Добірка наукової літератури з теми "High spatial and spectral remote sensing"
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Статті в журналах з теми "High spatial and spectral remote sensing"
Rocchini, Duccio. "Ecological Remote Sensing: A Challenging Section on Ecological Theory and Remote Sensing." Remote Sensing 13, no. 5 (February 25, 2021): 848. http://dx.doi.org/10.3390/rs13050848.
Повний текст джерелаHan, Yanling, Cong Wei, Ruyan Zhou, Zhonghua Hong, Yun Zhang, and Shuhu Yang. "Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification." Mathematical Problems in Engineering 2020 (April 7, 2020): 1–15. http://dx.doi.org/10.1155/2020/8065396.
Повний текст джерелаWei, Lifei, Ming Yu, Yajing Liang, Ziran Yuan, Can Huang, Rong Li, and Yiwei Yu. "Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery." Remote Sensing 11, no. 17 (August 27, 2019): 2011. http://dx.doi.org/10.3390/rs11172011.
Повний текст джерелаDuan, Meimei, and Lijuan Duan. "High Spatial Resolution Remote Sensing Data Classification Method Based on Spectrum Sharing." Scientific Programming 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/4356957.
Повний текст джерелаPeng, Mingyuan, Lifu Zhang, Xuejian Sun, Yi Cen, and Xiaoyang Zhao. "A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset." Remote Sensing 12, no. 23 (November 27, 2020): 3888. http://dx.doi.org/10.3390/rs12233888.
Повний текст джерелаImanian, A., M. H. Tangestani, and A. Asadi. "INVESTIGATION OF SPECTRAL CHARACTERISTICS OF CARBONATE ROCKS – A CASE STUDY ON POSHT MOLEH MOUNT IN IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 553–57. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-553-2019.
Повний текст джерелаXu, Qingsong, Xin Yuan, Chaojun Ouyang, and Yue Zeng. "Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images." Remote Sensing 12, no. 21 (October 24, 2020): 3501. http://dx.doi.org/10.3390/rs12213501.
Повний текст джерелаNanLan, Wang, and Zeng Xiaoyong. "Hyperspectral Data Classification Algorithm considering Spatial Texture Features." Mobile Information Systems 2022 (March 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/9915809.
Повний текст джерелаZhao, Rui, and Shihong Du. "Spectral-Spatial Residual Network for Fusing Hyperspectral and Panchromatic Remote Sensing Images." Remote Sensing 14, no. 3 (February 8, 2022): 800. http://dx.doi.org/10.3390/rs14030800.
Повний текст джерелаShi, Xue, Yu Wang, Yu Li, and Shiqing Dou. "Remote Sensing Image Segmentation Based on Hierarchical Student’s-t Mixture Model and Spatial Constrains with Adaptive Smoothing." Remote Sensing 15, no. 3 (February 1, 2023): 828. http://dx.doi.org/10.3390/rs15030828.
Повний текст джерелаДисертації з теми "High spatial and spectral remote sensing"
Jay, Steven Charles. "Detection of leafy spurge (Euphorbia esula) using affordable high spatial, spectral and temporal resolution imagery." Thesis, Montana State University, 2010. http://etd.lib.montana.edu/etd/2010/jay/JayS0510.pdf.
Повний текст джерелаArkun, Sedat. "Hyperspectral remote sensing and the urban environment : a study of automated urban feature extraction using a CASI image of high spatial and spectral resolution." Title page, contents, research aims and abstract only, 1999. http://web4.library.adelaide.edu.au/theses/09ARM/09arma721.pdf.
Повний текст джерелаLee, Jong Yeol. "Integrating spatial and spectral information for automatic feature identification in high resolution remotely sensed images." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1600.
Повний текст джерелаTitle from document title page. Document formatted into pages; contains x, 132 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references (p. 124-132).
Kaufman, Jason R. "Spatial-Spectral Feature Extraction on Pansharpened Hyperspectral Imagery." Ohio University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1408706595.
Повний текст джерелаMitri, Georges Habib. "An investigation in the use of advanced remote sensing and geographic information system techniques for post-fire impact assessment on vegetation." Doctoral thesis, Università degli studi di Trieste, 2008. http://hdl.handle.net/10077/2662.
