Готові списки джерел за темами / Technologies hyperspectrales

Добірка наукової літератури з теми "Technologies hyperspectrales"

Автор: Grafiati

Опубліковано: 28 вересня 2022

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Статті в журналах з теми "Technologies hyperspectrales":

Lefèvre-Fonollosa, Marie-José, Sylvain Michel, and Steven Hosford. "HYPXIM — An innovative spectroimager for science, security, and defence requirements." Revue Française de Photogrammétrie et de Télédétection, no. 200 (April 19, 2014): 20–27. http://dx.doi.org/10.52638/rfpt.2012.58.

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Cet article présente un aperçu des applications et des besoins de données hyperspectrales recueillis par un groupe adhoc d'une vingtaine de scientifiques français et d'utilisateurs Civil et de la Défense (i.a. dual). Ce groupe connu sous l'acronyme GHS (Groupe de Synthèse en Hyperspectral) a défini les exigences techniques pour une mission spatiale de haute résolution en hyperspectral répondant aux besoins des thèmes suivants: la végétation naturelle et agricole, les écosystèmes aquatiques côtiers et lacustres, les géosciences, l'environnement urbain, l'atmosphère, la sécurité et la défense.La synthèse de ces exigences a permis de décrire les spécifications d'un satellite très innovant en terme de domaine spectral, de résolution spectrale, de rapport signal à bruit, de résolution spatiale, de fauchée et de répétitivité. HYPXIM est une mission hyperspectrale spatiale de nouvelle génération qui répond aux besoins d'une large communauté d'utilisateurs de données à haute résolution dans le monde.Les principaux points ont été étudiés dans la phase 0 (pré-phase A) menée par le CNES avec ses partenaires industriels (EADS-Astrium et Thales Alenia Space). Deux concepts de satellites ont été étudiés et comparés. Le premier, appelé HYPXIM-C, vise à obtenir le niveau de résolution le plus élevé possible (15 m) réalisable en utilisant une plateforme de microsatellite. Les objectifs du deuxième, appelé HYPXIM-P, sont d'atteindre une résolution spatiale supérieure d'un facteur deux en hyperspectral (7-8m), un canal panchromatique (2m) et de fournir une capacité en infrarouge hyperspectral (100 m) sur un mini satellite. La phase A HYPXIM a été récemment décidée. Elle démarre en 2012 en se concentrant sur le concept le plus performant. Le défi pour la mission HYPXIM qui a été sélectionnée est de concevoir un spectroimageur à haute résolution spatiale, sur un mini-satellite agile à moindre coût.Ces études préliminaires ouvrent des perspectives pour un lancement possible en 2020/21 en fonction du développement des technologies critiques.

Stuart, Mary B., Leigh R. Stanger, Matthew J. Hobbs, Tom D. Pering, Daniel Thio, Andrew J. S. McGonigle, and Jon R. Willmott. "Low-Cost Hyperspectral Imaging System: Design and Testing for Laboratory-Based Environmental Applications." Sensors 20, no. 11 (June 9, 2020): 3293. http://dx.doi.org/10.3390/s20113293.

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The recent surge in the development of low-cost, miniaturised technologies provides a significant opportunity to develop miniaturised hyperspectral imagers at a fraction of the cost of currently available commercial set-ups. This article introduces a low-cost laboratory-based hyperspectral imager developed using commercially available components. The imager is capable of quantitative and qualitative hyperspectral measurements, and it was tested in a variety of laboratory-based environmental applications where it demonstrated its ability to collect data that correlates well with existing datasets. In its current format, the imager is an accurate laboratory measurement tool, with significant potential for ongoing future developments. It represents an initial development in accessible hyperspectral technologies, providing a robust basis for future improvements.

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/isprsarchives-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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Zhang, Ning, Guijun Yang, Yuchun Pan, Xiaodong Yang, Liping Chen, and Chunjiang Zhao. "A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades." Remote Sensing 12, no. 19 (September 29, 2020): 3188. http://dx.doi.org/10.3390/rs12193188.

