Thematische Bibliographien / Hyper-(multi-)spectral

Auswahl der wissenschaftlichen Literatur zum Thema „Hyper-(multi-)spectral“

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Veröffentlicht am 7. Juli 2024

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Zeitschriftenartikel zum Thema "Hyper-(multi-)spectral"

Pande, H., Poonam S. Tiwari und Shashi Dobhal. „Analyzing hyper-spectral and multi-spectral data fusion in spectral domain“. Journal of the Indian Society of Remote Sensing 37, Nr. 3 (September 2009): 395–408. http://dx.doi.org/10.1007/s12524-009-0038-2.

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Zhu, Siqi, Kang Su, Migao Li, Zhenqiang Chen, Hao Yin und Zhen Li. „Multi-type hyper-spectral microscopic imaging system“. Optik 127, Nr. 18 (September 2016): 7218–24. http://dx.doi.org/10.1016/j.ijleo.2016.05.053.

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Daigo, M., A. Ono†, R. Urabe‡ und N. Fujiwara. „Pattern decomposition method for hyper-multi-spectral data analysis“. International Journal of Remote Sensing 25, Nr. 6 (März 2004): 1153–66. http://dx.doi.org/10.1080/0143116031000139872.

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Chatoux, Hermine, Noël Richard und Bruno Mercier. „Colour key-point detection“. London Imaging Meeting 2020, Nr. 1 (29.09.2020): 114–18. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-02.

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A lot of image processing tasks require key-point detection. If grey-level approach are numerous, colour and hyper-spectral ones are scarce. In this paper, we propose a generic key-point detection for colour, multi and hyper-spectral images. A new synthetic database is created to compare key-point detection approaches. Our method improves detection when the image complexity increases.

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Yoshikawa, H., M. Murahashi, M. Saito, S. Jiang, M. Iga und E. Tamiya. „Parallelized label-free detection of protein interactions using a hyper-spectral imaging system“. Analytical Methods 7, Nr. 12 (2015): 5157–61. http://dx.doi.org/10.1039/c5ay00738k.

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Awad, Mohamad M., und Marco Lauteri. „Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests“. Sustainability 13, Nr. 10 (16.05.2021): 5548. http://dx.doi.org/10.3390/su13105548.

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Forest-type classification is a very complex and difficult subject. The complexity increases with urban and peri-urban forests because of the variety of features that exist in remote sensing images. The success of forest management that includes forest preservation depends strongly on the accuracy of forest-type classification. Several classification methods are used to map urban and peri-urban forests and to identify healthy and non-healthy ones. Some of these methods have shown success in the classification of forests where others failed. The successful methods used specific remote sensing data technology, such as hyper-spectral and very high spatial resolution (VHR) images. However, both VHR and hyper-spectral sensors are very expensive, and hyper-spectral sensors are not widely available on satellite platforms, unlike multi-spectral sensors. Moreover, aerial images are limited in use, very expensive, and hard to arrange and manage. To solve the aforementioned problems, an advanced method, self-organizing–deep learning (SO-UNet), was created to classify forests in the urban and peri-urban environment using multi-spectral, multi-temporal, and medium spatial resolution Sentinel-2 images. SO-UNet is a combination of two different machine learning technologies: artificial neural network unsupervised self-organizing maps and deep learning UNet. Many experiments have been conducted, and the results showed that SO-UNet overwhelms UNet significantly. The experiments encompassed different settings for the parameters that control the algorithms.

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Mancini, Adriano, Emanuele Frontoni und Primo Zingaretti. „Challenges of multi/hyper spectral images in precision agriculture applications“. IOP Conference Series: Earth and Environmental Science 275 (21.05.2019): 012001. http://dx.doi.org/10.1088/1755-1315/275/1/012001.

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Sun, Li-wei, Xin Ye, Wei Fang, Zhen-lei He, Xiao-long Yi und Yu-peng Wang. „Radiometric calibration of hyper-spectral imaging spectrometer based on optimizing multi-spectral band selection“. Optoelectronics Letters 13, Nr. 6 (November 2017): 405–8. http://dx.doi.org/10.1007/s11801-017-7174-7.

