Relevant bibliographies by topics / Hyperspectral imaging

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Hyperspectral imaging'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Hyperspectral imaging"

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V, Prathama, and Dr Thippeswamy G. "Food Safety Control Using Hyperspectral Imaging." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 796–806. http://dx.doi.org/10.31142/ijtsrd10983.

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Müller-Rowold, M., and R. Reulke. "HYPERSPECTRAL PANORAMIC IMAGING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 323–28. http://dx.doi.org/10.5194/isprs-archives-xlii-1-323-2018.

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<p><strong>Abstract.</strong> Hyperspectral instruments are designed for the characterisation of planetary surfaces, oceans and the atmosphere. At the moment there are a number of aircraft systems and planned space missions. Examples for this are the hyperspectral missions for Earth remote sensing (EnMAP) and also for deep space and planetary missions (Mercury mission Bepi Colombo).</p><p>There are basically two options for a hyperspectral system: Snapshot systems and scanning systems. This paper investigates a scanning hyperspectral push-broom systems. In most systems the input aperture is a long slit whose image is dispersed across a 2-D detector array, so that all points along a line in the scene are sampled simultaneously. To fill out the spatial dimension orthogonal to the slit, the scene is scanned across the entrance aperture. An ideal low cost hyperspectral scanning device analogue to push broom scanner is a 2D-detector with variable spectral filters, each filter being arranged perpendicular to the direction of flight.</p><p>The biggest challenge is the mapping of the images of the individual spectral channels to each other (co-registration). The solution of the problem is the prerequisite for the use of this kind of hyperspectral cameras e.g. on board of an aircraft. Therefore, an investigation should focus on the procedure of data acquisition, correction and registration. In addition, an example showing the advantages of a possible application is explained.</p>
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Bhargava, Rohit, and Kianoush Falahkheirkhah. "Enhancing hyperspectral imaging." Nature Machine Intelligence 3, no. 4 (April 2021): 279–80. http://dx.doi.org/10.1038/s42256-021-00336-9.

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Rui Zhou, Rui Zhou, Manping Ye Manping Ye, and Huacai Chen Huacai Chen. "Apple bruise detect with hyperspectral imaging technique." Chinese Optics Letters 12, s1 (2014): S11101–311103. http://dx.doi.org/10.3788/col201412.s11101.

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Changsheng Liu, Changsheng Liu, Zhimin Han Zhimin Han, and Tianyu Xie Tianyu Xie. "Hyperspectral high-dynamic-range endoscopic mucosal imaging." Chinese Optics Letters 13, no. 7 (2015): 071701–71705. http://dx.doi.org/10.3788/col201513.071701.

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Lu, Bing, Phuong D. Dao, Jiangui Liu, Yuhong He, and Jiali Shang. "Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture." Remote Sensing 12, no. 16 (August 18, 2020): 2659. http://dx.doi.org/10.3390/rs12162659.

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Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming. In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features. Due to limited accessibility outside of the scientific community, hyperspectral images have not been widely used in precision agriculture. In recent years, different mini-sized and low-cost airborne hyperspectral sensors (e.g., Headwall Micro-Hyperspec, Cubert UHD 185-Firefly) have been developed, and advanced spaceborne hyperspectral sensors have also been or will be launched (e.g., PRISMA, DESIS, EnMAP, HyspIRI). Hyperspectral imaging is becoming more widely available to agricultural applications. Meanwhile, the acquisition, processing, and analysis of hyperspectral imagery still remain a challenging research topic (e.g., large data volume, high data dimensionality, and complex information analysis). It is hence beneficial to conduct a thorough and in-depth review of the hyperspectral imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing hyperspectral information, and recent advances of hyperspectral imaging in agricultural applications. Publications over the past 30 years in hyperspectral imaging technology and applications in agriculture were thus reviewed. The imaging platforms and sensors, together with analytic methods used in the literature, were discussed. Performances of hyperspectral imaging for different applications (e.g., crop biophysical and biochemical properties’ mapping, soil characteristics, and crop classification) were also evaluated. This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology. Recommendations for future hyperspectral imaging research for precision agriculture are also presented.
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Wang, Zhixin, Peng Xu, Bohan Liu, Yankun Cao, Zhi Liu, and Zhaojun Liu. "Hyperspectral imaging for underwater object detection." Sensor Review 41, no. 2 (April 5, 2021): 176–91. http://dx.doi.org/10.1108/sr-07-2020-0165.