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The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

Chang, Chein-I., Meiping Song, Junping Zhang, and Chao-Cheng Wu. "Editorial for Special Issue “Hyperspectral Imaging and Applications”." Remote Sensing 11, no. 17 (August 27, 2019): 2012. http://dx.doi.org/10.3390/rs11172012.

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Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue “Hyperspectral Imaging and Applications” is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories, Data Unmixing, Spectral variability, Target Detection, Hyperspectral Image Classification, Band Selection, Data Fusion, Applications.

LeVan, Paul D. "Space-based hyperspectral technologies for the thermal infrared." Optical Engineering 52, no. 6 (March 4, 2013): 061311. http://dx.doi.org/10.1117/1.oe.52.6.061311.

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Allik, Toomas H., Roberta E. Dixon, Lenard V. Ramboyong, Mark Roberts, Thomas J. Soyka, George Trifon, and Lori Medley. "Novel Electro-Optic Imaging Technologies for Day/Night Oil Spill Detection." International Oil Spill Conference Proceedings 2014, no. 1 (May 1, 2014): 299609. http://dx.doi.org/10.7901/2169-3358-2014-1-299609.1.

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Joint program between the U.S. Departments of the Interior and Defense to bring knowledge, expertise and military, low-light level and hyperspectral imaging technologies to remote oil spill detection. Program emphasis is to determine remote infrared imaging techniques for the quantification of oil spill thickness. Spectral characteristics of various crude oils in the SWIR (1–2 microns), MWIR (3–5 microns) and LWIR (8–12 microns) were measured. Analysis of laboratory data and Deepwater Horizon hyperspectral imagery showed the utility of the SWIR region to detect crude oil and emulsions. We have evaluated two SWIR wavelengths (1200 nm and 1250 nm) for thickness assessment. An infrared, 3-color imager is discussed along with field tests at the BSEE's Ohmsett test facility.

Hu, B., J. Li, J. Wang, and B. Hall. "The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2 (November 11, 2014): 213–19. http://dx.doi.org/10.5194/isprsarchives-xl-2-213-2014.

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The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, individual trees were first extracted and then classified into different species based on their spectral information derived from hyperspectral imagery, spatial information from VHR imagery, and for each ash tree its health state and EAB infestation stage were determined based on hyperspectral imagery. The developed framework and methods were demonstrated to be effective according to the results obtained on two study sites in the city of Toronto, Ontario Canada. The individual tree delineation method provided satisfactory results with an overall accuracy of 78 % and 19 % commission and 23 % omission errors when used on the combined very high-spatial resolution imagery and LiDAR data. In terms of the identification of ash trees, given sufficient representative training data, our classification model was able to predict tree species with above 75 % overall accuracy, and mis-classification occurred mainly between ash and maple trees. The hypothesis that a strong correlation exists between general tree stress and EAB infestation was confirmed. Vegetation indices sensitive to leaf chlorophyll content derived from hyperspectral imagery can be used to predict the EAB infestation levels for each ash tree.

Wu, Zebin, Jinping Gu, Yonglong Li, Fu Xiao, Jin Sun, and Zhihui Wei. "Distributed Parallel Endmember Extraction of Hyperspectral Data Based on Spark." Scientific Programming 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3252148.

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Due to the increasing dimensionality and volume of remotely sensed hyperspectral data, the development of acceleration techniques for massive hyperspectral image analysis approaches is a very important challenge. Cloud computing offers many possibilities of distributed processing of hyperspectral datasets. This paper proposes a novel distributed parallel endmember extraction method based on iterative error analysis that utilizes cloud computing principles to efficiently process massive hyperspectral data. The proposed method takes advantage of technologies including MapReduce programming model, Hadoop Distributed File System (HDFS), and Apache Spark to realize distributed parallel implementation for hyperspectral endmember extraction, which significantly accelerates the computation of hyperspectral processing and provides high throughput access to large hyperspectral data. The experimental results, which are obtained by extracting endmembers of hyperspectral datasets on a cloud computing platform built on a cluster, demonstrate the effectiveness and computational efficiency of the proposed method.