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Li, Jin, und Zilong Liu. „Compression of hyper-spectral images using an accelerated nonnegative tensor decomposition“. Open Physics 15, Nr. 1 (29.12.2017): 992–96. http://dx.doi.org/10.1515/phys-2017-0123.

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AbstractNonnegative tensor Tucker decomposition (NTD) in a transform domain (e.g., 2D-DWT, etc) has been used in the compression of hyper-spectral images because it can remove redundancies between spectrum bands and also exploit spatial correlations of each band. However, the use of a NTD has a very high computational cost. In this paper, we propose a low complexity NTD-based compression method of hyper-spectral images. This method is based on a pair-wise multilevel grouping approach for the NTD to overcome its high computational cost. The proposed method has a low complexity under a slight decrease of the coding performance compared to conventional NTD. We experimentally confirm this method, which indicates that this method has the less processing time and keeps a better coding performance than the case that the NTD is not used. The proposed approach has a potential application in the loss compression of hyper-spectral or multi-spectral images

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Archambault, L., F. Theriault Proulx, S. Beddar und L. Beaulieu. „PO-0807 FORMALISM FOR HYPER-SPECTRAL, MULTI-POINT, PLASTIC SCINTILLATION DETECTORS“. Radiotherapy and Oncology 103 (Mai 2012): S313—S314. http://dx.doi.org/10.1016/s0167-8140(12)71140-4.

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Dissertationen zum Thema "Hyper-(multi-)spectral"

Keef, James Lewis. „Hyper-Spectral Sensor Calibration Extrapolated from Multi-Spectral Measurements“. Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/193627.

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Hyper-spectral (HS) sensors are the instruments of choice for remote sensing applications involving environmental monitoring, littoral survey, and military assessment. Accurate band-to-band sensor radiometric calibration is critical for successful data mining of such HS spectral sets. Current calibration is often performed with methods not necessarily developed for HS applications. This work describes two advances which facilitate laboratory source calibrations. First, an analytical solution to the attenuation of flux within an integrating sphere, the best laboratory source of non-directional radiance for numerous radiometric applications, is given. Relative component attenuations due to integrating sphere coating, exit port escape, and atmospheric absorption are derived employing a geometrical PDF of summed probabilities. Equations providing the attenuation ratios and mean number of reflections for the three outcomes are obtained, yielding the three partial mean pathlengths and variances of all quantities. This work then describes an approach allowing accurate radiometric calibration of HS sensor bands using well-characterized and stable multi-spectral transfer radiometers. The resulting high-quality calibration enables the best representation of the truth spectral signature of the imaged scene. In order to obtain the best calibration with the least instrument complexity and expense, it is critical that the radiometer samples the source with the fewest samples at those optimal wavelengths which predict that source with the highest accuracy. The optimal source-specific bands are determined efficiently by application of the Direct Search methodology described here. Using the minimal selection of multi-spectral radiometer measurements obtained from the optimized transfer radiometer bands, one can obtain a complete and accurate calibration set for the continuum of calibration coefficients required for a robust HS application. Degradation of the prediction is documented for several typical error sources encountered with calibration, thereby defining limitations on the usefulness of the optimization approach.

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Kliman, Douglas Hartley. „Rule-based classification of hyper-temporal, multi-spectral satellite imagery for land-cover mapping and monitoring“. Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/187473.

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A rule-based classification model was developed to derive land-cover information from a large set of hyper-temporal, multi-spectral satellite imagery encompassing the state of Arizona. The model uses Advanced Very High Resolution Radiometer (AVHRR) imagery and the 30-minute digital elevation model (DEM) from the EROS Data Center (EDC) Conterminous U.S. AVHRR Biweekly Composites. Sixty one images from 1990, 1991 and 1992 were analyzed using the Brown & Lowe (1973) Natural Vegetative Communities of Arizona map to identify temporal patterns of Normalized Difference Vegetation Index (NDVI) and thermal measurements for 13 land-cover classes. Fifteen characteristic layers were created to represent the spectral, thermal and temporal properties of the data set. These layers were inputs for the rule-based classification model. The model was run on three years of data, creating three single year land-cover maps. The modeling effort showed that NDVI, thermal and DEM characteristics are useful for discerning land-cover classes. The single year land-cover maps showed that the rule-based model could not detect land-cover change between years. The single year maps were combined to create a summary land-cover map. This map differs from the Brown and Lowe map in the shape, proportional size and spatial distribution of land-cover polygons. The rule-based model can discern more land-cover classes than spectral cluster classification. Ground observations and an aerial video was used to assess map accuracy. The same proportion of agreement was observed between the ground observations, the Brown and Lowe map, and the summary land-cover map. Agreement was higher between video and the summary map than between video and the Brown and Lowe map. With further refinements to the input data set, classification model rules and field accuracy assessment, higher levels of agreement can be expected. Overall results show that rule-based classification of hyper-temporal, multi-spectral satellite imagery is a desirable method for mapping global land-cover.

