, & 1.11 respectively, which are within 5% of previously reported experimental findings. Additionally, T & H2O compared favorably to adiabatic flame temperatures (2175, 2300, 2385 K) and equilibrium concentrations (10.4, 11.4, 12.8%) computed by NASA-Glenn\u27s Chemical Equilibrium with Applications (CEA) program. UNICORN CFD predictions were in excellent agreement with CEA calculations at flame center, and predicted a fall-off in both T and H2O with distance from flame center more slowly than the spectrally-estimated values. This is likely a shortcoming of the homogeneous assumption imposed by the single-layer model. Pixel-to-pixel variations in T and H2O were observed

Tinto: Multisensor Benchmark for 3-D Hyperspectral Point Cloud Segmentation in the Geosciences

The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2-D image data, which is insufficient for 3-D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multisensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for nonstructured 3-D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground truth. The point cloud is dense and contains 3242964 labeled points. We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping. By making Tinto publicly available, we hope to foster the development and adaptation of new deep learning tools for 3-D applications in Earth sciences. The dataset can be accessed through this link: https://doi.org/10.14278/rodare.2256