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Дисертації з теми "Technologies hyperspectrales":

Polat, Songül. "Combined use of 3D and hyperspectral data for environmental applications." Thesis, Lyon, 2021. http://www.theses.fr/2021LYSES049.

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La demande sans cesse croissante de solutions permettant de décrire notre environnement et les ressources qu'il contient nécessite des technologies qui permettent une description efficace et complète, conduisant à une meilleure compréhension du contenu. Les technologies optiques, la combinaison de ces technologies et un traitement efficace sont cruciaux dans ce contexte. Cette thèse se concentre sur les technologies 3D et les technologies hyper-spectrales (HSI). Tandis que les technologies 3D aident à comprendre les scènes de manière plus détaillée en utilisant des informations géométriques, topologiques et de profondeur, les développements rapides de l'imagerie hyper-spectrale ouvrent de nouvelles possibilités pour mieux comprendre les aspects physiques des matériaux et des scènes dans un large éventail d'applications grâce à leurs hautes résolutions spatiales et spectrales. Les travaux de recherches de cette thèse visent à l'utilisation combinée des données 3D et hyper-spectrales. Ils visent également à démontrer le potentiel et la valeur ajoutée d'une approche combinée dans le contexte de différentes applications. Une attention particulière est accordée à l'identification et à l'extraction de caractéristiques dans les deux domaines et à l'utilisation de ces caractéristiques pour détecter des objets d'intérêt.Plus spécifiquement, nous proposons différentes approches pour combiner les données 3D et hyper-spectrales en fonction des technologies 3D et d’imagerie hyper-spectrale (HSI) utilisées et montrons comment chaque capteur peut compenser les faiblesses de l'autre. De plus, une nouvelle méthode basée sur des critères de forme dédiés à la classification de signatures spectrales et des règles de décision liés à l'analyse des signatures spectrales a été développée et présentée. Les forces et les faiblesses de cette méthode par rapport aux approches existantes sont discutées. Les expérimentations réalisées, dans le domaine du patrimoine culturel et du tri de déchets plastiques et électroniques, démontrent que la performance et l’efficacité de la méthode proposée sont supérieures à celles des méthodes de machines à vecteurs de support (SVM).En outre, une nouvelle méthode d'analyse basée sur les caractéristiques 3D et hyper-spectrales est présentée. L'évaluation de cette méthode est basée sur un exemple pratique du domaine des déchet d'équipements électriques et électroniques (WEEE) et se concentre sur la séparation de matériaux comme les plastiques, les carte à circuit imprimé (PCB) et les composants électroniques sur PCB. Les résultats obtenus confirment qu'une amélioration des ré-sultats de classification a pu être obtenue par rapport aux méthodes proposées précédemment.L’avantage des méthodes et processus individuels développés dans cette thèse est qu’ils peuvent être transposé directement à tout autre domaine d'application que ceux investigué, et généralisé à d’autres cas d’étude sans adaptation préalable
Ever-increasing demands for solutions that describe our environment and the resources it contains, require technologies that support efficient and comprehensive description, leading to a better content-understanding. Optical technologies, the combination of these technologies and effective processing are crucial in this context. The focus of this thesis lies on 3D scanning and hyperspectral technologies. Rapid developments in hyperspectral imaging are opening up new possibilities for better understanding the physical aspects of materials and scenes in a wide range of applications due to their high spatial and spectral resolutions, while 3D technologies help to understand scenes in a more detailed way by using geometrical, topological and depth information. The investigations of this thesis aim at the combined use of 3D and hyperspectral data and demonstrates the potential and added value of a combined approach by means of different applications. Special focus is given to the identification and extraction of features in both domains and the use of these features to detect objects of interest. More specifically, we propose different approaches to combine 3D and hyperspectral data depending on the HSI/3D technologies used and show how each sensor could compensate the weaknesses of the other. Furthermore, a new shape and rule-based method for the analysis of spectral signatures was developed and presented. The strengths and weaknesses compared to existing approach-es are discussed and the outperformance compared to SVM methods are demonstrated on the basis of practical findings from the field of cultural heritage and waste management.Additionally, a newly developed analytical method based on 3D and hyperspectral characteristics is presented. The evaluation of this methodology is based on a practical exam-ple from the field of WEEE and focuses on the separation of materials like plastics, PCBs and electronic components on PCBs. The results obtained confirms that an improvement of classification results could be achieved compared to previously proposed methods.The claim of the individual methods and processes developed in this thesis is general validity and simple transferability to any field of application