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Purpose This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed. Design/methodology/approach First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions. Findings Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future. Originality/value This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.
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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.
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Zou, Chunbo, Jianfeng Yang, Dengshan Wu, Qiang Zhao, Yuquan Gan, Di Fu, Fanchao Yang, Hong Liu, Qinglan Bai, and Bingliang Hu. "Design and Test of Portable Hyperspectral Imaging Spectrometer." Journal of Sensors 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7692491.

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We design and implement a portable hyperspectral imaging spectrometer, which has high spectral resolution, high spatial resolution, small volume, and low weight. The flight test has been conducted, and the hyperspectral images are acquired successfully. To achieve high performance, small volume, and regular appearance, an improved Dyson structure is designed and used in the hyperspectral imaging spectrometer. The hyperspectral imaging spectrometer is suitable for the small platform such as CubeSat and UAV (unmanned aerial vehicle), and it is also convenient to use for hyperspectral imaging acquiring in the laboratory and the field.
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Stuart, Mary B., Andrew J. S. McGonigle, Matthew Davies, Matthew J. Hobbs, Nicholas A. Boone, Leigh R. Stanger, Chengxi Zhu, Tom D. Pering, and Jon R. Willmott. "Low-Cost Hyperspectral Imaging with A Smartphone." Journal of Imaging 7, no. 8 (August 5, 2021): 136. http://dx.doi.org/10.3390/jimaging7080136.

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Recent advances in smartphone technologies have opened the door to the development of accessible, highly portable sensing tools capable of accurate and reliable data collection in a range of environmental settings. In this article, we introduce a low-cost smartphone-based hyperspectral imaging system that can convert a standard smartphone camera into a visible wavelength hyperspectral sensor for ca. £100. To the best of our knowledge, this represents the first smartphone capable of hyperspectral data collection without the need for extensive post processing. The Hyperspectral Smartphone’s abilities are tested in a variety of environmental applications and its capabilities directly compared to the laboratory-based analogue from our previous research, as well as the wider existing literature. The Hyperspectral Smartphone is capable of accurate, laboratory- and field-based hyperspectral data collection, demonstrating the significant promise of both this device and smartphone-based hyperspectral imaging as a whole.
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Dissertations / Theses on the topic "Hyperspectral imaging"

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Porter, Michael Anthony. "Hyperspectral imaging using ultraviolet light /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Dec%5FPorter.pdf.

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Thesis (M.S. in Astronautical Engineering)--Naval Postgraduate School, December 2005.
Thesis Advisor(s): Richard C. Olsen, Christopher Brophy. Includes bibliographical references (p.55-56). Also available online.
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Sjunnebo, Joakim. "Hyperspectral imaging for gas detection." Thesis, KTH, Tillämpad fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-169623.

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Porter, Michael A. "Hyperspectral imaging using ultraviolet light." Thesis, Monterey, California. Naval Postgraduate School, 2005. http://hdl.handle.net/10945/1817.

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The LINEATE IMAGING NEAR ULTRAVIOLET SPECTROMETER (LINUS) instrument has been used to remotely detect and measure sulfur dioxide (SO2). The sensor was calibrated in the lab, with curves of growth created for the 0.29 0.31 æ - spectral range of the LINUS sensor. Field observations were made of a coal burning plant in St. Johnâ s, Arizona at a range of 537 m. The Salt River Coronado plant stacks were emitting on average about 100 ppm and 200 ppm from the left and right stacks respectively. Analysis of the LINUS data matched those values within a few percent. Possible uses for this technology include remote verification of industry emissions and detection of unreported SO2 sources.
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Jones, Julia Craven. "Infrared Hyperspectral Imaging Stokes Polarimeter." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/145409.

