38 research outputs found

    Learning Set Representations for LWIR In-Scene Atmospheric Compensation

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    Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed in point cloud classification. When applied to collected hyperspectral image data, this method shows comparable performance to Fast Line-of-Sight Atmospheric Analysis of Hypercubes-Infrared (FLAASH-IR), using an auto- mated pixel selection approach. Additionally, inference time is significantly reduced compared to FLAASH-IR with predictions made on average in 0.24 s on a 128 pixel by 5000 pixel data cube using a mobile graphics card. This computational speed-up on a low-power platform results in an autonomous atmospheric compensation method effective for real-time, onboard use, while only requiring a diversity of materials in the scene

    Physics-constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios

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    Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using collected data. These deep learning-based compensation algorithms resulted in comparable detection performance to established methods while accelerating the image processing chain by 8X

    Mineral identification using data-mining in hyperspectral infrared imagery

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    Les applications de l’imagerie infrarouge dans le domaine de la gĂ©ologie sont principalement des applications hyperspectrales. Elles permettent entre autre l’identification minĂ©rale, la cartographie, ainsi que l’estimation de la portĂ©e. Le plus souvent, ces acquisitions sont rĂ©alisĂ©es in-situ soit Ă  l’aide de capteurs aĂ©roportĂ©s, soit Ă  l’aide de dispositifs portatifs. La dĂ©couverte de minĂ©raux indicateurs a permis d’amĂ©liorer grandement l’exploration minĂ©rale. Ceci est en partie dĂ» Ă  l’utilisation d’instruments portatifs. Dans ce contexte le dĂ©veloppement de systĂšmes automatisĂ©s permettrait d’augmenter Ă  la fois la qualitĂ© de l’exploration et la prĂ©cision de la dĂ©tection des indicateurs. C’est dans ce cadre que s’inscrit le travail menĂ© dans ce doctorat. Le sujet consistait en l’utilisation de mĂ©thodes d’apprentissage automatique appliquĂ©es Ă  l’analyse (au traitement) d’images hyperspectrales prises dans les longueurs d’onde infrarouge. L’objectif recherchĂ© Ă©tant l’identification de grains minĂ©raux de petites tailles utilisĂ©s comme indicateurs minĂ©ral -ogiques. Une application potentielle de cette recherche serait le dĂ©veloppement d’un outil logiciel d’assistance pour l’analyse des Ă©chantillons lors de l’exploration minĂ©rale. Les expĂ©riences ont Ă©tĂ© menĂ©es en laboratoire dans la gamme relative Ă  l’infrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m Ă  11.8 m. Ces essais ont permis de proposer une mĂ©thode pour calculer l’annulation du continuum. La mĂ©thode utilisĂ©e lors de ces essais utilise la factorisation matricielle non nĂ©gative (NMF). En utlisant une factorisation du premier ordre on peut dĂ©duire le rayonnement de pĂ©nĂ©tration, lequel peut ensuite ĂȘtre comparĂ© et analysĂ© par rapport Ă  d’autres mĂ©thodes plus communes. L’analyse des rĂ©sultats spectraux en comparaison avec plusieurs bibliothĂšques existantes de donnĂ©es a permis de mettre en Ă©vidence la suppression du continuum. Les expĂ©rience ayant menĂ©s Ă  ce rĂ©sultat ont Ă©tĂ© conduites en utilisant une plaque Infragold ainsi qu’un objectif macro LWIR. L’identification automatique de grains de diffĂ©rents matĂ©riaux tels que la pyrope, l’olivine et le quartz a commencĂ©. Lors d’une phase de comparaison entre des approches supervisĂ©es et non supervisĂ©es, cette derniĂšre s’est montrĂ©e plus appropriĂ© en raison du comportement indĂ©pendant par rapport Ă  l’étape d’entraĂźnement. Afin de confirmer la qualitĂ© de ces rĂ©sultats quatre expĂ©riences ont Ă©tĂ© menĂ©es. Lors d’une premiĂšre expĂ©rience deux algorithmes ont Ă©tĂ© Ă©valuĂ©s 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 efficacitĂ© significativement accrue concernant l’identification en comparaison des rĂ©sultats de la littĂ©rature. Cependant des essais effectuĂ©s sur des donnĂ©es LWIR ont montrĂ© un manque de prĂ©diction de la surface du grain lorsque les grains Ă©taient irrĂ©guliers avec prĂ©sence d’agrĂ©gats minĂ©raux. La seconde expĂ©rience a consistĂ©, en une analyse quantitaive comparative entre deux bases de donnĂ©es de Ground Truth (GT), nommĂ©e rigid-GT et observed-GT (rigide-GT: Ă©tiquet manuel de la rĂ©gion, observĂ©e-GT:Ă©tiquetage manuel les pixels). La prĂ©cision des rĂ©sultats Ă©tait 1.5 fois meilleur lorsque l’on a utlisĂ© la base de donnĂ©es observed-GT que rigid-GT. Pour les deux derniĂšres epxĂ©rience, des donnĂ©es venant d’un MEB (Microscope Électronique Ă  Balayage) ainsi que d’un microscopie Ă  fluorescence (XRF) ont Ă©tĂ© ajoutĂ©es. Ces donnĂ©es ont permis d’introduire des informations relatives tant aux agrĂ©gats minĂ©raux qu’à la surface des grains. Les rĂ©sultats ont Ă©tĂ© comparĂ©s par des techniques d’identification automatique des minĂ©raux, utilisant ArcGIS. Cette derniĂšre a montrĂ© une performance prometteuse quand Ă  l’identification automatique et Ă  aussi Ă©tĂ© utilisĂ©e pour la GT de validation. Dans l’ensemble, les quatre mĂ©thodes de cette thĂšse reprĂ©sentent des mĂ©thodologies bĂ©nĂ©fiques pour l’identification des minĂ©raux. Ces mĂ©thodes prĂ©sentent l’avantage d’ĂȘtre non-destructives, relativement prĂ©cises et d’avoir un faible coĂ»t en temps calcul ce qui pourrait les qualifier pour ĂȘtre utilisĂ©e 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 (7.7ÎŒm to 11.8ÎŒm - 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