Cheng, Xuemei. "Hyperspectral imaging and pattern recognition technologies for real time fruit safety and quality inspection." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/2154.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.
Thesis research directed by: Biological Resources Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

(5930357), Michelle A. Visbal Onufrak. "Virtual Hyperspectral Imaging Toward Data-Driven mHealth." Thesis, 2020.

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Hyperspectral imaging is widely used for obtaining optical information of light absorbers (e.g. biochemical composition) in a variety of specimens or tissues in a label-free manner. Acquiring and processing spectral data using hyperspectral imaging usually requires advanced instrumentation such as spectrometers, spectrographs or tunable color filters, which are not easily adaptable in developing instrumentation for field-based applications. Also, use of only RGB information from conventional cameras is not sufficient to obtain a reliable correlation with the actual content of the analyte of interest. We propose a new concept of ‘virtual hyperspectral imaging’ to reconstruct the full reflectance spectra from RGB image data. This allows us to use only RGB image data to determine detailed spatial distributions of analytes of interest. More importantly, it simplifies instrumentation without requiring bulky and expensive hardware. Using a data-driven approach, we apply multivariate regression to reconstruct hyperspectral reflectance image data from RGB images obtained using a conventional camera or a smartphone.

In developing a reliable reconstruction matrix, it is critical to obtain a training data set of the specimen of study under the same optical geometry since the spectral reflectance and absorbance is sensitive to the detection and illumination parameters. We designed an image-guided hyperspectral system that can acquire both hyperspectral reflectance and RGB data sets under the same imaging configuration to minimize any discrepancies in the hyperspectral reflectance data acquired using different optical sensing geometries. In our technology development, a telecentric lens that is commonly used in machine vision systems but rarely in bioimaging, serves as a key component for reducing unwanted scattering in biological tissue due to its highly anisotropic scattering properties, by acting as a back-directional gating component to suppress diffuse light. We evaluate our spectrometer-less reflectance imaging method using RGB-based hyperspectral reconstruction algorithm for integration into a smartphone application for non-invasive hemoglobin analysis for anemia risk assessment in communities with limited access to central laboratory tests.

Mananze, Sosdito Estevão. "Statistical and physically based hyperspectral and multispectral reflectance modelling for agricultural monitoring: a case study in Vilankulo, Mozambique." Tese, 2020. https://hdl.handle.net/10216/127322.

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He, Jin. "Urban Detection From Hyperspectral Images Using Dimension-Reduction Model and Fusion of Multiple Segmentations Based on Stuctural and Textural Features." Thèse, 2013. http://hdl.handle.net/1866/10281.

