Bibliografías temáticas / Signature hyperspectrale

Literatura académica sobre el tema "Signature hyperspectrale"

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Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Signature hyperspectrale"

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Papp, Adam, Julian Pegoraro, Daniel Bauer, Philip Taupe, Christoph Wiesmeyr y Andreas Kriechbaum-Zabini. "Automatic Annotation of Hyperspectral Images and Spectral Signal Classification of People and Vehicles in Areas of Dense Vegetation with Deep Learning". Remote Sensing 12, n.º 13 (1 de julio de 2020): 2111. http://dx.doi.org/10.3390/rs12132111.

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Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral camera—installed on the ground or possibly a drone—and detection based on spectral signatures. We introduce a novel automatic method for annotating spectral signatures based on a combination of state-of-the-art deep learning methods. After we collected millions of samples with our method, we used a deep learning approach to train a classifier to detect people and cars. Our results show that, based only on spectral signature classification, we can achieve an Matthews Correlation Coefficient of 0.83. We evaluate our classification method in areas with varying vegetation and discuss the limitations and constraints that the current hyperspectral imaging technology has. We conclude that spectral signature classification is possible with high accuracy in uncontrolled outdoor environments. Nevertheless, even with state-of-the-art compact passive hyperspectral imaging technology, high dynamic range of illumination and relatively low image resolution continue to pose major challenges when developing object detection algorithms for areas of dense vegetation.
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Gromov, V. P., L. I. Lebedev y V. E. Turlapov. "Analysis and object markup of hyperspectral images for machine learning methods". Information Technology and Nanotechnology, n.º 2391 (2019): 309–17. http://dx.doi.org/10.18287/1613-0073-2019-2391-309-317.

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The development of the nominal sequence of steps for analyzing the HSI proposed by Landgrebe, which is necessary in the context of the appearance of reference signature libraries for environmental monitoring, is discussed. The approach is based on considering the HSI pixel as a signature that stores all spectral features of an object and its states, and the HSI as a whole - as a two-dimensional signature field. As a first step of the analysis, a procedure is proposed for detecting a linear dependence of signatures by the magnitude of the Pearson correlation coefficient. The main apparatus of analysis, as in Landgrebe sequence, is the method of principal component analysis, but it is no longer used to build classes and is applied to investigate the presence in the class of subclasses essential for the applied area. The experimental material includes such objects as water, swamps, soil, vegetation, concrete, pollution. Selection of object samples on the image is made by the user. From the studied images of HSI objects, a base of reference signatures for classes (subclasses) of objects is formed, which in turn can be used to automate HSI markup with the aim of applying machine learning methods to recognize HSI objects and their states.
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Hartfield, Kyle, Jeffrey K. Gillan, Cynthia L. Norton, Charles Conley y Willem J. D. van Leeuwen. "A Novel Spectral Index to Identify Cacti in the Sonoran Desert at Multiple Scales Using Multi-Sensor Hyperspectral Data Acquisitions". Land 11, n.º 6 (26 de mayo de 2022): 786. http://dx.doi.org/10.3390/land11060786.

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Accurate identification of cacti, whether seen as an indicator of ecosystem health or an invasive menace, is important. Technological improvements in hyperspectral remote sensing systems with high spatial resolutions make it possible to now monitor cacti around the world. Cacti produce a unique spectral signature because of their morphological and anatomical characteristics. We demonstrate in this paper that we can leverage a reflectance dip around 972 nm, due to cacti’s morphological structure, to distinguish cacti vegetation from non-cacti vegetation in a desert landscape. We also show the ability to calculate two normalized vegetation indices that highlight cacti. Furthermore, we explore the impacts of spatial resolution by presenting spectral signatures from cacti samples taken with a handheld field spectroradiometer, drone-based hyperspectral sensor, and aerial hyperspectral sensor. These cacti indices will help measure baseline levels of cacti around the world and examine changes due to climate, disturbance, and management influences.
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MESSINGER, DAVID W., CARL SALVAGGIO y NATALIE M. SINISGALLI. "DETECTION OF GASEOUS EFFLUENTS FROM AIRBORNE LWIR HYPERSPECTRAL IMAGERY USING PHYSICS-BASED SIGNATURES". International Journal of High Speed Electronics and Systems 17, n.º 04 (diciembre de 2007): 801–12. http://dx.doi.org/10.1142/s0129156407004990.

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Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and the concentration of the gas. All of these quantities vary spatially further complicating the detection problem. In complex scenes simple estimation of a “residual” spectrum may not be possible due to the variability in the scene background. A common detection scheme uses a matched filter formalism to compare laboratory-measured gas absorption spectra with measured pixel radiances. This methodology can not account for the variable signature strengths due to concentration path length and temperature contrast, nor does it take into account measured signatures that are observed in both absorption and emission in the same scene. We have developed a physics-based, forward model to predict in-scene signatures covering a wide range in gas / surface properties. This target space is reduced to a set of basis vectors using a geometrical model of the space. Corresponding background basis vectors are derived to describe the non-plume pixels in the image. A Generalized Likelihood Ratio Test is then used to discriminate between plume and non-plume pixels. Several species can be tested for iteratively. The algorithm is applied to airborne LWIR hyperspectral imagery collected by the Airborne Hyperspectral Imager (AHI) over a chemical facility with some ground truth. When compared to results from a clutter matched filter the physics-based signature approach shows significantly improved performance for the data set considered here.
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Lebedev, L. I., Yu V. Yasakov, T. H. Utesheva, V. P. Gromov, A. V. Borusjak y V. E. Turlapov. "Complex analysis and monitoring of the environment based on earth sensing data". Computer Optics 43, n.º 2 (abril de 2019): 282–95. http://dx.doi.org/10.18287/2412-6179-2019-43-2-282-295.