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This work presents the design, development, and testing of a field portable imaging spectropolarimeter that operates over the short-wavelength and middle-wavelength portion of the infrared spectrum. The sensor includes a pair of sapphire Wollaston prisms and several high order retarders to produce the first infrared implementation of an imaging Fourier transform spectropolarimeter, providing for the measurement of the complete spectropolarimetric datacube over the passband. The Wollaston prisms serve as a birefringent interferometer with reduced sensitivity to vibration when compared to an unequal path interferometer, such as a Michelson. Polarimetric data are acquired through the use of channeled spectropolarimetry to modulate the spectrum with the Stokes parameter information. The collected interferogram is Fourier filtered and reconstructed to recover the spatially and spectrally varying Stokes vector data across the image.The intent of this dissertation is to provide the reader with a detailed understanding of the steps involved in the development of this infrared hyperspectral imaging polarimeter (IHIP) instrument. First, Chapter 1 provides an overview of the fundamental concepts relevant to this research. These include imaging spectrometers, polarimeters, and spectropolarimeters. A detailed discussion of channeled spectropolarimetry, including a historical study of previous implementations, is also presented. Next a few of the design alternatives that are possible for this work are outlined and discussed in Chapter 2. The configuration that was selected for the IHIP is then presented in detail, including the optical layout, design, and operation. Chapter 3 then presents an artifact reduction technique (ART) that was developed to improve the IHIP's spectropolarimetric reconstructions by reducing errors associated with non-band-limited spectral features. ART is experimentally verified in the infrared using a commercial Fourier transform spectrometer in combination with Yttrium Vanadate as well as Cadmium Sulfide retarders.The remainder of this dissertation then details the testing and analysis of the IHIP instrument. Implementation of ART with the IHIP as well as the employed calibration techniques are described in Chapter 4. Complete calibration of the IHIP includes three distinct processes to provide radiometric, spectral, and polarimetric calibration. With the instrument assembled and calibrated, results and error analyses are presented in Chapter 5. Spectropolarimetric results are obtained in the laboratory as well as outdoors to test the IHIP's real world functionality. The performance of the instrument is also assessed, including experimental measurement of signal-to-noise ratio (SNR), and an analysis of the potential sources of systematic error (such as retarder misalignment and finite polarizer extinction ratio). Chapter 6 presents the design and experimental results for a variable Wollaston prism that can be added to the IHIP to vary the fringe contrast across the field of view. Finally, Chapter 7 includes brief closing remarks summarizing this work and a few observations which may be useful for future infrared imaging Fourier transform channeled spectropolarimeter instruments.
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Hartke, John. "DUAL BAND HYPERSPECTRAL IMAGING SPECTROMETER." Diss., The University of Arizona, 2005. http://hdl.handle.net/10150/195994.

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A temporally and spatially non-scanning imaging spectrometer covering two separate spectral bands in the visible region using computed tomographic imaging techniques is described. The computed tomographic techniques allow for the construction of a three-dimensional hyperspectral data cube (x, y, &#955;) from the two-dimensional input in a single frame time. A computer generated holographic dispersive grating is used to disperse the incoming light into several diffraction orders on a focal plane composed of interwoven pixels independently sensitive to the two bands of interest. Separating the input of the two spectral pixel types gives co-registered output between the two bands and overcomes the limitation of overlapping orders. The proof of concept in the visible is presented using a commercially available camera.The lessons learned from the visible system are applied to a dual infrared band imaging spectrometer. Utilizing recent developments in dual band infrared focal planes a dual band imaging spectrometer is designed covering portions of the MWIR and LWIR atmospheric transmission windows. The system design includes the evaluation of recent developments in dual band infrared focal planes, the design and evaluation of the computer generated holographic disperser, and the optical elements in the system.
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MAKKI, IHAB. "Hyperspectral Imaging for Landmine Detection." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2700516.