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Ce mémoire de maîtrise présente une nouvelle approche non supervisée pour détecter et segmenter les régions urbaines dans les images hyperspectrales. La méthode proposée n ́ecessite trois étapes. Tout d’abord, afin de réduire le coût calculatoire de notre algorithme, une image couleur du contenu spectral est estimée. A cette fin, une étape de réduction de dimensionalité non-linéaire, basée sur deux critères complémentaires mais contradictoires de bonne visualisation; à savoir la précision et le contraste, est réalisée pour l’affichage couleur de chaque image hyperspectrale. Ensuite, pour discriminer les régions urbaines des régions non urbaines, la seconde étape consiste à extraire quelques caractéristiques discriminantes (et complémentaires) sur cette image hyperspectrale couleur. A cette fin, nous avons extrait une série de paramètres discriminants pour décrire les caractéristiques d’une zone urbaine, principalement composée d’objets manufacturés de formes simples g ́eométriques et régulières. Nous avons utilisé des caractéristiques texturales basées sur les niveaux de gris, la magnitude du gradient ou des paramètres issus de la matrice de co-occurrence combinés avec des caractéristiques structurelles basées sur l’orientation locale du gradient de l’image et la détection locale de segments de droites. Afin de réduire encore la complexité de calcul de notre approche et éviter le problème de la ”malédiction de la dimensionnalité” quand on décide de regrouper des données de dimensions élevées, nous avons décidé de classifier individuellement, dans la dernière étape, chaque caractéristique texturale ou structurelle avec une simple procédure de K-moyennes et ensuite de combiner ces segmentations grossières, obtenues à faible coût, avec un modèle efficace de fusion de cartes de segmentations. Les expérimentations données dans ce rapport montrent que cette stratégie est efficace visuellement et se compare favorablement aux autres méthodes de détection et segmentation de zones urbaines à partir d’images hyperspectrales.
This master’s thesis presents a new approach to urban area detection and segmentation in hyperspectral images. The proposed method relies on a three-step procedure. First, in order to decrease the computational complexity, an informative three-colour composite image, minimizing as much as possible the loss of information of the spectral content, is computed. To this end, a non-linear dimensionality reduction step, based on two complementary but contradictory criteria of good visualization, namely accuracy and contrast, is achieved for the colour display of each hyperspectral image. In order to discriminate between urban and non-urban areas, the second step consists of extracting some complementary and discriminant features on the resulting (three-band) colour hyperspectral image. To attain this goal, we have extracted a set of features relevant to the description of different aspects of urban areas, which are mainly composed of man-made objects with regular or simple geometrical shapes. We have used simple textural features based on grey-levels, gradient magnitude or grey-level co-occurence matrix statistical parameters combined with structural features based on gradient orientation, and straight segment detection. In order to also reduce the computational complexity and to avoid the so-called “curse of dimensionality” when clustering high-dimensional data, we decided, in the final third step, to classify each individual feature (by a simple K-means clustering procedure) and to combine these multiple low-cost and rough image segmentation results with an efficient fusion model of segmentation maps. The experiments reported in this report demonstrate that the proposed segmentation method is efficient in terms of visual evaluation and performs well compared to existing and automatic detection and segmentation methods of urban areas from hyperspectral images.

Книги з теми "Technologies hyperspectrales":

Shen, Sylvia S. Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XIV: 17-19 March 2008, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2008.

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Shen, Sylvia S. Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XIV: 17-19 March 2008, Orlando, Florida, USA. Edited by Society of Photo-optical Instrumentation Engineers. Bellingham, Wash: SPIE, 2008.

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Shen, Sylvia S. Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XV: 13-16 April 2009, Orlando, Florida, United States. Edited by SPIE (Society). Bellingham, Wash: SPIE, 2009.

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Shen, Sylvia S., and Paul E. Lewis. Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XVI: 5-8 April 2010, Orlando, Florida, United States. Bellingham, Wash: SPIE, 2010.

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Shen, Sylvia S., and Paul E. Lewis. Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XVII: 25-28 April 2011, Orlando, Florida, United States. Edited by SPIE (Society). Bellingham, Wash: SPIE, 2011.