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We study a problem of complex analysis and monitoring of the environment based on Earth Sensing Data, with the emphasis on the use of hyperspectral images (HSI), and propose a solution based on developing algorithmic procedures for HSI processing and storage. HSI is considered as a two-dimensional field of pixel signatures. Methods are proposed for evaluating the similarity of a HSI pixel signature with a reference signature, via simple alignment transformations: identical; amplitude scaling; shift along y-axis; and a combination of the last two. A clustering / recognition method with self-learning is proposed, which determines values of the transformation parameters that ensure the alignment of the current pixel signature with the reference signature. Similarity with the reference is determined by a standard deviation value. A HSI compression method with controlled losses has been proposed. The method forms a basis via accumulating reference signatures and represents the rest of the signatures by parameters matching them with the already detected class-reference signature. In an experiment with the GSI f100520t01p00-12 data of the AVIRIS spectrometer, the method provided a 2 % loss and compression coefficients of the original HSI ranging from 43 to 165 for various types of alignment transformation, while not requiring archiving and thus maintaining active access to the HSI and using the list of references as an analogue of the HSI palette. An algorithm for the formation of dense groups of detectable objects (for example, oil spots) and their nonconvex contouring, controlled by 4 parameters, is proposed. A pilot version of the concept of geographic information system (GIS) and an appropriate database management system (DBMS) was built and implemented, which provides monitoring and is based on the prioritized processing and storage of the HSI, which serve as a data source for the system. A laboratory complex with new algorithms for processing and storing the GSE is introduced into the structure of the system.
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Jamaludin, Muhammad Ikhwan, Abdul Nasir Matori, Mohammad Faize Kholik y Munirah Mohd Mokhtar. "Development Spectral Library of Vegetation Stress for Hydrocarbon Seepage". Applied Mechanics and Materials 567 (junio de 2014): 693–98. http://dx.doi.org/10.4028/www.scientific.net/amm.567.693.

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Spectral Library is data archive of spectral signatures of various natural and man-made materials. In oil and gas industry, spectral library might not be heard often, but these tools can pose a great help for future oil and gas exploration. A developing spectral library for hydrocarbon is basically a new advancement in this field, and this project may implement the spectral library on global hydrocarbon seeps in the future. In this paper, the procedure in the developing spectral library from vegetation stress was demonstrated. In order to obtain these spectral signatures of hydrocarbon, the usage of hyperspectral remote sensing analysis and spectroradiometer is required. But for the early stage of development, spectrophotometer with the range up to 740 ηm was used to extract the spectral signature of the plants in term of green percentage. Nine samples of palm oil trees represent the stressed vegetation was planted with 10% and 40% porosity of crude oil and control samples having three palm oil trees for each of those. The vegetation has been left in an open environment with enough sunlight and watered daily. The significance result of changes in green percentage in the spectral signature of the trees with different porosity level was compared to the control samples. It shows that the existence of crude oil influences the health of the vegetation which has been notified through the spectral signature of the plant. This paper signifies a first step towards the development of spectral signature of hydrocarbon for oil and gas exploration.
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Licciardi, Giorgio, Costantino Del Gaudio y Jocelyn Chanussot. "Non-Linear Spectral Unmixing for the Estimation of the Distribution of Graphene Oxide Deposition on 3D Printed Composites". Applied Sciences 10, n.º 21 (3 de noviembre de 2020): 7792. http://dx.doi.org/10.3390/app10217792.

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Hyperspectral analysis is a well-established technique that can be suitably implemented in several application fields, including materials science. This approach allows us to deal with data samples containing spatial and spectral information at very high resolution, thus enabling us to evaluate materials properties at a nanoscale level. As a proof of concept, hyperspectral imaging was here considered to investigate 3D printed polymer matrix composites, considering graphene oxide (GO) as a nanofiller. Commercial polycaprolactone and polylactic acid filaments were firstly treated with GO to be then printed into testing specimens. Raman analysis was performed to assess the GO distribution on samples surface by mapping different regions of interest and the collected data were the input of a custom-made algorithm for hyperspectral image analysis, tailored to detect the GO signature. Findings showed a valuable matching to Raman maps and were also characterized by the positive feature of avoiding to set specific conditions to perform the investigation as GO Raman distribution was carried out by fixing the wavenumber at 1580 cm−1, which is representative of the G band of the nanofiller. This occurrence might lead to an uneven intensity representation related to possible peak shifts which can bias the acquired results. Differently, hyperspectral imaging needs a minimal set of data input, i.e., the spectral signatures of neat materials, to directly identify the searched nanomaterial. More in-depth investigations need to be performed to fully validate the proposed approach, but the here presented results already show the potential and versatility of hyperspectral analysis in the materials science field.
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Ma, Pengfei, Jiaoli Li, Ying Zhuo, Pu Jiao y Genda Chen. "Coating Condition Detection and Assessment on the Steel Girder of a Bridge through Hyperspectral Imaging". Coatings 13, n.º 6 (29 de mayo de 2023): 1008. http://dx.doi.org/10.3390/coatings13061008.

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The organic coating of bridge steel girders is subjected to physical scratches, corrosion, and aging in natural weathering. The breakdown of the coating may cause serviceability and safety problems if left unnoticed. Conventional coating inspection is time-consuming and lacks information about the coating’s chemical integrity. A hyperspectral imaging method is proposed to detect the condition of steel coatings based on coating-responsive features in reflectance spectra. A field test was conducted on the real-world bridge, which shows obvious signs of degradation. The hyperspectral signature enables an assessment of the coating’s health and defect severity. The results indicated that the coating scratch can be effectively located in the domain of a hyperspectral image and the scratch depth can be determined by mapping a scratch depth indicator (SDI = R532 nm/R641 nm). Rust sources and products in steel girders can be identified by the unique spectral signatures in the VNIR range, and the rust stains (and thus stain areas) scattered on the coating can be pinpointed at pixel level by the chloride rust (CR) indicators >1.11 (CR = R733 nm/R841 nm). The chemical integrity of a topcoat is demonstrated by the short-wave infrared spectroscopy and the topcoat degradation can be evaluated by the decreased absorption at 8000 cm−1 and 5850 cm−1. Hyperspectral imaging enables faster and more reliable coating condition detection by the spectral features and provides an alternative for multi-object coating detection.
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Srinivas, Umamahesh, Yi Chen, Vishal Monga, Nasser Nasrabadi y Trac Tran. "Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models". Geoscience and Remote Sensing Letters, IEEE 10, n.º 3 (noviembre de 2012): 505–9. http://dx.doi.org/10.1109/lgrs.2012.2211858.