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This PhD thesis aims at investigating the possibility to detect landmines using hyperspectral imaging. Using this technology, we are able to acquire at each pixel of the image spectral data in hundreds of wavelengths. So, at each pixel we obtain a reflectance spectrum that is used as fingerprint to identify the materials in each pixel, and mainly in our project help us to detect the presence of landmines. The proposed process works as follows: a preconfigured drone (hexarotor or octorotor) will carry the hyperspectral camera. This programmed drone is responsible of flying over the contaminated area in order to take images from a safe distance. Various image processing techniques will be used to treat the image in order to isolate the landmine from the surrounding. Once the presence of a mine or explosives is suspected, an alarm signal is sent to the base station giving information about the type of the mine, its location and the clear path that could be taken by the mine removal team in order to disarm the mine. This technology has advantages over the actually used techniques: • It is safer because it limits the need of humans in the searching process and gives the opportunity to the demining team to detect the mines while they are in a safe region. • It is faster. A larger area could be cleared in a single day by comparison with demining techniques • This technique can be used to detect at the same time objects other than mines such oil or minerals. First, a presentation of the problem of landmines that is expanding worldwide referring to some statistics from the UN organizations is provided. In addition, a brief presentation of different types of landmines is shown. Unfortunately, new landmines are well camouflaged and are mainly made of plastic in order to make their detection using metal detectors harder. A summary of all landmine detection techniques is shown to give an idea about the advantages and disadvantages of each technique. In this work, we give an overview of different projects that worked on the detection of landmines using hyperspectral imaging. We will show the main results achieved in this field and future work to be done in order to make this technology effective. Moreover, we worked on different target detection algorithms in order to achieve high probability of detection with low false alarm rate. We tested different statistical and linear unmixing based methods. In addition, we introduced the use of radial basis function neural networks in order to detect landmines at subpixel level. A comparative study between different detection methods will be shown in the thesis. A study of the effect of dimensionality reduction using principal component analysis prior to classification is also provided. The study shows the dependency between the two steps (feature extraction and target detection). The selection of target detection algorithm will define if feature extraction in previous phase is necessary. A field experiment has been done in order to study how the spectral signature of landmine will change depending on the environment in which the mine is planted. For this, we acquired the spectral signature of 6 types of landmines in different conditions: in Lab where specific source of light is used; in field where mines are covered by grass; and when mines are buried in soil. The results of this experiment are very interesting. The signature of two types of landmines are used in the simulations. They are a database necessary for supervised detection of landmines. Also we extracted some spectral characteristics of landmines that would help us to distinguish mines from background.
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Nguyen, Dinh hoang. "Development of an optical system for preclinical molecular imaging of atherothrombosis." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCD062/document.