Повний текст джерелаGli incendi boschivi rappresentano uno dei maggiori problemi ambientali nella regione Mediterranea con vaste superfici colpite ogni estate. Una stima dell’impatto ambientale degli incendi (a breve e a lungo termine) richiede la raccolta di informazioni accurate post-incendio relative al tipo di incendio, all’intensità, alla rigenerazione forestale ed al ripristino della vegetazione. L’utilizzo di tecniche avanzate di telerilevamento può fornire un valido strumento per lo studio di questi fenomeni. L’importanza di queste ricerche è stata più volte sottolineata dalla Commissione Europea che si è concentrata sullo studio degli incendi boschivi ed il loro effetto sulla vegetazione attraverso lo sviluppo di adeguati metodi di stima dell’impatto e di mitigazione. Scopo di questo lavoro è la stima dell’impatto post-incendio sulla vegetazione in ambiente Mediterraneo per mezzo di immagini satellitari ad alta risoluzione, di rilievi a terra e mediante tecniche avanzate di analisi dei dati. Il lavoro ha riguardato lo sviluppo di un sistema per l’integrazione di dati telerilevati ad altissima risoluzione spaziale e spettrale. Per la stima dell’impatto a breve termine, un modello di classificazione ad oggetti è stato sviluppato utilizzando immagini Ikonos ad altissima risoluzione spaziale per cartografare il tipo di incendio, differenziando l’incendio radente dall’incendio di chioma. I risultati mostrano che la classificazione ad oggetti potrebbe essere utilizzata per distinguere con elevata accuratezza (87% di accuratezza complessiva) le due tipologie di incendio, in particolare nei boschi Mediterranei aperti. È stata inoltre valutata la capacità della classificazione ad oggetti di distinguere e cartografare tre livelli di intensità del fuoco utilizzando le immagini Ikonos e l’accuratezza del risultato è stimata all’ 83%. Per la stima dell’impatto a lungo termine, la mappatura della rigenerazione post-incendio (pino) e la ripresa della vegetazione arbustiva sono state valutate mediante tre approcci: 1) la classificazione ad oggetti di immagini ad altissima risoluzione QuickBird che ha permesso di mappare la ripresa della vegetazione e l’impatto sulla copertura a seguito dell’incendio distinguendo due livelli di intensità dell’incendio (accuratezza della classificazione 86%). 2) l’analisi statistica di dati iperspettrali rilevati in campo che ha permesso una riduzione del 97% del volume di dati e la selezione delle migliori 14 bande per discriminare l’età e le specie di pino e le 18 migliori bande per la caratterizzazione delle specie arbustive. Successivamente, i dati iperspettrali Hyperion sono stati utlizzati per mappare la rigenerazione forestale e la ripresa della vegetazione. L’accuratezza complessiva della classificazione è stata del 75.1% considerando due diverse specie di pino ed altre specie vegetali. 3) una classificazione ad oggetti che ha combinato l’analisi dei dati QuickBird ed Hyperion. Si è registrato un aumento dell’accuratezza della classificazione pari all’8.06% rispetto all’utilizzo dei soli dati Hyperion. Complessivamente, si osserva che strumenti avanzati di telerilevamento consentono di raccogliere le informazioni relative alle aree incendiate, la rigenerazione forestale e la ripresa della vegetazione in modo accurato e vantaggioso in termini di costi e tempi.
Forest fires are a major environmental problem in the Mediterranean region, where large areas are affected each summer. An assessment of the environmental impact of forest fires (in the short-term and in the long-term) requires the collection of accurate and detailed post-fire information related to fire type, fire severity, forest regeneration and vegetation recovery. Advanced tools in remote sensing provide a powerful tool for the study of this phenomenon. The importance of this work was often emphasized by the European Commission, which focused on the studying of forest fires and their effect on vegetation through the development of appropriate impact assessment and mitigation methods. The aim of this study was to assess the post-fire impact on vegetation in a Mediterranean environment by employing high quality satellite and field data and by using advanced data processing techniques. The work entailed the development of a whole system integrating very high spatial and spectral resolution remotely sensed data. For short-term impact assessment, an object-oriented model was developed using very high spatial resolution Ikonos imagery to map the type of fire, namely, canopy fire and surface fire. The results showed that object-oriented classification could be used to accurately distinguish and map areas of surface and crown fire spread (overall accuracy of 87%), especially that occurring in open Mediterranean forests. Also, the performance of object-based classification in mapping three levels of fire severity by employing high spatial resolution Ikonos imagery was evaluated, and accuracy of the obtained results was estimated to be 83%. As for long-term impact assessment, the mapping of post-fire forest regeneration (pine) and vegetation recovery (shrub) was performed by following three different approaches. First, the developed object-based classification of QuickBird (very high spatial resolution) allowed post-fire vegetation recovery and survival mapping of canopy within two different fire severity levels (86% of classification accuracy). The main effect of fire has been to create a more homogeneous landscape. Second, statistical analysis of field hyperspectral data allowed a 97% reduction in data volume and recommended 14 best narrowbands to discriminate among pine trees (age and species) and 18 bands that best characterize the different shrub species. Then, hyperspectral Hyperion was employed for mapping post-fire forest regeneration and vegetation recovery. The overall classification accuracy was found to be 75.81% when mapping two different regenerated pine species and other species of vegetation recovery. Third, an object-oriented combined analysis of QuickBird and Hyperion was investigated for the same objective. An improvement in classification accuracy of 8.06% was recorded when combining both Hyperion and QuickBird imageries than by using only the Hyperion image. Overall, it was observed that advanced tools in remote sensing provided the necessary means for gathering information about the burned areas, the regenerated forests and the recovered vegetations in a successful and a timely/cost effective manner.