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A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra from an overcomplete dictionary. A spatiospectral notion of sparsity is further captured by developing a joint sparsity model, wherein spectral signatures of pixels in a local spatial neighborhood (of the pixel of interest) are constrained to be represented by a common collection of training spectra, albeit with different weights. A challenging open problem is to effectively capture the class conditional correlations between these multiple sparse representations corresponding to different pixels in the spatial neighborhood. We propose a probabilistic graphical model framework to explicitly mine the conditional dependences between these distinct sparse features. Our graphical models are synthesized using simple tree structures which can be discriminatively learnt (even with limited training samples) for classification. Experiments on benchmark HSI data sets reveal significant improvements over existing approaches in classification rates as well as robustness to choice of training.
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SCHAUM, A. "ADVANCED HYPERSPECTRAL ALGORITHMS FOR TACTICAL TARGET DETECTION AND DISCRIMINATION". International Journal of High Speed Electronics and Systems 18, n.º 03 (septiembre de 2008): 531–44. http://dx.doi.org/10.1142/s0129156408005540.

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Region-of-interest cueing by hyperspectral imaging systems for tactical reconnaissance has emphasized wide area coverage, low false alarm rates, and the search for manmade objects. Because they often appear embedded in complex environments and can exhibit large intrinsic spectral variability, these targets usually cannot be characterized by consistent signatures that might facilitate the detection process. Template matching techniques that focus on distinctive and persistent absorption features, such as those characterizing gases or liquids, prove ineffectual for most hard-body targets. High-performance autonomous detection requires instead the integration of limited and uncertain signature knowledge with a statistical approach. Effective techniques devised in this way using Gaussian models have transitioned to fielded systems. These first-generation algorithms are described here, along with heuristic modifications that have proven beneficial. Higher-performance Gaussian-based algorithms are also described, but sensitivity to parameter selection can prove problematical. Finally, a next-generation parameter-free non-Gaussian method is outlined whose performance compares favorably with the best Gaussian methods.
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Tesis sobre el tema "Signature hyperspectrale"

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Hemissi, Selim. "Modélisation multidimensionnelle de signature spectrale pour le démixage et la classification en imagerie hyperspectrale multi-temporelle". Télécom Bretagne, 2014. http://www.theses.fr/2014TELB0307.

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L'imagerie hyperspectrale constitue une technologie de pointe assez fructueuse pour une cartographie précise de la surface terrestre. En analysant les données, la plupart des approches classiques traitent chaque date indépendamment, sans considérer l'entremêlement temporel omniprésent dans la formation des signatures spectrales. Inéluctablement, les types hétérogènes d'occupation du sol manifestent des signatures spectrales chevauchantes à cause de la variabilité inter/intra saisonnière des propriétés spectrales. Pour y pallier, nous essayons de repenser l'hypothèse d'unicité de la signature spectrale et nous soulignons l'importance d'incorporer la dimension temporelle dans une modélisation plus sophistiquée. En effet, nous proposons dans cette thèse des nouvelles méthodes pour la classification et le démixage spectral des séries temporelles d'images hyperspectrales. Dans un premier temps, l'intégration de la dimension temporelle dans le modèle classique de la signature spectrale est envisagée en utilisant la reconstruction de Delaunay. Cette unification nous a permis de proposer un modèle multi-temporel 3D incorporant les facettes spectrale, temporelle et spatiale des objets. Ensuite, nous nous préoccupons de la mise en oeuvre d'une nouvelle version des bases de signatures spectrales en proposant un schéma conceptuel approprié. Nous avons également étudié des techniques d'apprentissage actif pour la sélection des descripteurs les plus pertinents. De la sorte, l'approche proposée s'inspire de l'algorithme RankBoost pour essayer d'établir le meilleur choix des descripteurs les plus influents. Dans la deuxième partie de la thèse, nous nous focalisons sur la problématique de démixage spectral dans un cadre multi-temporel en essayant de dégager les enjeux d'une analyse fine des composants. Subséquemment, nous développons deux approches, la première adopte une modélisation matricielle tandis que la deuxième étend ce modèle en utilisant le cadre théorique de l'algèbre multi-linéaire. Également, nous considérons les possibilités de résolution du problème de démixage spectral en adoptant une optimisation sous contraintes. Finalement, et dans l'ambition de réduire les effets de l'imperfection des corpus d'apprentissage sur le processus classificatoire, nous proposons une version évidentielle de l'analyse discriminante de Fisher. Les méthodes proposées dans cette thèse améliorent les résultats de classification par rapport aux méthodes classiques et dévoilent, ainsi, un potentiel appréciable pour divers scénarios d'interprétation des séries d'images
Hyperspectral imaging transcribes each specific spectrum of the received energy from a material in a specific pixel of the image. Since heterogeneous land occupation types exhibit different spectral signatures, hyperspectral imaging can be considered as an effective technology for precise image classification. Nevertheless, the temporal variability of spectral signatures complicates the image analysis task due to the interlacement of spectral properties of different land occupation types throughout the year. Standard classification approaches treat each date separately whereas recent research has proven that modelling hyperspectral images incorporating time dimension is crucial. In this dissertation, we propose new methods and algorithms for the classification of time series of hyperspectral images. Our first contribution in the inclusion the temporal dimension into the classical model of spectral signature using the Delaunay reconstruction. This investigation allows us to develop a 3D multi-temporal model of spectral signatures incorporating spectral, temporal and spatial facets of objects. Indeed, we have proposed a new set of spectral signatures based on the above-mentioned model and have developed an appropriate conceptual schema. The database of satellite images is supported by a hierarchical indexing model using Kohonen's Self Organizing Feature Maps. We also studied boosting learning techniques for the selection of the most relevant features. This proposal is based on the Rankboost algorithm. Our second contribution is tackling the problem of mixed pixels in hyperspectral imagery for time series images. Indeed, for the extraction of multi-temporal endmembers, we developed two approaches: a matrix-based approach and a tensor-based approach which has its roots in the multilinear algebra. Moreover, for the purpose of the classification of non-linearly separable data and modelling imperfect data, we used the Fisher discriminant analysis and the Dempster-Shafer theory, respectively. We also proposed a new classification algorithm that is an evidential extension of the discriminant analysis. Our third contribution consists in modelling the spectral unmixing problem as a constrained optimization problem. Experimental results show that the new methods and algorithms proposed in our work improve the classification results compared to standard methods, and thus reveal a real potential for various scenarios of image sequences interpretation
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Al, Hayek Marianne. "Modélisation optique de signatures spectrales et polarimétriques d'objets pour augmenter les performances d'un système de reconnaissance". Electronic Thesis or Diss., Brest, 2023. http://www.theses.fr/2023BRES0101.