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Velez-Reyes, Miguel, and Fred Kruse. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX. SPIE, 2014.

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Velez-Reyes, Miguel, and David Messinger. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII. SPIE, 2018.

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SPIE. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI. SPIE, 2015.

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Bhunia, Arun K., Moon S. Kim, and Chris R. Taitt. High Throughput Screening for Food Safety Assessment: Biosensor Technologies, Hyperspectral Imaging and Practical Applications. Elsevier Science & Technology, 2014.

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Частини книг з теми "Technologies hyperspectrales":

Li, Jindong. "Design and Analysis of Hyperspectral Remote Sensing Satellite System." In Space Science and Technologies, 175–226. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4871-0_5.

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Xing, Huimin, Haikuan Feng, Jingying Fu, Xingang Xu, and Guijun Yang. "Development and Application of Hyperspectral Remote Sensing." In Computer and Computing Technologies in Agriculture XI, 271–82. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06179-1_28.

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Xiu, Shiyong, Feng Gao, and Yong Chen. "Residual Multi-resolution Network for Hyperspectral Image Denoising." In Image and Graphics Technologies and Applications, 3–9. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7189-0_1.

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Amorim, Paulo, Thiago Moraes, Jorge Silva, and Helio Pedrini. "Adaptive Filtering Techniques for Improving Hyperspectral Image Classification." In New Advances in Information Systems and Technologies, 889–98. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31232-3_84.

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Das, Jintu Kumar, Christopher D. Tholou, Alok Anand Minz, and Sonia Sarmah. "A Graph-Based Band Selection Method for Hyperspectral Images Using Correlation Matrix." In Emerging Technologies for Smart Cities, 119–26. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1550-4_13.

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Chen, Jiayu, Honghui Chen, Xiaodong Wang, Chunhua Yu, Cheng Wang, and Dazhou Zhu. "The Characteristic of Hyperspectral Image of Wheat Seeds during Sprouting." In Computer and Computing Technologies in Agriculture VII, 408–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54344-9_47.

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Liu, Jianglong, Shujuan Zhang, Haixia Sun, and Zhiming Wu. "Detection of Defects in Malus asiatica Nakai Using Hyperspectral Imaging." In Computer and Computing Technologies in Agriculture X, 111–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06155-5_11.

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Jianwen, Wang, Li Zhenhai, Xu Xingang, Zhu Hongchun, Feng Haikuan, Liu Chang, Gan Ping, and Xu Xiaobin. "New NNI Model in Winter Wheat Based on Hyperspectral Index." In Computer and Computing Technologies in Agriculture XI, 154–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06179-1_16.

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Liu, Chang, Guijun Yang, Zhenhai Li, Fuquan Tang, Haikuan Feng, Jianwen Wang, Chunlan Zhang, and Liyan Zhang. "Monitoring of Winter Wheat Biomass Using UAV Hyperspectral Texture Features." In Computer and Computing Technologies in Agriculture XI, 241–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06179-1_25.

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Xue, Long. "Application of IDL and ENVI Redevelopment in Hyperspectral Image Preprocessing." In Computer and Computing Technologies in Agriculture IV, 403–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18369-0_47.

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Тези доповідей конференцій з теми "Technologies hyperspectrales":

Sturm, Barbara, Roberto Moscetti, S. O. J. Crichton, Sharvari Raut, Michael Bantle, and Riccardo Massantini. "Feasibility of Vis/NIR spectroscopy and image analysis as basis of the development of smart-drying technologies." In 21st International Drying Symposium. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/ids2018.2018.7616.

