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Добірка наукової літератури з теми "Hyperspectral signature"

Автор: Grafiati

Опубліковано: 25 травня 2024

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Статті в журналах з теми "Hyperspectral signature"

Papp, Adam, Julian Pegoraro, Daniel Bauer, Philip Taupe, Christoph Wiesmeyr, and 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, no. 13 (July 1, 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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Wang, Jing. "Progressive coding for hyperspectral signature characterization." Optical Engineering 45, no. 9 (September 1, 2006): 097002. http://dx.doi.org/10.1117/1.2353113.

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Gromov, V. P., L. I. Lebedev, and V. E. Turlapov. "Analysis and object markup of hyperspectral images for machine learning methods." Information Technology and Nanotechnology, no. 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, and 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, no. 6 (May 26, 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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Chang, Chein-I., Sumit Chakravarty, Hsian-Min Chen, and Yen-Chieh Ouyang. "Spectral derivative feature coding for hyperspectral signature analysis." Pattern Recognition 42, no. 3 (March 2009): 395–408. http://dx.doi.org/10.1016/j.patcog.2008.07.016.

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Kim, Sungho, Jungho Kim, Jinyong Lee, and Junmo Ahn. "AS-CRI: A New Metric of FTIR-Based Apparent Spectral-Contrast Radiant Intensity for Remote Thermal Signature Analysis." Remote Sensing 11, no. 7 (April 1, 2019): 777. http://dx.doi.org/10.3390/rs11070777.

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Infrared signature analysis that considers both the target and background is fundamentally important to the development of target detection systems as well as in the design of ships for thermal stealth. This paper presents the analysis results of long-term infrared signature variations in terms of the apparent spectral-contrast radiant intensity measured using Fourier transform infrared (FTIR)-based hyperspectral images. A novel apparent spectral-contrast radiant intensity (AS-CRI) measure is proposed to evaluate the spectral infrared signature accurately at the sensor point of view. The spectral information by AS-CRI can provide the optimal band for either target detection or thermal stealth purposes, considering the background and atmospheric transmittance. In addition, the effects of seasonal and weather variations were analyzed from the long-term hyperspectral image database constructed during 2018.01–2018.08 (three times a day). A TELOPS HYPER-CAM MWE camera was adopted to acquire 374 bands in 1.5–5.5 μm. The automatic weather system (AWS) can provide 24 h weather recordings for the signature evaluation. The experimental results validate the utility of the novel AS-CRI method to find spectral bands for a range of infrared signature applications including small infrared target detection.

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MESSINGER, DAVID W., CARL SALVAGGIO, and 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, no. 04 (December 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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Honkavaara, E., T. Hakala, O. Nevalainen, N. Viljanen, T. Rosnell, E. Khoramshahi, R. Näsi, R. Oliveira, and A. Tommaselli. "GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 77–82. http://dx.doi.org/10.5194/isprs-archives-xli-b7-77-2016.

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Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.

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Miljković, V., and D. Gajski. "ADAPTATION OF INDUSTRIAL HYPERSPECTRAL LINE SCANNER FOR ARCHAEOLOGICAL APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 343–45. http://dx.doi.org/10.5194/isprs-archives-xli-b5-343-2016.

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The spectral characteristic of the visible light reflected from any of archaeological artefact is the result of the interaction of its surface illuminated by incident light. Every particular surface depends on what material it is made of and/or which layers put on it has its spectral signature. Recent archaeometry recognises this information as very valuable data to extend present documentation of artefacts and as a new source for scientific exploration. However, the problem is having an appropriate hyperspectral imaging system available and adopted for applications in archaeology. In this paper, we present the new construction of the hyperspectral imaging system, made of industrial hyperspectral line scanner ImSpector V9 and CCD-sensor PixelView. The hyperspectral line scanner is calibrated geometrically, and hyperspectral data are geocoded and converted to the hyperspectral cube. The system abilities are evaluated for various archaeological artefacts made of different materials. Our experience in applications, visualisations, and interpretations of collected hyperspectral data are explored and presented.