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Carmody, James Daniel Physical Environmental &amp Mathematical Sciences Australian Defence Force Academy UNSW. „Deriving bathymetry from multispectral and hyperspectral imagery“. Awarded by:University of New South Wales - Australian Defence Force Academy. School of Physical, Environmental and Mathematical Sciences, 2007. http://handle.unsw.edu.au/1959.4/38654.

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Knowledge of water depth is a crucial for planning military amphibious operations. Bathymetry from remote sensing with multispectral or hyperspectral imagery provides an opportunity to acquire water depth data faster than traditional hydrographic survey methods without the need to deploy a hydrographic survey vessel. It also provides a means of collecting bathymetric data covertly. This research explores two techniques for deriving bathymetry and assesses them for use by those involved in providing support to military operations. To support this aim a fieldwork campaign was undertaken in May, 2000, in northern Queensland. The fieldwork collected various inherent and apparent water optical properties and was concurrent with airborne hyperspectral imagery collection, space-based multispectral imagery collection and a hydrographic survey. The water optical properties were used to characterise the water and to understand how they affect deriving bathymetry from imagery. The hydrographic data was used to assess the performance of the bathymetric techniques. Two methods for deriving bathymetry were trialled. One uses a ratio of subsurface irradiance reflectance at two wavelengths and then tunes the result with known water depths. The other inverts the radiative transfer equation utilising the optical properties of the water to derive water depth. Both techniques derived water depth down to approximately six to seven metres. At that point the Cowley Beach waters became optically deep. Sensitivity analysis of the inversion method found that it was most sensitive to errors in vertical attenuation Kd and to errors in transforming the imagery into subsurface irradiance reflectance, R(0-) units. Both techniques require a priori knowledge to derive depth and a more sophisticated approach would be required to determine water depth without prior knowledge of the area of interest. This research demonstrates that water depth can be accurately mapped with optical techniques in less than ideal optical conditions. It also demonstrates that the collection of inherent and apparent optical properties is important for validating remotely sensed imagery.

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Gimenez, Rollin. „Exploitation de données optiques multimodales pour la cartographie des espèces végétales suivant leur sensibilité aux impacts anthropiques“. Electronic Thesis or Diss., Toulouse, ISAE, 2023. http://www.theses.fr/2023ESAE0030.

