Academic literature on the topic 'Hyper-(multi-)spectral'

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Journal articles on the topic "Hyper-(multi-)spectral"

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Pande, H., Poonam S. Tiwari, and Shashi Dobhal. "Analyzing hyper-spectral and multi-spectral data fusion in spectral domain." Journal of the Indian Society of Remote Sensing 37, no. 3 (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, and Zhen Li. "Multi-type hyper-spectral microscopic imaging system." Optik 127, no. 18 (2016): 7218–24. http://dx.doi.org/10.1016/j.ijleo.2016.05.053.

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Berke, József, Zsolt Polgar, Zoltán Horváth, and Tamás Nagy. "Developing on Exact Quality and Classification System for Plant Improvement." JUCS - Journal of Universal Computer Science 12, no. (9) (2006): 1154–64. https://doi.org/10.3217/jucs-012-09-1154.

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On the field of potato research and breeding, there are several possibilities for the application of modern digital image processing and data collection/analysing techniques. One of the most obvious methods is the multi/hyper spectral analysis. In our experiments research were done in the visible as well as in the infra, near infra and thermal wavelength. For more advanced analysis we developed a multi/hyper-spectral analysis method (spectral fractal dimension measurement and application). In the following we summarize its basic elements and the developed integrated information system of potat
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Chatoux, Hermine, Noël Richard, and Bruno Mercier. "Colour key-point detection." London Imaging Meeting 2020, no. 1 (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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Daigo, M., A. Ono†, R. Urabe‡, and N. Fujiwara. "Pattern decomposition method for hyper-multi-spectral data analysis." International Journal of Remote Sensing 25, no. 6 (2004): 1153–66. http://dx.doi.org/10.1080/0143116031000139872.

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Yoshikawa, H., M. Murahashi, M. Saito, S. Jiang, M. Iga, and E. Tamiya. "Parallelized label-free detection of protein interactions using a hyper-spectral imaging system." Analytical Methods 7, no. 12 (2015): 5157–61. http://dx.doi.org/10.1039/c5ay00738k.

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Awad, Mohamad M., and Marco Lauteri. "Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests." Sustainability 13, no. 10 (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 d
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Salau, Ayodeji Olalekan, Shruti Jain, and Joy Nnenna Eneh. "A review of various image fusion types and transforms." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1515–22. https://doi.org/10.11591/ijeecs.v24.i3.pp1515-1522.

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Utilizing multiple views of an image is an important approach in digital photography, video editing, and medical image fusion applications. Image fusion (ImF) methods are used to improve an image's quality and remove noise from the image signal, resulting in a higher signal-to-noise ratio. A complete assessment of the literature on the different transform kinds, techniques, and rules utilized in ImF is presented in this paper. To assess the outcomes, a white flower image was fused using discrete wavelet transform (DWT) and discrete cosine transform (DCT) techniques. For validation of resul
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Mancini, Adriano, Emanuele Frontoni, and Primo Zingaretti. "Challenges of multi/hyper spectral images in precision agriculture applications." IOP Conference Series: Earth and Environmental Science 275 (May 21, 2019): 012001. http://dx.doi.org/10.1088/1755-1315/275/1/012001.

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Li, Jin, and Zilong Liu. "Compression of hyper-spectral images using an accelerated nonnegative tensor decomposition." Open Physics 15, no. 1 (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 de
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Dissertations / Theses on the topic "Hyper-(multi-)spectral"

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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-directio
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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 the
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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,
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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
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Book chapters on the topic "Hyper-(multi-)spectral"

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

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Mohd Ali, Maimunah, and Norhashila Hashim. "Multi/Hyper Spectral Imaging for Mango." In Nondestructive Quality Assessment Techniques for Fresh Fruits and Vegetables. 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, and Dimitrios Argyropoulos. "Hyper- and Multi-spectral Imaging Technologies." In Encyclopedia of Smart Agriculture Technologies. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89123-7_65-1.

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

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Chi, Tao, Yang Wang, Ming Chen, and Manman Chen. "Hyper-Spectral Image Classification by Multi-layer Deep Convolutional Neural Networks." In Advances in Intelligent Systems and Computing. 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, and Mei Yang. "Multi-spectral Remote Sensing Images Classification Method Based on SVC with Optimal Hyper-parameters." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23896-3_80.

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Raj, Shanthi Pichandi Ananda, Awakash Mishra, Preethi D, and Himani Sivaraman. "Investigating the Use of Multi-Sourced Input Data for Time Series Algorithms Applied to Hyper Spectral Imagery." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8043-3_94.

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

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

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Buchhorn Marcel and Schneider Thomas. "Synergistic Use of Spectral and Angular Signatures from Proba/CHRIS Hyper-spectral Images in a Temporal Context." In Imagin[e,g] Europe. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-494-8-18.

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The anisotropy of backscattering heterodyne the spectral information, which is in general deemed as main information source in remote sensing. However, the anisotropy approach enables to gather information about differences in phenotypes or stand structures, if it is possible to analyze objects with help of satellite systems with multi-angle observation possibilities. Proba/CHRIS is such a new generation satellite system. This paper deals with the synergistic usage of spectral and angular signatures for image classification in order to calculate a possible information surplus. For this, the co
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Conference papers on the topic "Hyper-(multi-)spectral"

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Ohgi, Nagamitsu, Akira Iwasaki, Takahiro Kawashima, and 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, and M. Hirn. "Wavelet packets for multi- and hyper-spectral imagery." In IS&T/SPIE Electronic Imaging, edited by Frédéric Truchetet and Olivier Laligant. SPIE, 2010. http://dx.doi.org/10.1117/12.843039.

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Yu, Xiujuan, Qin Yan, and 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., and Reid B. Porter. "Spectral morphology for feature extraction from multi- and hyper-spectral imagery." In Defense and Security, edited by Sylvia S. Shen and 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, and 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, and Sergio Ortiz. "Design and comparison of multi- and hyper-spectral imaging systems." In European Symposium on Optics and Photonics for Defence and Security, edited by Ronald G. Driggers and David A. Huckridge. SPIE, 2005. http://dx.doi.org/10.1117/12.630540.

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

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Borel, Christoph C., Clyde Spencer, Ken Ewald, and 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., and 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, et al. "Accelerating hyper-spectral data processing on the multi-CPU and multi-GPU heterogeneous computing platform." In Second International Conference on Photonics and Optical Engineering, edited by Chunmin Zhang and Anand Asundi. SPIE, 2017. http://dx.doi.org/10.1117/12.2257563.

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Reports on the topic "Hyper-(multi-)spectral"

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

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