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L’imagerie conventionnelle, qui se limite aux formes et couleurs des objets, montre ses limites en matière de reconnaissance. Pour améliorer les performances des systèmes d’imagerie, l’imagerie hyperspectrale et polarimétrique apporte une richesse d’informations, notamment des grandeurs physiques difficiles à obtenir autrement. Cela permet d’améliorer la détection, la caractérisation quantitative et la classification des objets. Cependant, le traitement des données complexes de ces modalités reste un défi. L’objectif de ce travail est de proposer une méthodologie générique pour analyser les signaux optiques, en se concentrant sur l’imagerie hyperspectrale (HSI) en premier terme. Une classification originale des modèles hyperspectraux inversibles basés sur la physique est présentée, avec description des modèles variés les plus récents pour des applications diverses : MPBOM pour le biofilm d’algues et de bactéries, MARMIT pour le sol, PROSPECT pour les feuilles de plantes, Farrell pour les tissus biologiques turbides, Schmitt pour la peau humaine et Hapke pour les objets du système solaire. Une convergence entre les modèles PROSPECT et Farrell pour des objets intermédiaires (pomme verte et poireau) ouvrant la voie au développement d’une nouvelle modélisation générique et complète. Notamment dans le domaine de la biologie, par une collaboration avec le laboratoire de l’ANSES, nous avons procédé à une détection précoce suivie d’une quantification du biofilm qui se forme dans les bassins d’élevage de poissons en utilisant l’imagerie hyperspectrale et polarimétrique du fait que sa détection actuelle est visuelle et n’est pas assez efficace pour prévenir son accumulation et pour mettre en place des procédures de nettoyage et de désinfection. Ainsi une première version d’une modélisation physique propre nommée "DNA-HSI" a été mise en place
Conventional imaging, limited to object shapes and colors, faces limitations in object recognition. To enhance imaging system performance, hyperspectral and polarimetric imaging provides a wealth of information, includingchallenging-to-obtain physical parameters. This facilitates improved object detection, quantitative characterization, and classification. However, the processing of complex data from these modalities remains a challenge. The aim of this work is to propose a generic methodology for the analysis of optical signals, with a primary focus on hyperspectral imaging (HSI). An original classification of invertible physics-based hyperspectral models is presented, along with descriptions of recent diverse models for various applications: MPBOM for algae and bacteria biofilm, MARMIT for soil, PROSPECT for plant leaves, Farrell for turbid biological tissues, Schmitt for human skin, and Hapke for objects in the solar system. A convergence between the PROSPECT and Farrell models for intermediate objects (green apple and leek) paves the way for the development of a new generic and comprehensive modeling approach.Particularly in the field of biology, in collaboration with the ANSES laboratory, we conducted early detection ollowed by quantification of biofilms forming in fish farming basins using hyperspectral and polarimetric imaging. This is crucial as the current visual detection method is not efficient in preventing biofilm accumulation and implementingcleaning and disinfection procedures. Hence, an initial version of a dedicated physical modeling approach called "DNA-HSI" has been established
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Mathur, Abhinav. "DIMENSIONALITY REDUCTION OF HYPERSPECTRAL SIGNATURES FOR OPTIMIZED DETECTION OF INVASIVE SPECIES". MSSTATE, 2003. http://sun.library.msstate.edu/ETD-db/theses/available/etd-07112003-160125/.

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The aim of this thesis is to investigate the use of hyperspectral reflectance signals for the discrimination of cogongrass (Imperata cylindrica) from other subtly different vegetation species. Receiver operating characteristics (ROC) curves are used to determine which spectral bands should be considered as candidate features. Multivariate statistical analysis is then applied to the candidate features to determine the optimum subset of spectral bands. Linear discriminant analysis (LDA) is used to compute the optimum linear combination of the selected subset to be used as a feature for classification. Similarly, for comparison purposes, ROC analysis, multivariate statistical analysis, and LDA are utilized to determine the most advantageous discrete wavelet coefficients for classification. The overall system was applied to hyperspectral signatures collected with a handheld spectroradiometer (ASD) and to simulated satellite signatures (Hyperion). A leave-one-out testing of a nearest mean classifier for the ASD data shows that cogongrass can be detected amongst various other grasses with an accuracy as high as 87.86% using just the pure spectral bands and with an accuracy of 92.77% using the Haar wavelet decomposition coefficients. Similarly, the Hyperion signatures resulted in classification accuracies of 92.20% using just the pure spectral bands and with an accuracy of 96.82% using the Haar wavelet decomposition coefficients. These results show that hyperspectral reflectance signals can be used to reliably detect cogongrass from subtly different vegetation.
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4

Sirois, Jean-Philippe. "Impact et suivi de la variabilité climatique sur la production viticole dans le sud du Québec à l’aide de la télédétection hyperspectrale". Mémoire, Université de Sherbrooke, 2015. http://hdl.handle.net/11143/6011.