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Drying is a complex, dynamic, unsteady and nonlinear process that, when not optimized on a system level, may be responsible for (1) significant quality degradation and (2) energy wastage. Consequently, new drying technologies must be designed combining non-invasive at-/on-/in-line advanced measurement and control systems with models cross-linking all relevant aspects of product quality changes and heat and mass transfer phenomena. This paper presents preliminary results on the use of RGB imaging, NIR spectroscopy and Vis-NIR hyperspectral imaging for real-time monitoring of physicochemical changes of apples and carrots during drying. Keywords: chemometrics, artificial intelligence, deep learning

LeVan, Paul D. "Space-based hyperspectral technologies for the thermal infrared." In SPIE Defense, Security, and Sensing, edited by Bjørn F. Andresen, Gabor F. Fulop, and Paul R. Norton. SPIE, 2012. http://dx.doi.org/10.1117/12.919715.

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Nevalainen, O., E. Honkavaara, T. Hakala, Sanna Kaasalainen, N. Viljanen, T. Rosnell, E. Khoramshahi, and R. Näsi. "Close-range environmental remote sensing with 3D hyperspectral technologies." In SPIE Remote Sensing, edited by Ulrich Michel, Karsten Schulz, Manfred Ehlers, Konstantinos G. Nikolakopoulos, and Daniel Civco. SPIE, 2016. http://dx.doi.org/10.1117/12.2240936.

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Cobb, Joshua M., Lovell E. Comstock, Paul G. Dewa, Mike M. Dunn, and Scott D. Flint. "Innovative manufacturing and test technologies for imaging hyperspectral spectrometers." In Defense and Security Symposium, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2006. http://dx.doi.org/10.1117/12.665889.

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Zakrzewski, Jay, and Kevin Didona. "Advances in hyperspectral imaging technologies for multichannel fiber sensing." In SPIE Defense, Security, and Sensing, edited by Eric Udd, Henry H. Du, and Anbo Wang. SPIE, 2009. http://dx.doi.org/10.1117/12.818261.

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Cancio, Leopoldo C. "Application of novel hyperspectral imaging technologies in combat casualty care." In MOEMS-MEMS, edited by Michael R. Douglass and Larry J. Hornbeck. SPIE, 2010. http://dx.doi.org/10.1117/12.846331.

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Sang, B., J. Schubert, S. Kaiser, V. Mogulsky, C. Neumann, K. P. Förster, S. Hofer, et al. "The EnMAP hyperspectral imaging spectrometer: instrument concept, calibration, and technologies." In Optical Engineering + Applications, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2008. http://dx.doi.org/10.1117/12.794870.

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Velez-Reyes, Miguel. "Modeling and Applications of Hyperspectral Imaging." In SPIE Future Sensing Technologies, edited by Christopher R. Valenta, Joseph A. Shaw, and Masafumi Kimata. SPIE, 2020. http://dx.doi.org/10.1117/12.2583745.

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Strese, Helene, P. Ribes-Pleguezuelo, A. Zuccaro Marchi, and L. Maresi. "Hyperspectral innovation on instruments and technologies at the European Space Agency." In International Conference on Space Optics — ICSO 2021, edited by Zoran Sodnik, Bruno Cugny, and Nikos Karafolas. SPIE, 2021. http://dx.doi.org/10.1117/12.2600234.

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Shou, Jingwen, and Yasuyuki Ozeki. "Dual-polarization hyperspectral stimulated Raman scattering microscopy." In Advanced Optical Imaging Technologies, edited by Xiao-Cong Yuan, Kebin Shi, and Michael G. Somekh. SPIE, 2018. http://dx.doi.org/10.1117/12.2500598.

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Звіти організацій з теми "Technologies hyperspectrales":

Watson, Nik, Ahmed Rady, Crispin Coombs, Alicia Parkes, Rob Mos, and Ashkan Ajeer. 21st Century Meat Inspector – Project Report. Food Standards Agency, April 2022. http://dx.doi.org/10.46756/sci.fsa.hup976.