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Більше джерел

Дисертації з теми "Hyperspectral signature"

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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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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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, and 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, and 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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Книги з теми "Hyperspectral signature"

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

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Ponder, Henley J., and 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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Частини книг з теми "Hyperspectral signature"

Chang, Chein-I. "Target Signature-Constrained Mixed Pixel Classification (TSCMPC): LCMV Classifiers." In 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)." In 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)." In 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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Leshem, Guy, and Menachem Domb. "Face Authentication Using Image Signature Generated from Hyperspectral Inner Images." In 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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Appice, Annalisa, and Pietro Guccione. "Exploiting Spatial Correlation of Spectral Signature for Training Data Selection in Hyperspectral Image Classification." In 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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Patil, Trunal, Claudia Pagano, Roberto Marani, Tiziana D’Orazio, Giacomo Copani, and Irene Fassi. "Hyperspectral Imaging for Non-destructive Testing of Composite Materials and Defect Classification." In 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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Turra, Giovanni, Simone Arrigoni, and Alberto Signoroni. "CNN-Based Identification of Hyperspectral Bacterial Signatures for Digital Microbiology." In 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, and Maria Casamitjana. "Abiotic Maize Stress Detection Using Hyperspectral Signatures and Band Selection." In 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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"Binary Coding for Spectral Signatures." In 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." In 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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Тези доповідей конференцій з теми "Hyperspectral signature"

Morgan, Seldon O., Richard B. Gomez, and William E. Roper. "Squeezed signature analysis hyperspectral classification." In AeroSense 2003, edited by Nickolas L. Faust and William E. Roper. SPIE, 2003. http://dx.doi.org/10.1117/12.502414.

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Cathcart, J. Michael, Robert V. Worrall, and Daniel P. Cash. "Hyperspectral signature modeling for terrain backgrounds." In Defense and Security Symposium, edited by Wendell R. Watkins and Dieter Clement. SPIE, 2006. http://dx.doi.org/10.1117/12.666478.

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Vyas, Saurabh, Amit Banerjee, Luis Garza, Sewon Kang, and Philippe Burlina. "Hyperspectral signature analysis of skin parameters." In SPIE Medical Imaging, edited by Carol L. Novak and Stephen Aylward. SPIE, 2013. http://dx.doi.org/10.1117/12.2001428.

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Knaeps, Els, and Mehrdad Moshtaghi. "Evaluating the hyperspectral signature of marine plastics." In Hyperspectral Imaging and Sounding of the Environment. Washington, D.C.: OSA, 2021. http://dx.doi.org/10.1364/hise.2021.htu2c.5.

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Settouti, Nesma, Olga Assainova, Nadine Abdallah Saab, and Marwa El Bouz. "Automated Hyperspectral Apple Variety Identification Based on Patch-wise Classification." In 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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Ozdemir, Okan Bilge, Hilal Soydan, Yasemin Yardimci Cetin, and H. Sebnem Duzgun. "Signature based vegetation detection on hyperspectral images." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7130392.

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Chakravarty, Sumit, and Chein-I. Chang. "Block truncation signature coding for hyperspectral analysis." In Optical Engineering + Applications, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2008. http://dx.doi.org/10.1117/12.796711.

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Pereira, Wellesley, David Less, Leonard Rodriguez, Allen Curran, Uri Bernstein, and Yit-Tsi Kwan. "Hyperspectral extensions in the MuSES signature code." In SPIE Defense and Security Symposium, edited by Dawn A. Trevisani. SPIE, 2008. http://dx.doi.org/10.1117/12.783933.

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

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Seyfioglu, Mehmet Saygin, Seyma Bayindir, and Sevgi Zubeyde Gurbuz. "Automatic spectral signature extraction for hyperspectral target detection." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326815.

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Звіти організацій з теми "Hyperspectral signature"

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

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Pokrzywinski, Kaytee, Cliff Morgan, Scott Bourne, Molly Reif, Kenneth Matheson, and 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.), June 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, and 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, September 2001. http://dx.doi.org/10.21236/ada627969.

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Budkewitsch, P., K. Staenz, J. Secker, A. Rencz, and 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, and 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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