    Fast and Effective Techniques for LWIR Radiative Transfer Modeling: A Dimension-Reduction Approach

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    The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. These detailed measurements allow for material classification, with many recent advancements from the fields of machine learning and deep learning. In many scenarios, the hyperspectral image must first be corrected or compensated for atmospheric effects. Radiative Transfer (RT) computations can provide look up tables (LUTs) to support these corrections. This research investigates a dimension-reduction approach using machine learning methods to create an effective sensor-specific long-wave infrared (LWIR) RT model

    Passively Estimating Index of Refraction for Specular Reflectors Using Polarimetric Hyperspectral Imaging

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    As off-nadir viewing platforms becoming increasingly prevalent in remote sensing, material classification and ID techniques robust to changing viewing geometries must be developed. Traditionally, either reflectivity or emissivity are used for classification, but these quantities vary with viewing angle. Instead, estimating index of refraction may be advantageous as it is invariant with respect to viewing geometry. This work focuses on estimating index of refraction from LWIR (875-1250 wavenumbers) polarimetric hyperspectral radiance measurements

    Multimodal Representation Learning and Set Attention for LWIR In-Scene Atmospheric Compensation

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    A multimodal generative modeling approach combined with permutation-invariant set attention is investigated in this paper to support long-wave infrared (LWIR) in-scene atmospheric compensation. The generative model can produce realistic atmospheric state vectors (T;H2O;O3) and their corresponding transmittance, upwelling radiance, and downwelling radiance (TUD) vectors by sampling a low-dimensional space. Variational loss, LWIR radiative transfer loss and atmospheric state loss constrain the low-dimensional space, resulting in lower reconstruction error compared to standard mean-squared error approaches. A permutation-invariant network predicts the generative model low-dimensional components from in-scene data, allowing for simultaneous estimates of the atmospheric state and TUD vector. Forward modeling the predicted atmospheric state vector results in a second atmospheric compensation estimate. Results are reported for collected LWIR data and compared to Fast Line-of-Sight Atmospheric Analysis of Hypercubes - Infrared (FLAASH-IR), demonstrating commensurate performance when applied to a target detection scenario. Additionally, an approximate 8 times reduction in detection time is realized using this neural network-based algorithm compared to FLAASH-IR. Accelerating the target detection pipeline while providing multiple atmospheric estimates is necessary for many real-world, time sensitive tasks

    Estimating Index of Refraction from Polarimetric Hyperspectral Imaging Measurements