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Les impacts anthropiques sur les sols végétalisés sont difficiles à caractériser à l'aide d’instruments de télédétection optique. Ces impacts peuvent cependant entrainer de graves conséquences environnementales. Leur détection indirecte est rendue possible par les altérations provoquées sur la biocénose et la physiologie des plantes, qui se traduisent par des changements de propriétés optiques au niveau de la plante et de la canopée. L'objectif de cette thèse est de cartographier les espèces végétales en fonction de leur sensibilité aux impacts anthropiques à l'aide de données de télédétection optique multimodale. Différents impacts anthropiques associés à des activités industrielles passées sont considérés (présence d'hydrocarbures dans le sol, contamination chimique polymétallique, remaniement et compactage du sol, etc.) dans un contexte végétal complexe (distribution hétérogène de diverses espèces de différentes strates). Les informations spectrales, temporelles et/ou morphologiques sont utilisées pour identifier les genres et espèces et caractériser leur état de santé afin de définir et de cartographier leur sensibilité aux différents impacts anthropiques. Des images hyperspectrales aéroportées, des séries temporelles Sentinel-2 et des modèles numériques d'élévation sont exploités indépendamment ou combinés. La démarche proposée repose sur trois étapes. La première consiste à cartographier les impacts anthropiques en combinant des données de télédétection optique et des données fournies par l'opérateur du site (analyses de sol, cartes d'activité, etc.). La seconde étape vise à développer une méthode de cartographie de la végétation à l'aide de données de télédétection optique adaptée à des contextes complexes tels que les sites industriels. Enfin, les variations de la biodiversité et des traits fonctionnels dérivées des images hyperspectrales aéroportées et des modèles numériques d'élévation sont analysées en relation avec la carte d'impact au cours de la troisième étape. Les espèces identifiées comme espèces invasives ainsi que celles en lien avec les pratiques agricoles et forestières et les mesures de biodiversité renseignent sur les impacts biologiques. La cartographie des strates de végétation et la caractérisation de la hauteur des arbres, liées à une succession secondaire, sont utilisées pour détecter les impacts physiques (remaniement du sol, excavations). Enfin, les conséquences du stress induit sur la signature spectrale des espèces sensibles permettent d'identifier les impacts chimiques. Plus précisément, dans le contexte de l'étude, les signatures spectrales de Quercus spp, Alnus glutinosa et des mélanges herbacés varient en fonction de l'acidité du sol, tandis que celles de Platanus x hispanica et des mélanges arbustifs présentent des différences dues aux autres impacts chimiques
Anthropogenic impacts on vegetated soils are difficult to characterize using optical remote sensing devices. However, these impacts can lead to serious environmental consequences. Their indirect detection is made possible by the induced alterations to biocenosis and plant physiology, which result in optical property changes at plant and canopy levels. The objective of this thesis is to map plant species based on their sensitivity to anthropogenic impacts using multimodal optical remote sensing data. Various anthropogenic impacts associated with past industrial activities are considered (presence of hydrocarbons in the soil, polymetallic chemical contamination, soil reworking and compaction, etc.) in a complex plant context (heterogeneous distribution of multiple species from different strata). Spectral, temporal and/or morphological information is used to identify genera and species and characterise their health status to define and map their sensitivity to the various anthropogenic impacts. Hyperspectral airborne images, Sentinel-2 time series and digital elevation models are then used independently or combined. The proposed scientific approach consists of three stages. The first one involves mapping anthropogenic impacts at site level by combining optical remote sensing data with data supplied by the site operator (soil analyses, activity maps, etc.). The second stage seeks to develop a vegetation mapping method using optical remote sensing data suitable to complex contexts like industrial sites. Finally, the variations in biodiversity and functional response traits derived from airborne hyperspectral images and digital elevation models are analysed in relation to the impact map during the third stage. The species identified as invasive species, as well as those related to agricultural and forestry practices, and biodiversity measures provide information about biological impacts. Vegetation strata mapping and characterisation of tree height, linked to secondary succession, are used to detect physical impacts (soil reworking, excavations). Finally, the consequences of induced stress on the spectral signature of susceptible species allow the identification of chemical impacts. Specifically, in the study context, the spectral signatures of Quercus spp., Alnus glutinosa, and grass mixtures vary with soil acidity, while those of Platanus x hispanica and shrub mixtures exhibit differences due to other chemical impacts

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Buchteile zum Thema "Hyper-(multi-)spectral"

Ardabilian, Mohsen, Abdel-Malek Zine und Shiwei Li. „Multi-, Hyper-Spectral Biometrics Modalities“. In Series in BioEngineering, 127–53. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0956-4_8.

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Mohd Ali, Maimunah, und Norhashila Hashim. „Multi/Hyper Spectral Imaging for Mango“. In Nondestructive Quality Assessment Techniques for Fresh Fruits and Vegetables, 143–61. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5422-1_7.

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Tsoulias, Nikos, Ming Zhao, Dimitrios S. Paraforos und Dimitrios Argyropoulos. „Hyper- and Multi-spectral Imaging Technologies“. In Encyclopedia of Digital Agricultural Technologies, 629–40. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24861-0_65.

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Tsoulias, Nikos, Ming Zhao, Dimitrios S. Paraforos und Dimitrios Argyropoulos. „Hyper- and Multi-spectral Imaging Technologies“. In Encyclopedia of Smart Agriculture Technologies, 1–11. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89123-7_65-1.

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Chi, Tao, Yang Wang, Ming Chen und Manman Chen. „Hyper-Spectral Image Classification by Multi-layer Deep Convolutional Neural Networks“. In Advances in Intelligent Systems and Computing, 861–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29516-5_65.