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1977
Sheffield, Kathryn Jane, and kathryn sheffield@dpi vic gov au. "Multi-spectral remote sensing of native vegetation condition." RMIT University. Mathematical and Geospatial Sciences, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091110.112816.
Повний текст джерелаSong, Shi. "The Spectral Signature of Cloud Spatial Structure in Shortwave Radiation." Thesis, University of Colorado at Boulder, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10151129.
Повний текст джерелаIn this thesis, we aim to systematically understand the relationship between cloud spatial structure and its radiation imprints, i.e., three-dimensional (3D) cloud effects, with the ultimate goal of deriving accurate radiative energy budget estimates from space, aircraft, or ground-based observations under spatially inhomogeneous conditions. By studying the full spectral information in the measured and modeled shortwave radiation fields of heterogeneous cloud scenes sampled during aircraft field experiments, we find evidence that cloud spatial structure reveals itself through spectral signatures in the associated irradiance and radiance fields in the near-ultraviolet and visible spectral range.
The spectral signature of 3D cloud effects in irradiances is apparent as a domain- wide, consistent correlation between the magnitude and spectral dependence of net horizontal photon transport. The physical mechanism of this phenomenon is molecular scattering in conjunction with cloud heterogeneity. A simple parameterization with a single parameter ϵ is developed, which holds for individual pixels and the domain as a whole. We then investigate the impact of scene parameters on the discovered correlation and find that it is upheld for a wide range of scene conditions, although the value of ϵ varies from scene to scene.
The spectral signature of 3D cloud effects in radiances manifests itself as a distinct relationship between the magnitude and spectral dependence of reflectance, which cannot be reproduced in the one-dimensional (1D) radiative transfer framework. Using the spectral signature in radiances and irradiances, it is possible to infer information on net horizontal photon transport from spectral radiance perturbations on the basis of pixel populations in sub-domains of a cloud scene.
We show that two different biases need to be considered when attempting radiative closure between measured and modeled irradiance fields below inhomogeneous cloud fields: the remote sensing bias (affecting cloud radiances and thus retrieved properties of the inhomogeneous scene) and the irradiance bias (ignoring 3D effects in the calculation of irradiance fields from imagery-based cloud retrievals). The newly established relationships between spatial and spectral structure lay the foundation for first-order corrections for these 3D biases within a 1D framework, once the correlations are explored on a more statistical basis.
Suliman, Ahmed Saeid Ahmed. "Spectral and spatial variability of the soils on the Maricopa Agricultural Center, Arizona." Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184678.
Повний текст джерелаAlam, Fahim Irfan. "Deep Feature Learning for Spectral-Spatial Classification of Hyperspectral Remote Sensing Images." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386535.
Повний текст джерелаThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Garner, Jamada J. "Scene classification using high spatial resolution multispectral data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FGarner.pdf.
Повний текст джерелаКниги з теми "High spatial and spectral remote sensing"
He, Yuhong, and Qihao Weng, eds. High Spatial Resolution Remote Sensing. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196.
Повний текст джерелаPedram, Ghamisi, ed. Spectral-spatial classififcation of hyperspectral remote sensing images. Boston: Artech House, 2015.
Знайти повний текст джерелаChedin, Alain, Moustafa T. Chahine, and Noëlle A. Scott, eds. High Spectral Resolution Infrared Remote Sensing for Earth’s Weather and Climate Studies. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84599-4.