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    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(Μ̃), an inherent material property, is presented. The imaginary component of N̂(Μ̃) is modeled using a small number of “knot” points and interpolation at in-between frequencies Μ̃. 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(Μ̃) can be retrieved to within 0.08 RMS error between 875 cm−1 ≀ Μ̃ ≀ 1250 cm−1. P-HSI emission measurements of a heated smooth Pyrex beaker also enable successful N̂(Μ̃) estimates, which are also invariant to object temperature

    Midwave Infrared Imaging Fourier Transform Spectrometry of Combustion Plumes

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    A midwave infrared (MWIR) imaging Fourier transform spectrometer (IFTS) was used to successfully capture and analyze hyperspectral imagery of combustion plumes. Jet engine exhaust data from a small turbojet engine burning diesel fuel at a flow rate of 300 cm3/min was collected at 1 cm−1 resolution from a side-plume vantage point on a 200x64 pixel window at a range of 11.2 meters. Spectral features of water, CO, and CO2 were present, and showed spatial variability within the plume structure. An array of thermocouple probes was positioned within the plume to aid in temperature analysis. A single-temperature plume model was implemented to obtain spatiallyvarying temperatures and plume concentrations. Model-fitted temperatures of 811 ± 1.5 K and 543 ± 1.6 K were obtained from plume regions in close proximity to thermocouple probes measuring temperatures of 719 K and 522 K, respectively. Industrial smokestack plume data from a coal-burning stack collected at 0.25 cm−1 resolution at a range of 600 meters featured strong emission from NO, CO, CO2, SO2, and HCl in the spectral region 1800-3000 cm−1. A simplified radiative transfer model was employed to derive temperature and concentrations for clustered regions of the 128x64 pixel scene, with corresponding statistical error bounds. The hottest region (closest to stack centerline) was 401 ± 0.36 K, compared to an in-stack measurement of 406 K, and model-derived concentration values of NO, CO2, and SO2 were 140 ± 1 ppmV, 110,400 ± 950 ppmV, and 382 ± 4 ppmV compared to in-stack measurements of 120 ppmV (NOχ), 94,000 ppmV, and 382 ppmV, respectively. In-stack measurements of CO and HCl were not provided by the stack operator, but model-derived values of 19 ± 0.2 ppmV and 111 ± 1 ppmV are reported near stack centerline. A deployment to Dugway Proving Grounds, UT to collect hyperspectral imagery of chemical and biological threat agent simulants resulted in weak spectral signatures from several species. Plume detection of methyl salicilate was achieved from both a stack release and explosive detonation, although spectral identification was not accomplished due to weak signal strength

    Performance metrics for the evaluation of hyperspectral chemical identification systems

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    Remote sensing of chemical vapor plumes is a difficult but important task for many military and civilian applications. Hyperspectral sensors operating in the long-wave infrared regime have well-demonstrated detection capabilities. However, the identification of a plume’s chemical constituents, based on a chemical library, is a multiple hypothesis testing problem which standard detection metrics do not fully describe. We propose using an additional performance metric for identification based on the so-called Dice index. Our approach partitions and weights a confusion matrix to develop both the standard detection metrics and identification metric. Using the proposed metrics, we demonstrate that the intuitive system design of a detector bank followed by an identifier is indeed justified when incorporating performance information beyond the standard detection metrics.United States. Defense Threat Reduction Agency (Air Force contract FA8721-05-C-0002

    Characterization of the Earth\u27s surface and atmosphere for multispectral and hyperspectral thermal imagery

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    The goal of this research was to develop a new approach to solve the inverse problem of thermal remote sensing of the Earth. This problem falls under a large class of inverse problems that are ill-conditioned because there are many more unknowns than observations. The approach is based on a multivariate analysis technique known as Canonical Correlation Analysis (CCA). By collecting two ensembles of observations, it is possible to find the latent dimensionality where the data are maximally correlated. This produces a reduced and orthogonal space where the problem is not ill-conditioned. In this research, CCA was used to extract atmospheric physical parameters such as temperature and water vapor profiles from multispectral and hyperspectral thermal imagery. CCA was also used to infer atmospheric optical properties such as spectral transmission, upwelled radiance, and downwelled radiance. These properties were used to compensate images for atmospheric effects and retrieve surface temperature and emissivity. Results obtained from MODTRAN simulations, the MODerate resolution Imaging Spectrometer (MODIS) Airborne Sensor (MAS), and the MODIS and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (MASTER) airborne sensor show that it is feasible to retrieve land surface temperature and emissivity with 1.0 K and 0.01 accuracies, respectively
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