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Au Québec, la viticulture commerciale ou artisanale n’a que 35 ans. Cependant, le Québec est soumis à de nombreuses pressions climatiques comme la présence de gel hâtif à l’automne et tardif au printemps. La période de croissance (la différence entre le dernier gel au printemps et le premier gel à l’automne) est donc très limitée par la présence du froid. Dans un contexte de réchauffement climatique, cette période de croissance sera portée à s’allonger. Une plus longue période de croissance pourrait inciter les vignerons à modifier leurs cépages ou à augmenter la superficie cultivée. Trois vignobles ont fait l’objet d’étude et des prises de mesures spectrales des ceps y ont été effectuées. Des indices climatiques appliqués à la viticulture y ont été calculés sur une période de 30 ans avec les données du NARR et validés avec les données des stations météorologiques d’Environnement Canada. La moyenne de certains indices permet de les comparer à ceux des régions viticoles renommées comme Bordeaux et Dijon en France. L’étude des indices climatiques des 30 dernières années puis des 10 dernières années permet de découvrir qu’il existe un réel potentiel pour l’implantation de cépages nobles dans le sud du Québec. Ainsi, on remarque que l’énergie thermique est suffisante pour faire la culture de ces cépages. Cependant, la période sans gel est très variable et vient tempérer ces résultats. L’analyse des signatures spectrales de données de réalité de terrain permet de faire une différenciation entre les cépages en fonction des étapes de développement et de la vitesse d’adaptation des ceps aux pratiques culturales et au climat. Les longueurs d’onde entre 720-740 nm (proche infrarouge) et 550 nm (vert) sont les plus touchées par le changement. L’analyse dérivative permet d’éliminer les facteurs d’éclairement. De plus, il est possible de rehausser les différences dans les longueurs d’onde du pic de réflectance de la chlorophylle (≈720 nm). Avec toutes ses informations, il devient possible d’identifier les principaux cépages dans les vignobles grâce à des mesures spectrales temporelles. L’utilisation d’une image hyperspectrale et de données de réalité de terrain ont permis de différencier les cépages et d’en faire l’évolution phénologique entre deux saisons de croissance. Ainsi, avec l’extraction des signatures des pixels d’un secteur n’ayant pas subi de changement physiologique majeur (secteur de vieux ceps), la signature spectrale mesurée par le capteur est comparable à celles des données de réalité de terrain. L’analyse a permis de confirmer que l’énergie thermique acquise pour le 9 juillet 2009 (422 ∘C) est comparable à celle du 27 juin 2011 (419 ∘C). L’énergie thermique cumulée à ces deux dates suppose un développement comparable des cépages. Les similarités dans les signatures spectrales reflètent ce développement comparable.
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Rousseau, Sylvain. "Détection de points d'intérêts dans une image multi ou hyperspectral par acquisition compressée". Thesis, Poitiers, 2013. http://www.theses.fr/2013POIT2269/document.

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Les capteurs multi- et hyper-spectraux génèrent un énorme flot de données. Un moyende contourner cette difficulté est de pratiquer une acquisition compressée de l'objet multi- ethyper-spectral. Les données sont alors directement compressées et l'objet est reconstruitlorsqu'on en a besoin. L'étape suivante consiste à éviter cette reconstruction et à travaillerdirectement avec les données compressées pour réaliser un traitement classique sur un objetde cette nature. Après avoir introduit une première approche qui utilise des outils riemannienspour effectuer une détection de contours dans une image multispectrale, nous présentonsles principes de l'acquisition compressée et différents algorithmes utilisés pour résoudre lesproblèmes qu'elle pose. Ensuite, nous consacrons un chapitre entier à l'étude détaillée de l'und'entre eux, les algorithmes de type Bregman qui, par leur flexibilité et leur efficacité vontnous permettre de résoudre les minimisations rencontrées plus tard. On s'intéresse ensuiteà la détection de signatures dans une image multispectrale et plus particulièrement à unalgorithme original du Guo et Osher reposant sur une minimisation L1. Cet algorithme estgénéralisé dans le cadre de l'acquisition compressée. Une seconde généralisation va permettrede réaliser de la détection de motifs dans une image multispectrale. Et enfin, nous introduironsde nouvelles matrices de mesures qui simplifie énormément les calculs tout en gardant debonnes qualités de mesures
Multi- and hyper-spectral sensors generate a huge stream of data. A way around thisproblem is to use a compressive acquisition of the multi- and hyper-spectral object. Theobject is then reconstructed when needed. The next step is to avoid this reconstruction and towork directly with compressed data to achieve a conventional treatment on an object of thisnature. After introducing a first approach using Riemannian tools to perform edge detectionin multispectral image, we present the principles of the compressive sensing and algorithmsused to solve its problems. Then we devote an entire chapter to the detailed study of one ofthem, Bregman type algorithms which by their flexibility and efficiency will allow us to solvethe minimization encountered later. We then focuses on the detection of signatures in amultispectral image relying on an original algorithm of Guo and Osher based on minimizingL1. This algorithm is generalized in connection with the acquisition compressed. A secondgeneralization will help us to achieve the pattern detection in a multispectral image. Andfinally, we introduce new matrices of measures that greatly simplifies calculations whilemaintaining a good quality of measurements
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TENG, Chih-Heng y 鄧至亨. "A spectral signature based non-local mean for hyperspectral image denoising". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/5wtabh.