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Dans ce travail de thèse, nous développons des protocoles d'imagerie optique pour l'observation des nanoparticules sur des coupes de tissus afin de relier leur localisation et leur «comportement» à l'environnement biologique, en particulier son éventuel état pathologique. Nous avons synthétisé des agents de contraste bimodaux, sous forme de nanoparticules -NP- visibles en résonance magnétique et en optique, à base d'oxydes de fer et de zinc (Zn(Fe)O) avec une nouvelle méthode de polyol azéotropique dans des solvants glycoliques (DEG et PG). L'élimination de l'eau à l'aide de l'appareil Dean-Stark est une nouvelle stratégie pour la synthèse de NP dans une solution de polyol, avec un rendement élevé et produisant des particules de petite taille. Les NP les plus visibles, selon leur contraste IRM, ont été revêtus de carboxyméthyl pullulane, de polyéthylène glycol, de carboxyméthyl dextrane et de fucoïdane, ce dernier étant un polysaccharide capable de se lier spécifiquement à la paroi vasculaire. Les NPs montrent de bonnes propriétés magnétiques et optiques à température ambiante. Les NP recouvertes ont été injectées dans un modèle de rat d’athérothrombose pour localiser le thrombus par IRM avant sacrifice et collecte des tissus pour étude des coupes histologiques par microscopie optique. La différence entre les images IRM avant et après l'injection de fucoïdane-NPs et de CMD-NPs est claire. Les résultats montrent que les NP recouvertes de fucoïdane sont liées au thrombus. Certains types de microscopies, tels que la microscopie de fluorescence, la microscopie en champ sombre, la microscopie hyperspectrale à champ sombre et la microscopie interférentielle à champ sombre ont été développés pour la détection des NPs en milieu liquide et dans les tissus. En analysant le spectre de chaque pixel et en le comparant au spectre des matériaux de référence, la microscopie hyperspectrale peut détecter la présence de NPs sur des coupes de tissus, les localiser, les identifier et les caractériser. Zn(Fe)O NPs constituerait donc un agent de contraste bimodal potentiel pour l’IRM et l’imagerie optique. Cependant, bien que de nombreux outils optiques avancés aient été développés, nous avons constaté qu'il est toujours difficile d'identifier de manière fiable les NP dans le tissu
In this thesis work, we develop optical imaging protocols for the observation of then anoparticles on tissue slices in order to further link their localization and their “behaviour” to the biological pathological environment. Bimodal zinc and iron oxide-based MRI/optical nanoparticle contrast agents (Zn(Fe)O) have been synthesised with a novel azeotropicpolyol method in glycol solvents (DEG and PG). The most potent NPs, as regard to their MR contrast power, have been coated with carboxymethyl pullulan, polyethylene glycol,carboxymethyl dextran (CMD) and fucoidan, the latter being a polysaccharide able to specifically bind to the vascular wall. The coated NPs were injected into rat to locate atherothrombosis by MRI. Then the histological slices of harvested diseased tissue were imaged with our homemade optical microscope. Water removal using Dean-Stark apparatus is a novel strategy for the synthesis of NPs in polyol solution with high yield and small size.The NPs show the good magnetic and optical properties at room temperature. The coated nanoparticles were injected into an atherothrombotic rat model to locate the thrombus by MRI prior to sacrifice of the animals and tissue collection for histological study by optical microscopy. The difference of MRI images between before and after injection with Fucoidan-NPs and CMD-NPs is clear. The results indicated that fucoidan-NPs are linked to the thrombus. Some type of microscopies, such as fluorescent microscopy, dark field microscopy, hyperspectral dark field microscopy and interference dark field microscopy have been developed for the detection of NPs in liquid medium and in the histological tissue. By analyzing the spectrum of every pixel and comparing to the spectrum of reference materials, hyperspectral microscopy can detect the presence of nanomaterial on exposed tissue slices, locate, identify, and characterize them. Zn(Fe)O NPs would therefore constitute a potential bimodal contrast agent for MRI and optical imaging. Although many advance optical tools have been developed, but we found it is still a challenge to identify reliably the NPs in the tissue
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Frontera, Pons Joana Maria. "Robust target detection for Hyperspectral Imaging." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0024/document.