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Guo, Yi-nan, Dawei Xiao, Jian Cheng und Mei Yang. „Multi-spectral Remote Sensing Images Classification Method Based on SVC with Optimal Hyper-parameters“. In Artificial Intelligence and Computational Intelligence, 648–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23896-3_80.

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„Multi/Hyper-Spectral Imaging“. In Handbook of Biomedical Optics, 151–84. CRC Press, 2016. http://dx.doi.org/10.1201/b10951-11.

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Mehta, Dalip Singh, Ankit Butola und Veena Singh. „Multi-spectral and hyper-spectral phase microscopy“. In Quantitative Phase Microscopy and Tomography, 9–1. IOP Publishing, 2022. http://dx.doi.org/10.1088/978-0-7503-3987-2ch9.

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Konferenzberichte zum Thema "Hyper-(multi-)spectral"

Ohgi, Nagamitsu, Akira Iwasaki, Takahiro Kawashima und Hitomi Inada. „Japanese hyper-multi spectral mission“. In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5651968.

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Benedetto, J. J., W. Czaja, M. Ehler, C. Flake und M. Hirn. „Wavelet packets for multi- and hyper-spectral imagery“. In IS&T/SPIE Electronic Imaging, herausgegeben von Frédéric Truchetet und Olivier Laligant. SPIE, 2010. http://dx.doi.org/10.1117/12.843039.

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Yu, Xiujuan, Qin Yan und Zhengjun Liu. „Atmospheric correction of HJ-1A multi-spectral and hyper-spectral images“. In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5647381.

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Harvey, Neal R., und Reid B. Porter. „Spectral morphology for feature extraction from multi- and hyper-spectral imagery“. In Defense and Security, herausgegeben von Sylvia S. Shen und Paul E. Lewis. SPIE, 2005. http://dx.doi.org/10.1117/12.602747.

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Iwasaki, Akira, Nagamitsu Ohgi, Jun Tanii, Takahiro Kawashima und Hitomi Inada. „Hyperspectral Imager Suite (HISUI) -Japanese hyper-multi spectral radiometer“. In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6049308.

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Pareja-Illeras, Rosario, Jose Diaz-Caro, Carmen Blanco-Bartolomé, Rodrigo Linares-Herrero, Joaquín Ramos-Marín und Sergio Ortiz. „Design and comparison of multi- and hyper-spectral imaging systems“. In European Symposium on Optics and Photonics for Defence and Security, herausgegeben von Ronald G. Driggers und David A. Huckridge. SPIE, 2005. http://dx.doi.org/10.1117/12.630540.

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Lasaponara, Rosa, und Antonio Lanorte. „Remote characterization of fuel types using multi- and hyper-spectral data“. In Remote Sensing, herausgegeben von Manfred Owe, Guido D'Urso, Christopher M. U. Neale und Ben T. Gouweleeuw. SPIE, 2006. http://dx.doi.org/10.1117/12.683088.

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Borel, Christoph C., Clyde Spencer, Ken Ewald und Charles Wamsley. „Novel methods for panchromatic sharpening of multi/hyper-spectral image data“. In Imaging Spectrometry XIV. SPIE, 2009. http://dx.doi.org/10.1117/12.825992.

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Borel, Christoph C., und Clyde H. Spencer. „Novel methods for panchromatic sharpening of multi/hyper-spectral image data“. In 2009 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5417487.

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Zhang, Lei, Jiao Bo Gao, Yu Hu, Ying Hui Wang, Ke Feng Sun, Juan Cheng, Dan Dan Sun und Yu Li. „Accelerating hyper-spectral data processing on the multi-CPU and multi-GPU heterogeneous computing platform“. In Second International Conference on Photonics and Optical Engineering, herausgegeben von Chunmin Zhang und Anand Asundi. SPIE, 2017. http://dx.doi.org/10.1117/12.2257563.

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Berichte der Organisationen zum Thema "Hyper-(multi-)spectral"

FOGLER, ROBERT J. Multi- and Hyper-Spectral Sensing for Autonomous Ground Vehicle Navigation. Office of Scientific and Technical Information (OSTI), Juni 2003. http://dx.doi.org/10.2172/820893.

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