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碩士
國立臺灣大學
電信工程學研究所
106
A new spectral signature method for hyperspectral images denoising named as hyperspectral non-local mean is proposed in this thesis. This method uses spectral information and spatial information to denoise hyperspectral images. Traditionally, spectral information and spatial information are used separately. Thus, there are two different groups of methods to denoise hyperspectral images, spatial algorithms and spectral algorithms. The spatial denoising methods such as smoothing filter, non-local mean and non-local Bayesian consider the correlation in an image. The spectral denoising methods such as PCA (Principal component analysis), HySime (Hyperspectral subspace identification by minimum error) and MNF (Minimum noise fraction) consider the correlation in spectral. Hyperspectral non-local mean takes the advantages of these two groups of algorithms and processes spectral information and spatial information in the same time. Our contributions are 1) reduction of the processing complexity of algorithm. 2) choice of the proper algorithm parameters according to the properties of hyperspectral images. 3) combination and comparison with state-of-the-art.
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Feng, Siwei. "Wavelet-Based Non-Homogeneous Hidden Markov Chain Model For Hyperspectral Signature Classification". 2015. https://scholarworks.umass.edu/masters_theses_2/145.

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Hyperspectral signature classification is a kind of quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from corresponding hyperspectral signatures containing information like signature energy or shape. In this paper, we describe a technique that applies non-homogeneous hidden Markov chain (NHMC) models to hyperspectral signature classification. The basic idea is to use statistical models (NHMC models) to characterize wavelet coefficients which capture the spectrum structural information at multiple levels. Experimental results show that the approach based on NHMC models outperforms existing approaches relevant in classification tasks.
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Hoffman, Forrest McCoy. "Analysis of reflected spectral signatures and detection of geophysical disturbance using hyperspectral imagery". 2004. http://etd.utk.edu/2004/HoffmanForrest.pdf.

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Thesis (M.S.)--University of Tennessee, Knoxville, 2004.
Title from title page screen (viewed Jan. 14, 2005). Thesis advisor: William E. Blass. Document formatted into pages (xi, 197 p. : ill. (some col.), maps)). Vita. Includes bibliographical references (p. 81-85).
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HSIEH, MINGCHE y 謝明哲. "Study on the Modeling and Classification of the Mixed Pixel Analysis on Vegetation Hyperspectral Signatures". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/63526429667807224219.

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碩士
國立嘉義大學
森林暨自然資源學系研究所
99
Accurate measurement and characterization of fluctuations in the remote sensing data from satellite, airborne or in situ measurement. The adjacency effect increases the reflection of the target pixel from nearby pixels and path scattering. When substances with different spectral properties in the same pixel within the time, there will be mixed pixel. Mixed pixel is not entirely belong to a particular surface features, in order to make image classification more precise, It is necessary to divide into a variety of features in the percentage of pixel. There are many mathematic models and atmospheric correction methods, which could remove the adjacency effect from the satellite and airborne imaginary, but few discusses are made about the influence of adjacency effect on field spectroscopy, especially the variable come from the measure distance, which means the size of the target pixel, and furthermore. As long as the measure distance increases, it may cause the path scattering unpolarized reflectance come from nearby pixels. Owing to the atmosphere and solar irradiance change varyingly in outdoor measurements, the research is indoor test under artificial light source to reduce the effect of uncertainties by measuring the reflectance of light energy from spectroradiometer. We evaluate the influence of pixel sizes on the adjacency effect from different background canopy density and selective absorption by polarizer. In this study, we discuss the contribution from differerent ratio of the vegetation and soil spectral reflectance and spectral characteristics, and the use of polarized lens that filter polarized light outside the pixal to find out the contribution to spectral reflectance, the results show a quadratic function can model its response mode.
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Libros sobre el tema "Signature hyperspectrale"

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1968-, Rajendran S., ed. Hyperspectral remote sensing & spectral signature applications. New Delhi: New India Pub. Agency, 2009.

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Ponder, Henley J. y U.S. Army Engineer Topographic Laboratories., eds. Hyperspectral signatures (400 to 2500 nm) of vegetation, minerals, soils, rocks, and cultural features: Laboratory and field measurements. Fort Belvoir, Va: U.S. Army Corps of Engineers, Engineer Topographic Laboratories, 1990.

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Capítulos de libros sobre el tema "Signature hyperspectrale"

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Chang, Chein-I. "Target Signature-Constrained Mixed Pixel Classification (TSCMPC): LCMV Classifiers". En Hyperspectral Imaging, 207–27. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_11.

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Chang, Chein-I. "Target Signature-Constrained Subpixel Detection: Linearly Constrained Minimum Variance (LCMV)". En Hyperspectral Imaging, 51–71. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_4.

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Chang, Chein-I. "Target Signature-Constrained Mixed Pixel Classification (TSCMPC): Linearly Constrained Discriminant Analysis (LCDA)". En Hyperspectral Imaging, 229–42. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_12.

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Patil, Trunal, Claudia Pagano, Roberto Marani, Tiziana D’Orazio, Giacomo Copani y Irene Fassi. "Hyperspectral Imaging for Non-destructive Testing of Composite Materials and Defect Classification". En Lecture Notes in Mechanical Engineering, 404–12. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_39.

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AbstractCarbon fiber composite materials are intensively used in many manufacturing domains such as aerospace, aviation, marine, automation and civil industries due to their excellent strength, corrosion resistance, and lightweight properties. However, their increased use requires a conscious awareness of their entire life cycle and not only of their manufacturing. Therefore, to reduce waste and increase sustainability, reparation, reuse, or recycling are recommended in case of defects and wear. This can be largely improved with reliable and efficient non-destructive defect detection techniques; those are able to identify damages automatically for quality control inspection, supporting the definition of the best circular economy options. Hyperspectral imaging techniques provide unique features for detecting physical and chemical alterations of any material and, in this study, it is proposed to identify the constitutive material and classify local defects of composite specimens. A Middle Wave Infrared Hyperspectral Imaging (MWIR-HSI) system, able to capture spectral signatures of the specimen surfaces in a range of wavelengths between 2.6757 and 5.5056 µm, has been used. The resulting signatures feed a deep neural network with three convolutional layers that filter the input and isolate data-driven features of high significance. A complete experimental case study is presented to validate the methodology, leading to an average classification accuracy of 93.72%. This opens new potential opportunities to enable sustainable life cycle strategies for carbon fiber composite materials.
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Leshem, Guy y Menachem Domb. "Face Authentication Using Image Signature Generated from Hyperspectral Inner Images". En Advances in Intelligent Systems and Computing, 113–25. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0637-6_9.

