Academic literature on the topic 'Hyper-(multi-)spectral'
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Journal articles on the topic "Hyper-(multi-)spectral":
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 (September 2009): 395–408. http://dx.doi.org/10.1007/s12524-009-0038-2.
Zhu, Siqi, Kang Su, Migao Li, Zhenqiang Chen, Hao Yin, and Zhen Li. "Multi-type hyper-spectral microscopic imaging system." Optik 127, no. 18 (September 2016): 7218–24. http://dx.doi.org/10.1016/j.ijleo.2016.05.053.
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 (March 2004): 1153–66. http://dx.doi.org/10.1080/0143116031000139872.
Chatoux, Hermine, Noël Richard, and Bruno Mercier. "Colour key-point detection." London Imaging Meeting 2020, no. 1 (September 29, 2020): 114–18. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-02.
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.
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 (May 16, 2021): 5548. http://dx.doi.org/10.3390/su13105548.
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.
Sun, Li-wei, Xin Ye, Wei Fang, Zhen-lei He, Xiao-long Yi, and Yu-peng Wang. "Radiometric calibration of hyper-spectral imaging spectrometer based on optimizing multi-spectral band selection." Optoelectronics Letters 13, no. 6 (November 2017): 405–8. http://dx.doi.org/10.1007/s11801-017-7174-7.
Li, Jin, and Zilong Liu. "Compression of hyper-spectral images using an accelerated nonnegative tensor decomposition." Open Physics 15, no. 1 (December 29, 2017): 992–96. http://dx.doi.org/10.1515/phys-2017-0123.
Archambault, L., F. Theriault Proulx, S. Beddar, and L. Beaulieu. "PO-0807 FORMALISM FOR HYPER-SPECTRAL, MULTI-POINT, PLASTIC SCINTILLATION DETECTORS." Radiotherapy and Oncology 103 (May 2012): S313—S314. http://dx.doi.org/10.1016/s0167-8140(12)71140-4.
Dissertations / Theses on the topic "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.
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.
Carmody, James Daniel Physical Environmental & 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.
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.
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
Book chapters on the topic "Hyper-(multi-)spectral":
Ardabilian, Mohsen, Abdel-Malek Zine, and 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.
Mohd Ali, Maimunah, and 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.
Tsoulias, Nikos, Ming Zhao, Dimitrios S. Paraforos, and 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.
Tsoulias, Nikos, Ming Zhao, Dimitrios S. Paraforos, and 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.
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, 861–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29516-5_65.
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, 648–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23896-3_80.
"Multi/Hyper-Spectral Imaging." In Handbook of Biomedical Optics, 151–84. CRC Press, 2016. http://dx.doi.org/10.1201/b10951-11.
Mehta, Dalip Singh, Ankit Butola, and 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.
Conference papers on the topic "Hyper-(multi-)spectral":
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zhang, Lei, Jiao Bo Gao, Yu Hu, Ying Hui Wang, Ke Feng Sun, Juan Cheng, Dan Dan Sun, and 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, edited by Chunmin Zhang and Anand Asundi. SPIE, 2017. http://dx.doi.org/10.1117/12.2257563.
Reports on the topic "Hyper-(multi-)spectral":
FOGLER, ROBERT J. Multi- and Hyper-Spectral Sensing for Autonomous Ground Vehicle Navigation. Office of Scientific and Technical Information (OSTI), June 2003. http://dx.doi.org/10.2172/820893.