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L'imagerie hyperspectrale (HSI) repose sur le fait que, pour un matériau donné, la quantité de rayonnement émis varie avec la longueur d'onde. Les capteurs HSI mesurent donc le rayonnement des matériaux au sein de chaque pixel pour un très grand nombre de bandes spectrales contiguës et fournissent des images contenant des informations à la fois spatiale et spectrale. Les méthodes classiques de détection adaptative supposent généralement que le fond est gaussien à vecteur moyenne nul ou connu. Cependant, quand le vecteur moyen est inconnu, comme c'est le cas pour l'image hyperspectrale, il doit être inclus dans le processus de détection. Nous proposons dans ce travail d'étendre les méthodes classiques de détection pour lesquelles la matrice de covariance et le vecteur de moyenne sont tous deux inconnus.Cependant, la distribution statistique multivariée des pixels de l'environnement peut s'éloigner de l'hypothèse gaussienne classiquement utilisée. La classe des distributions elliptiques a été déjà popularisée pour la caractérisation de fond pour l’HSI. Bien que ces modèles non gaussiens aient déjà été exploités dans la modélisation du fond et dans la conception de détecteurs, l'estimation des paramètres (matrice de covariance, vecteur moyenne) est encore généralement effectuée en utilisant des estimateurs conventionnels gaussiens. Dans ce contexte, nous analysons de méthodes d’estimation robuste plus appropriées à ces distributions non-gaussiennes : les M-estimateurs. Ces méthodes de détection couplées à ces nouveaux estimateurs permettent d'une part, d'améliorer les performances de détection dans un environment non-gaussien mais d'autre part de garder les mêmes performances que celles des détecteurs conventionnels dans un environnement gaussien. Elles fournissent ainsi un cadre unifié pour la détection de cibles et la détection d'anomalies pour la HSI
Hyperspectral imaging (HSI) extends from the fact that for any given material, the amount of emitted radiation varies with wavelength. HSI sensors measure the radiance of the materials within each pixel area at a very large number of contiguous spectral bands and provide image data containing both spatial and spectral information. Classical adaptive detection schemes assume that the background is zero-mean Gaussian or with known mean vector that can be exploited. However, when the mean vector is unknown, as it is the case for hyperspectral imaging, it has to be included in the detection process. We propose in this work an extension of classical detection methods when both covariance matrix and mean vector are unknown.However, the actual multivariate distribution of the background pixels may differ from the generally used Gaussian hypothesis. The class of elliptical distributions has already been popularized for background characterization in HSI. Although these non-Gaussian models have been exploited for background modeling and detection schemes, the parameters estimation (covariance matrix, mean vector) is usually performed using classical Gaussian-based estimators. We analyze here some robust estimation procedures (M-estimators of location and scale) more suitable when non-Gaussian distributions are assumed. Jointly used with M-estimators, these new detectors allow to enhance the target detection performance in non-Gaussian environment while keeping the same performance than the classical detectors in Gaussian environment. Therefore, they provide a unified framework for target detection and anomaly detection in HSI
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Yijian, Meng. "Extreme Ultraviolet Hyperspectral Coherent Diffractive Imaging." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/31928.

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We demonstrate hyperspectral imaging using two time-delayed, coherent extreme ultraviolet (XUV) sources. The approach combines broadband XUV high-harmonic generation, holographic imaging, and Fourier transform spectroscopy. The two harmonics sources are spatially separated at generation,and overlap in the far field resulting in a double slit diffraction pattern. We record the two-dimensional intensity modulation as a function of relative time delay; the Fourier transform determines the spatially dependent spectrum. To reduce the delay jitter and improve the spectral resolution, we demonstrate a novel experimental setup that records the relative delay of the two pulses through optical interference. Moreover, we have demonstrated that this broadband approach can be extended to Fourier transform holographic imaging, which avoids extensive phase retrieval computations. Applications include imaging of biological materials near the carbon K-edge.
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Alabboud, Ied. "Human retinal oximetry using hyperspectral imaging." Thesis, Heriot-Watt University, 2009. http://hdl.handle.net/10399/2297.

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The aim of the work reported in this thesis was to investigate the possibility of measuring human retinal oxygen saturation using hyperspectral imaging. A direct non-invasive quantitative mapping of retinal oxygen saturation is enabled by hyperspectral imaging whereby the absorption spectra of oxygenated and deoxygenated haemoglobin are recorded and analysed. Implementation of spectral retinal imaging thus requires ophthalmic instrumentation capable of efficiently recording the requisite spectral data cube. For this purpose, a spectral retinal imager was developed for the first time by integrating a liquid crystal tuneable filter into the illumination system of a conventional fundus camera to enable the recording of narrow-band spectral images in time sequence from 400nm to 700nm. Postprocessing algorithms were developed to enable accurate exploitation of spectral retinal images and overcome the confounding problems associated with this technique due to the erratic eye motion and illumination variation. Several algorithms were developed to provide semi-quantitative and quantitative oxygen saturation measurements. Accurate quantitative measurements necessitated an optical model of light propagation into the retina that takes into account the absorption and scattering of light by red blood cells. To validate the oxygen saturation measurements and algorithms, a model eye was constructed and measurements were compared with gold-standard measurements obtained by a Co-Oximeter. The accuracy of the oxygen saturation measurements was (3.31%± 2.19) for oxygenated blood samples. Clinical trials from healthy and diseased subjects were analysed and oxygen saturation measurements were compared to establish a merit of certain retinal diseases. Oxygen saturation measurements were in agreement with clinician expectations in both veins (48%±9) and arteries (96%±5). We also present in this thesis the development of novel clinical instrument based on IRIS to perform retinal oximetry.
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Books on the topic "Hyperspectral imaging"

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Chang, Chein-I. Hyperspectral Imaging. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6.