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Turra, Giovanni, Simone Arrigoni y Alberto Signoroni. "CNN-Based Identification of Hyperspectral Bacterial Signatures for Digital Microbiology". En Image Analysis and Processing - ICIAP 2017, 500–510. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68548-9_46.

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Carmona-Zuluaga, Pablo, Maria C. Torres-Madronero, Manuel Goez, Tatiana Rondon, Manuel Guzman y Maria Casamitjana. "Abiotic Maize Stress Detection Using Hyperspectral Signatures and Band Selection". En Smart Technologies, Systems and Applications, 480–93. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32213-6_35.

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Appice, Annalisa y Pietro Guccione. "Exploiting Spatial Correlation of Spectral Signature for Training Data Selection in Hyperspectral Image Classification". En Discovery Science, 295–309. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46307-0_19.

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"Binary Coding for Spectral Signatures". En Hyperspectral Data Processing, 719–40. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118269787.ch24.

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"Vector Coding for Hyperspectral Signatures". En Hyperspectral Data Processing, 741–71. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118269787.ch25.

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Actas de conferencias sobre el tema "Signature hyperspectrale"

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Settouti, Nesma, Olga Assainova, Nadine Abdallah Saab y Marwa El Bouz. "Automated Hyperspectral Apple Variety Identification Based on Patch-wise Classification". En Applied Industrial Spectroscopy. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/ais.2023.jw2a.28.

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Manual apple sorting is costly and subjective. We investigate using VNIR hyperspectral imaging for an efficient and objective solution. Our study presents a patch-wise classification approach for automatic recognition of apple varieties using their hyperspectral signature.
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Bárta, Vojtech y František Racek. "Hyperspectral discrimination of camouflaged target". En Target and Background Signatures, editado por Karin U. Stein y Ric Schleijpen. SPIE, 2017. http://dx.doi.org/10.1117/12.2278578.

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Bárta, Vojtech, František Racek y Jaroslav Krejcí. "NATO hyperspectral measurement of natural background". En Target and Background Signatures, editado por Karin U. Stein y Ric Schleijpen. SPIE, 2018. http://dx.doi.org/10.1117/12.2325468.

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Cropper, A. D., David C. Mann y Milton O. Smith. "Target detection performance of hyperspectral imagers". En Target and Background Signatures V, editado por Karin U. Stein y Ric Schleijpen. SPIE, 2019. http://dx.doi.org/10.1117/12.2532406.

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Amann, Simon, Mazen Mel, Pietro Zanuttigh, Tobias Haist, Markus Kamm y Alexander Gatto. "Material Characterization using a Compact Computed Tomography Imaging Spectrometer with Super-resolution Capability". En OCM 2023 - 6th International Conference on Optical Characterization of Materials, March 22nd – 23rd, 2023, Karlsruhe, Germany : Conference Proceedings. KIT Scientific Publishing, 2023. http://dx.doi.org/10.58895/ksp/1000155014-13.

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Computed Tomography Imaging Spectrometer (CTIS) systems are snapshot hyperspectral imaging devices capable of capturing dense spectra of static as well as dynamic scenes. A three-dimensional hyperspectral cube is smeared across the spatial dimension via Diffractive Optical Element (DOE) and projected across multiple angles forming a two-dimensional compressed sensor image. In this paper we demonstrate material characterization and classification capability of a compact CTIS system leveraging spectral signatures. Then we propose an approach to simultaneously reconstruct and segment into regions corresponding to different materials hyperspectral images with enhanced spatial resolution from CTIS sensor measurements.
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Gross, Wolfgang, Florian Queck, Simon Schreiner, Marius Vögtli, Jannick Kuester, Jonas Mispelhorn, Mathias Kneubühler y Wolfgang Middelmann. "A multi-temporal hyperspectral camouflage detection and transparency experiment". En Target and Background Signatures VIII, editado por Karin Stein y Ric Schleijpen. SPIE, 2022. http://dx.doi.org/10.1117/12.2636132.

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Ito, Takaaki, Daiki Nakaya, Shin Satori, Mitsuharu Shiwa y Tomonori Ito. "Detection technology of foreign matter on the ocean for MDA with hyperspectral imaging". En Target and Background Signatures, editado por Karin U. Stein y Ric Schleijpen. SPIE, 2018. http://dx.doi.org/10.1117/12.2324647.

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Gross, Wolfgang, Florian Queck, Marius Vögtli, Simon Schreiner, Jannick Kuester, Jonas Böhler, Jonas Mispelhorn, Mathias Kneubühler y Wolfgang Middelmann. "A multi-temporal hyperspectral target detection experiment: evaluation of military setups". En Target and Background Signatures VII, editado por Karin U. Stein y Ric Schleijpen. SPIE, 2021. http://dx.doi.org/10.1117/12.2597991.

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Morgan, Seldon O., Richard B. Gomez y William E. Roper. "Squeezed signature analysis hyperspectral classification". En AeroSense 2003, editado por Nickolas L. Faust y William E. Roper. SPIE, 2003. http://dx.doi.org/10.1117/12.502414.

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Shah, Dharambhai, Y. N. Trivedi y Tanish Zaveri. "Non-Linear Spectral Unmixing: A Case Study On Mangalore Aviris-Ng Hyperspectral Data". En 2020 IEEE Bombay Section Signature Conference (IBSSC). IEEE, 2020. http://dx.doi.org/10.1109/ibssc51096.2020.9332215.

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Informes sobre el tema "Signature hyperspectrale"

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Chang, Chein-I., Jing Wang, Chein-Chi Chang y Chinsu Lin. Progressive Coding for Hyperspectral Signature Characterization. Fort Belvoir, VA: Defense Technical Information Center, enero de 2006. http://dx.doi.org/10.21236/ada455705.