Повний текст джерелаAlain, Chedin, Chahine Moustafa T, Scott Noëlle A. 1941-, North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Research Workshop on High Spectral Resolution Infrared Remote Sensing for Earth's Weather and Climate Studies (1992 : Paris, France), eds. High spectral resolution infrared remote sensing for earth's weather and climate studies. Berlin: Springer, 1993.
Знайти повний текст джерелаS, Carlson G., and George C. Marshall Space Flight Center., eds. Inter-comparison of wildfire and high-resolution interferometer sounder (HIS) data from STORM-FEST: An investigation of wildfire spectral channel discrepancies. [Marshall Space Flight Center, Ala.]: National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1994.
Знайти повний текст джерелаS, Carlson G., and George C. Marshall Space Flight Center., eds. Inter-comparison of wildfire and high-resolution interferometer sounder (HIS) data from STORM-FEST: An investigation of wildfire spectral channel discrepancies. [Marshall Space Flight Center, Ala.]: National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1994.
Знайти повний текст джерелаS, Carlson G., and George C. Marshall Space Flight Center., eds. Inter-comparison of wildfire and high-resolution interferometer sounder (HIS) data from STORM-FEST: An investigation of wildfire spectral channel discrepancies. [Marshall Space Flight Center, Ala.]: National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1994.
Знайти повний текст джерелаJoanne, White, Mountain Pine Beetle Initiative (Canada), and Pacific Forestry Centre, eds. Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery. Victoria, B.C: Canadian Forest Service, Pacific Forestry Centre, 2005.
Знайти повний текст джерелаJ, Tucker Compton, Dye Dennis G, and Goddard Space Flight Center, eds. North American vegetation patterns observed with the NOAA-7 Advanced Very High Resolution Radiometer. [Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1985.
Знайти повний текст джерелаHlavka, Christine A. Unmixing AVHRR imagery to assess clearcuts and forest regrowth in Oregon. [Washington, D.C: National Aeronautics and Space Administration, 1995.
Знайти повний текст джерелаЧастини книг з теми "High spatial and spectral remote sensing"
Yang, Jian. "Suitable Spectral Mixing Space Selection for Linear Spectral Unmixing of Fine-Scale Urban Imagery." In High Spatial Resolution Remote Sensing, 187–200. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-9.
Повний текст джерелаSingh, Kunwar K., Lindsey Smart, and Gang Chen. "LiDAR and Spectral Data Integration for Coastal Wetland Assessment." In High Spatial Resolution Remote Sensing, 71–88. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-4.
Повний текст джерелаFu, Wenxue, Jianwen Ma, Pei Chen, and Fang Chen. "Remote Sensing Satellites for Digital Earth." In Manual of Digital Earth, 55–123. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9915-3_3.
Повний текст джерелаvan der Meer, Freek. "Image classification through spectral unmixing." In Spatial Statistics for Remote Sensing, 185–93. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/0-306-47647-9_11.
Повний текст джерелаBishop, Michael P., Muthukumar V. Bagavathiannan, Dale A. Cope, Da Huo, Seth C. Murray, Jeffrey A. Olsenholler, William L. Rooney, et al. "High-Resolution UAS Imagery in Agricultural Research Concepts, Issues, and Research Directions." In High Spatial Resolution Remote Sensing, 3–32. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-1.
Повний текст джерелаZhang, Xiuyuan, Shihong Du, and Dongping Ming. "Segmentation Scale Selection in Geographic Object-Based Image Analysis." In High Spatial Resolution Remote Sensing, 201–28. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-10.
Повний текст джерелаSeltzer, Joshua, Michael Guerzhoy, and Monika Havelka. "Computer Vision Methodologies for Automated Processing of Camera Trap Data." In High Spatial Resolution Remote Sensing, 229–42. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-11.
Повний текст джерелаLu, Bing, and Yuhong He. "UAV-Based Multispectral Images for Investigating Grassland Biophysical and Biochemical Properties." In High Spatial Resolution Remote Sensing, 245–59. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-12.
Повний текст джерелаTong, Alexander, Bing Lu, and Yuhong He. "Inversion of a Radiative Transfer Model Using Hyperspectral Data for Deriving Grassland Leaf Chlorophyll." In High Spatial Resolution Remote Sensing, 261–82. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-13.