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Aikio, Mauri. Hyperspectral prism-grating-prism imaging spectrograph. Espoo [Finland]: Technical Research Centre of Finland, 2001.

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Park, Bosoon, and Renfu Lu, eds. Hyperspectral Imaging Technology in Food and Agriculture. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2836-1.

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Sun, Da-Wen. Hyperspectral imaging for food quality analysis and control. London: Academic, 2010.

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Hyperspectral imaging: Techniques for spectral detection and classification. New York: Kluwer Academic/Plenum Publishers, 2003.

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United States. National Aeronautics and Space Administration., ed. Planetary Hyperspectral Imager (PHI): PIDDP, final report. Danbury, CT: Hughes Danbury Optical Systems, 1996.

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Pedram, Ghamisi, ed. Spectral-spatial classififcation of hyperspectral remote sensing images. Boston: Artech House, 2015.

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Hans, Grahn, and Geladi Paul, eds. Techniques and applications of hyperspectral image analysis. Chichester, West Sussex, England: J. Wiley, 2007.

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United States. National Aeronautics and Space Administration., ed. Programmable hyperspectral image mapper with on-array processing: [patent application]. [Washington, DC: National Aeronautics and Space Administration, 1992.

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United States. National Aeronautics and Space Administration., ed. Programmable hyperspectral image mapper with on-array processing: [patent application]. [Washington, DC: National Aeronautics and Space Administration, 1992.

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Book chapters on the topic "Hyperspectral imaging"

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Gowen, A. A., E. Gaston, and J. Burger. "Hyperspectral Imaging." In Food Engineering Series, 199–216. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0311-5_9.

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Nieves, Juan Luis. "Hyperspectral Imaging." In Encyclopedia of Color Science and Technology, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-642-27851-8_425-1.

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Nieves, Juan Luis. "Hyperspectral Imaging." In Encyclopedia of Color Science and Technology, 910–17. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89862-5_425.

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Chang, Chein-I. "Introduction." In Hyperspectral Imaging, 1–11. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_1.

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Chang, Chein-I. "Target Abundance-Constrained Mixed Pixel Classification (TACMPC)." In Hyperspectral Imaging, 181–205. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_10.

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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 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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Chang, Chein-I. "Automatic Mixed Pixel Classification (AMPC): Unsupervised Mixed Pixel Classification." In Hyperspectral Imaging, 245–55. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_13.

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Chang, Chein-I. "Automatic Mixed Pixel Classificatio (AMPC): Anomaly Classification." In Hyperspectral Imaging, 257–75. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_14.

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Chang, Chein-I. "Automatic mixed pixel classification (AMPC): Linear spectral random mixture analysis (LSRMA)." In Hyperspectral Imaging, 277–303. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_15.

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Conference papers on the topic "Hyperspectral imaging"

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Cui, Qi, and Liang Gao. "Compressive Hyperspectral Imaging." In Imaging Systems and Applications. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/isa.2023.im4e.6.

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Conventional hyperspectral cameras encounter a trade-off between spatial and spectral samplings while capturing an input scene. To address this problem, we propose two imaging systems: Hyperspectral Light Field Tomography (Hyper-LIFT) and Tunable Image Projection Spectrometry (TIPS).
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Dai, Qionghai, Chenguang Ma, Jinli Suo, and Xun Cao. "Computational Hyperspectral Imaging." In JSAP-OSA Joint Symposia. Washington, D.C.: OSA, 2014. http://dx.doi.org/10.1364/jsap.2014.20p_c4_5.