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Pokrzywinski, Kaytee, Cliff Morgan, Scott Bourne, Molly Reif, Kenneth Matheson y Shea Hammond. A novel laboratory method for the detection and identification of cyanobacteria using hyperspectral imaging : hyperspectral imaging for cyanobacteria detection. Engineer Research and Development Center (U.S.), junio de 2021. http://dx.doi.org/10.21079/11681/40966.

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To assist US Army Corps of Engineers resource managers in monitoring for cyanobacteria bloom events, a laboratory method using hyperspectral imaging has been developed. This method enables the rapid detection of cyanobacteria in large volumes and has the potential to be transitioned to aerial platforms for field deployment. Prior to field data collection, validation of the technology in the laboratory using monocultures was needed. This report describes the development of the detection method using hyperspectral imaging and the stability/reliability of these signatures for identification purposes. Hyperspectral signatures of different cyanobacteria were compared to evaluate spectral deviations between genera to assess the feasibility of using this imaging method in the field. Algorithms were then developed to spectrally deconvolute mixtures of cyanobacteria to determine relative abundances of each species. Last, laboratory cultures of Microcystis aeruginosa and Anabaena sp. were subjected to varying macro (nitrate and phosphate) and micro-nutrient (iron and magnesium) stressors to establish the stability of signatures within each species. Based on the findings, hyperspectral imaging can be a valuable tool for the detection and monitoring of cyanobacteria. However, it should be used with caution and only during stages of active growth for accurate identification and limited interference owing to stress.
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White, H. P., L. Sun, K. Staenz, R. A. Fernandes y C. Champagne. Determining the Contribution of Shaded Elements of a Canopy to Remotely Sensed Hyperspectral Signatures. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219961.

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Lesser, Michael P. Coastal Benthic Optical Properties (CoBOP) of Coral Reef Environments: Small Scale Fluorescent Optical Signatures and Hyperspectral Remote Sensing of Coral Reef Habitats. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2001. http://dx.doi.org/10.21236/ada627969.

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Lesser, Michael P. Coastal Benthic Optical Properties (CoBOP) of Coral Reef Environments: Small Scale Fluorescent Optical Signatures and Hyperspectral Remote Sensing of Coral Reef Habitats. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2002. http://dx.doi.org/10.21236/ada628422.

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Budkewitsch, P., K. Staenz, J. Secker, A. Rencz y D. Sangster. Spectral Signatures of Carbonate Rocks Surrounding the Nanisivik MVT Zn-Pb Mine and Implications of Hyperspectral Imaging for Exploration in Arctic Environments. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219736.

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Hodul, M., H. P. White y A. Knudby. A report on water quality monitoring in Quesnel Lake, British Columbia, subsequent to the Mount Polley tailings dam spill, using optical satellite imagery. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330556.

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In the early morning on the 4th of August 2014, a tailings dam near Quesnel, BC burst, spilling approximately 25 million m3 of runoff containing heavy metal elements into nearby Quesnel Lake (Byrne et al. 2018). The runoff slurry, which included lead, arsenic, selenium, and vanadium spilled through Hazeltine Creek, scouring its banks and picking up till and forest cover on the way, and ultimately ended up in Quesnel Lake, whose water level rose by 1.5 m as a result. While the introduction of heavy metals into Quesnel Lake was of environmental concern, the additional till and forest cover scoured from the banks of Hazeltine Creek added to the lake has also been of concern to salmon spawning grounds. Immediate repercussions of the spill involved the damage of sensitive environments along the banks and on the lake bed, the closing of the seasonal salmon fishery in the lake, and a change in the microbial composition of the lake bed (Hatam et al. 2019). In addition, there appears to be a seasonal resuspension of the tailings sediment due to thermal cycling of the water and surface winds (Hamilton et al. 2020). While the water quality of Quesnel Lake continues to be monitored for the tailings sediments, primarily by members at the Quesnel River Research Centre, the sample-and-test methods of water quality testing used, while highly accurate, are expensive to undertake, and not spatially exhaustive. The use of remote sensing techniques, though not as accurate as lab testing, allows for the relatively fast creation of expansive water quality maps using sensors mounted on boats, planes, and satellites (Ritchie et al. 2003). The most common method for the remote sensing of surface water quality is through the use of a physics-based semianalytical model which simulates light passing through a water column with a given set of Inherent Optical Properties (IOPs), developed by Lee et al. (1998) and commonly referred to as a Radiative Transfer Model (RTM). The RTM forward-models a wide range of water-leaving spectral signatures based on IOPs determined by a mix of water constituents, including natural materials and pollutants. Remote sensing imagery is then used to invert the model by finding the modelled water spectrum which most closely resembles that seen in the imagery (Brando et al 2009). This project set out to develop an RTM water quality model to monitor the water quality in Quesnel Lake, allowing for the entire surface of the lake to be mapped at once, in an effort to easily determine the timing and extent of resuspension events, as well as potentially investigate greening events reported by locals. The project intended to use a combination of multispectral imagery (Landsat-8 and Sentinel-2), as well as hyperspectral imagery (DESIS), combined with field calibration/validation of the resulting models. The project began in the Autumn before the COVID pandemic, with plans to undertake a comprehensive fieldwork campaign to gather model calibration data in the summer of 2020. Since a province-wide travel shutdown and social distancing procedures made it difficult to carry out water quality surveying in a small boat, an insufficient amount of fieldwork was conducted to suit the needs of the project. Thus, the project has been put on hold, and the primary researcher has moved to a different project. This document stands as a report on all of the work conducted up to April 2021, intended largely as an instructional document for researchers who may wish to continue the work once fieldwork may freely and safely resume. This research was undertaken at the University of Ottawa, with supporting funding provided by the Earth Observations for Cumulative Effects (EO4CE) Program Work Package 10b: Site Monitoring and Remediation, Canada Centre for Remote Sensing, through the Natural Resources Canada Research Affiliate Program (RAP).
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