Повний текст джерелаMui, Amy B. "Wetland Detection Using High Spatial Resolution Optical Remote Sensing Imagery." In High Spatial Resolution Remote Sensing, 283–305. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429470196-14.
Повний текст джерелаТези доповідей конференцій з теми "High spatial and spectral remote sensing"
Winter, Edwin M. "Classification of vegetation types using a high-spectral and spatial resolution hyperspectral sensor." In Remote Sensing, edited by Hiroyuki Fujisada. SPIE, 1998. http://dx.doi.org/10.1117/12.333632.
Повний текст джерелаOrtenberg, Fred, V. Adasko, R. Salichov, V. Antoshkin, and R. Muchamediarov. "Infrared scanning radiometer of high spatial and spectral resolution for the Meteor-3 satellite." In Satellite Remote Sensing, edited by Anton Kohnle and Adam D. Devir. SPIE, 1994. http://dx.doi.org/10.1117/12.197357.
Повний текст джерелаMatteoli, Stefania, Francesca Carnesecchi, Marco Diani, Giovanni Corsini, and Leandro Chiarantini. "Comparative analysis of hyperspectral anomaly detection strategies on a new high spatial and spectral resolution data set." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2007. http://dx.doi.org/10.1117/12.738062.
Повний текст джерелаYang, He, Ben Ma, Qian Du, and Liangpei Zhang. "Comparison of spectral-spatial classification for urban hyperspectral imagery with high resolution." In 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137604.
Повний текст джерелаLazcano López, Raquel, Daniel Madroñal Quintín, Samuel Ortega, Himar Fabelo Gómez, Ruben Salvador, Gustavo M. Callicó, Eduardo Juárez Martínez, and César Sanz Álvaro. "Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL." In High-Performance Computing in Geoscience and Remote Sensing, edited by Bormin Huang, Sebastián López, and Zhensen Wu. SPIE, 2017. http://dx.doi.org/10.1117/12.2279613.
Повний текст джерелаZhao, Ji, Yanfei Zhong, Hong Shu, and Liangpei Zhang. "Spectral-spatial conditional random field classifier with location cues for high spatial resolution imagery." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326797.
Повний текст джерелаClough, Shepard A. "Retrieval of Atmospheric State Parameters from High Resolution Spectral Radiance Data." In Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1993. http://dx.doi.org/10.1364/orsa.1993.tua.1.
Повний текст джерелаLuo, Jiancheng, Yongwei Sheng, Zhanfeng Shen, and Junli Li. "High-precise water extraction based on spectral-spatial coupled remote sensing information." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5648978.
Повний текст джерелаCavalli, Rosa Maria, Lorenzo Fusilli, Giovanni Laneve, Simone Pascucci, Angelo Palombo, Stefano Pignatti, and Federico Santini. "Lake Victoria aquatic weeds monitoring by high spatial and spectral resolution satellite imagery." In 2009 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5418284.
Повний текст джерелаDelalieux, S., D. Raymaekers, K. Nackaerts, E. Honkavaara, J. Soukkamaki, and J. Van Den Borne. "High spatial and spectral remote sensing for detailed mapping of potato plant parameters." In 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2014. http://dx.doi.org/10.1109/whispers.2014.8077564.
Повний текст джерелаЗвіти організацій з теми "High spatial and spectral remote sensing"
Deguise, J. C., M. McGovern, H. McNairn, and K. Staenz. Spatial High Resolution Crop Measurements with Airborne Hyperspectral Remote Sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219371.
Повний текст джерелаSinghroy, V., J. E. Loehr, and A. C. Correa. Landslide Risk Assessment with High Spatial Resolution Remote Sensing Satellite Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219716.
Повний текст джерелаCohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7594385.bard.
Повний текст джерелаChen, Z., S. E. Grasby, C. Deblonde, and X. Liu. AI-enabled remote sensing data interpretation for geothermal resource evaluation as applied to the Mount Meager geothermal prospective area. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330008.
Повний текст джерелаSuir, Glenn, Christina Saltus, and Sam Jackson. Remote Assessment of Swamp and Bottomland Hardwood Habitat Condition in the Maurepas Diversion Project Area. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41563.
Повний текст джерелаAnderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.
Повний текст джерелаHuntley, D., D. Rotheram-Clarke, R. Cocking, J. Joseph, and P. Bobrowsky. Current research on slow-moving landslides in the Thompson River valley, British Columbia (IMOU 5170 annual report). Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331175.
Повний текст джерела