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Chang, Chein-I. "Progressive hyperspectral imaging." In SPIE Remote Sensing, edited by Bormin Huang and Antonio J. Plaza. SPIE, 2012. http://dx.doi.org/10.1117/12.979188.

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Nischan, Melissa L., Amy B. Newbury, Rose Joseph, Mrinal A. Iyengar, Berton C. Willard, Gary J. Swanson, Justin Libby, Bernadette Johnson, and Hsiao-hua K. Burke. "Active hyperspectral imaging." In International Symposium on Optical Science and Technology. SPIE, 2000. http://dx.doi.org/10.1117/12.406578.

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Descour, M. R., C. E. Volin, B. K. Ford, E. L. Dereniak, P. D. Maker, and D. W. Wilson. "Snapshot Hyperspectral Imaging." In Integrated Computational Imaging Systems. Washington, D.C.: OSA, 2001. http://dx.doi.org/10.1364/icis.2001.itha4.

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Descour, Michael, C. E. Volin, B. K. Ford, E. L. Dereniak, P. D. Maker, and D. W. Wilson. "Snapshot hyperspectral imaging." In Integrated Computational Imaging Systems. Washington, D.C.: OSA, 2001. http://dx.doi.org/10.1364/icis.2001.iwb4.

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Descour, Michael, C. E. Volin, B. K. Ford, E. L. Dereniak, P. D. Maker, and D. W. Wilson. "Snapshot hyperspectral imaging." In Integrated Computational Imaging Systems. Washington, D.C.: OSA, 2001. http://dx.doi.org/10.1364/icis.2001.wb4.

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Murzina, Marina V. A., and J. Paul Farrell. "Dynamic hyperspectral imaging." In Nondestructive Evaulation for Health Monitoring and Diagnostics, edited by Aaron A. Diaz, A. Emin Aktan, H. Felix Wu, Steven R. Doctor, and Yoseph Bar-Cohen. SPIE, 2005. http://dx.doi.org/10.1117/12.598620.

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Stern, Adrian, August Yitzhak, Vladimir Farber, and Yair Rivenson. "Hyperspectral compressive imaging." In 2013 12th Workshop on Information Optics (WIO). IEEE, 2013. http://dx.doi.org/10.1109/wio.2013.6601243.

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Cui, Qi, and Liang Gao. "Compressive hyperspectral imaging." In Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, edited by Liang Gao, Guoan Zheng, and Seung Ah Lee. SPIE, 2024. http://dx.doi.org/10.1117/12.3003319.

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Reports on the topic "Hyperspectral imaging"

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Gittins, Christopher M., William J. Marinelli, and Anthony J. Ratkowski. Airis Hyperspectral Imaging Technology,. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada329070.

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Bissett, W. P. High Altitude Hyperspectral Imaging Spectroscopy. Fort Belvoir, VA: Defense Technical Information Center, August 2005. http://dx.doi.org/10.21236/ada439987.

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Davis, Curtiss O. Hyperspectral Imaging of River Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada572752.

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Davis, Curtiss O. Hyperspectral Imaging of River Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557150.

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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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Manolakis, D. Detection Algorithms for Hyperspectral Imaging Applications. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada399744.

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Thiyanarantnam, Pradeep, Stanley Osher, Susan Chen, Wotao Yin, and Kevin Kelly. Compressive Hyperspectral Imaging and Anomaly Detection. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada580327.

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Kwon, Heesung, Dalton Rosario, Neelam Gupta, Matthew Thielke, Dale Smith, Partick Rauss, Patti Gillespie, and Nasser M. Nasrabadi. Hyperspectral Imaging and Obstacle Detection for Robotics Navigation. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada485820.

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Grimm, David C., David W. Messinger, John P. Kerekes, and John R. Schott. Hybridization of Hyperspectral Imaging Target Detection Algorithm Chains. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada431819.

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Wolf, Malima. Hyperspectral Imaging for the Identification of Light Metals. Office of Scientific and Technical Information (OSTI), June 2015. http://dx.doi.org/10.2172/1187882.

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