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Poultry is the most widely consumed meat in the UK, and its effective inspection within processing facilities is essential to ensure regulatory compliance. Poultry inspection is performed manually and is extremely challenging due to the short time available to inspect each bird and the sustained level of concentration required. The project focused specifically on post-mortem inspection of poultry, adopting a benefits realisation approach to determine the requirements for any new technologies and ensure that business benefits are delivered to all stakeholders within the poultry chain. This interdisciplinary project included expertise in a variety of complimentary inspection technologies; optical (visual, Near-Infrared, Infrared, Hyperspectral), X-ray and Ultrasonic and IT-enabled benefits realisation management with the Hartree Centre (STFC), a food business operator (referred to throughout as Food Co.) and CSB as project partners.

Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.

Bonfil, David J., Daniel S. Long, and Yafit Cohen. Remote Sensing of Crop Physiological Parameters for Improved Nitrogen Management in Semi-Arid Wheat Production Systems. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7696531.bard.

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To reduce financial risk and N losses to the environment, fertilization methods are needed that improve NUE and increase the quality of wheat. In the literature, ample attention is given to grid-based and zone-based soil testing to determine the soil N available early in the growing season. Plus, information is available on in-season N topdressing applications as a means of improving GPC. However, the vast majority of research has focused on wheat that is grown under N limiting conditions in sub-humid regions and irrigated fields. Less attention has been given to wheat in dryland that is water limited. The objectives of this study were to: (1) determine accuracy in determining GPC of HRSW in Israel and SWWW in Oregon using on-combine optical sensors under field conditions; (2) develop a quantitative relationship between image spectral reflectance and effective crop physiological parameters; (3) develop an operational precision N management procedure that combines variable-rate N recommendations at planting as derived from maps of grain yield, GPC, and test weight; and at mid-season as derived from quantitative relationships, remote sensing, and the DSS; and (4) address the economic and technology-transfer aspects of producers’ needs. Results from the research suggest that optical sensing and the DSS can be used for estimating the N status of dryland wheat and deciding whether additional N is needed to improve GPC. Significant findings include: 1. In-line NIR reflectance spectroscopy can be used to rapidly and accurately (SEP <5.0 mg g⁻¹) measure GPC of a grain stream conveyed by an auger. 2. On-combine NIR spectroscopy can be used to accurately estimate (R² < 0.88) grain test weight across fields. 3. Precision N management based on N removal increases GPC, grain yield, and profitability in rainfed wheat. 4. Hyperspectral SI and partial least squares (PLS) models have excellent potential for estimation of biomass, and water and N contents of wheat. 5. A novel heading index can be used to monitor spike emergence of wheat with classification accuracy between 53 and 83%. 6. Index MCARI/MTVI2 promises to improve remote sensing of wheat N status where water- not soil N fertility, is the main driver of plant growth. Important features include: (a) computable from commercial aerospace imagery that include the red edge waveband, (b) sensitive to Chl and resistant to variation in crop biomass, and (c) accommodates variation in soil reflectance. Findings #1 and #2 above enable growers to further implement an efficient, low cost PNM approach using commercially available on-combine optical sensors. Finding #3 suggests that profit opportunities may exist from PNM based on information from on-combine sensing and aerospace remote sensing. Finding #4, with its emphasis on data retrieval and accuracy, enhances the potential usefulness of a DSS as a tool for field crop management. Finding #5 enables land managers to use a DSS to ascertain at mid-season whether a wheat crop should be harvested for grain or forage. Finding #6a expands potential commercial opportunities of MS imagery and thus has special importance to a majority of aerospace imaging firms specializing in the acquisition and utilization of these data. Finding #6b on index MCARI/MVTI2 has great potential to expand use of ground-based sensing and in-season N management to millions of hectares of land in semiarid environments where water- not N, is the main determinant of grain yield. Finding #6c demonstrates that MCARI/MTVI2 may alleviate the requirement of multiple N-rich reference strips to account for soil differences within farm fields. This simplicity will be less demanding of grower resources, promising substantially greater acceptance of sensing technologies for in-season N management.