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Статті в журналах з теми "Hyperspectral and multispectral data fusion"
Chakravortty, S., and P. Subramaniam. "Fusion of Hyperspectral and Multispectral Image Data for Enhancement of Spectral and Spatial Resolution." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 1099–103. http://dx.doi.org/10.5194/isprsarchives-xl-8-1099-2014.
Повний текст джерелаMifdal, Jamila, Bartomeu Coll, Jacques Froment, and Joan Duran. "Variational Fusion of Hyperspectral Data by Non-Local Filtering." Mathematics 9, no. 11 (May 31, 2021): 1265. http://dx.doi.org/10.3390/math9111265.
Повний текст джерелаGao, Jianhao, Jie Li, and Menghui Jiang. "Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner." Remote Sensing 13, no. 16 (August 13, 2021): 3226. http://dx.doi.org/10.3390/rs13163226.
Повний текст джерелаLi, Jiaxin, Ke Zheng, Jing Yao, Lianru Gao, and Danfeng Hong. "Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion." IEEE Geoscience and Remote Sensing Letters 19 (2022): 1–5. http://dx.doi.org/10.1109/lgrs.2022.3151779.
Повний текст джерелаNikolakopoulos, K., Ev Gioti, G. Skianis, and D. Vaiopoulos. "AMELIORATING THE SPATIAL RESOLUTION OF HYPERION HYPERSPECTRAL DATA. THE CASE OF ANTIPAROS ISLAND." Bulletin of the Geological Society of Greece 43, no. 3 (January 24, 2017): 1627. http://dx.doi.org/10.12681/bgsg.11337.
Повний текст джерелаChang, Chein-I., Meiping Song, Chunyan Yu, Yulei Wang, Haoyang Yu, Jiaojiao Li, Lin Wang, Hsiao-Chi Li, and Xiaorun Li. "Editorial for Special Issue “Advances in Hyperspectral Data Exploitation”." Remote Sensing 14, no. 20 (October 13, 2022): 5111. http://dx.doi.org/10.3390/rs14205111.
Повний текст джерелаHervieu, Alexandre, Arnaud Le Bris, and Clément Mallet. "FUSION OF HYPERSPECTRAL AND VHR MULTISPECTRAL IMAGE CLASSIFICATIONS IN URBAN α–AREAS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 457–64. http://dx.doi.org/10.5194/isprs-annals-iii-3-457-2016.
Повний текст джерелаPeng, Mingyuan, Guoyuan Li, Xiaoqing Zhou, Chen Ma, Lifu Zhang, Xia Zhang, and Kun Shang. "A Registration-Error-Resistant Swath Reconstruction Method of ZY1-02D Satellite Hyperspectral Data Using SRE-ResNet." Remote Sensing 14, no. 22 (November 21, 2022): 5890. http://dx.doi.org/10.3390/rs14225890.
Повний текст джерелаGuilloteau, Claire, Thomas Oberlin, Olivier Berné, Émilie Habart, and Nicolas Dobigeon. "Simulated JWST Data Sets for Multispectral and Hyperspectral Image Fusion." Astronomical Journal 160, no. 1 (June 18, 2020): 28. http://dx.doi.org/10.3847/1538-3881/ab9301.
Повний текст джерелаYokoya, Naoto, Takehisa Yairi, and Akira Iwasaki. "Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion." IEEE Transactions on Geoscience and Remote Sensing 50, no. 2 (February 2012): 528–37. http://dx.doi.org/10.1109/tgrs.2011.2161320.
Повний текст джерелаДисертації з теми "Hyperspectral and multispectral data fusion"
Vivone, Gemine. "Multispectral and hyperspectral pansharpening." Doctoral thesis, Universita degli studi di Salerno, 2014. http://hdl.handle.net/10556/1604.
Повний текст джерелаRemote sensing consists in measuring some characteristics of an object from a distance. A key example of remote sensing is the Earth observation from sensors mounted on satellites that is a crucial aspect of space programs. The first satellite used for Earth observation was Explorer VII. It has been followed by thousands of satellites, many of which are still working. Due to the availability of a large number of different sensors and the subsequent huge amount of data collected, the idea of obtaining improved products by means of fusion algorithms is becoming more intriguing. Data fusion is often exploited for indicating the process of integrating multiple data and knowledge related to the same real-world scene into a consistent, accurate, and useful representation. This term is very generic and it includes different levels of fusion. This dissertation is focused on the low level data fusion, which consists in combining several sources of raw data. In this field, one of the most relevant scientific application is surely the Pansharpening. Pansharpening refers to the fusion of a panchromatic image (a single band that covers the visible and near infrared spectrum) and a multispectral/hyperspectral image (tens/hundreds bands) acquired on the same area. [edited by author]
XII ciclo n.s.
Ahn, Byung Joon. "Design and development of a work-in-progress, low-cost Earth Observation multispectral satellite for use on the International Space Station." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587426345809705.
Повний текст джерелаJacq, Kévin. "Traitement d'images multispectrales et spatialisation des données pour la caractérisation de la matière organique des phases solides naturelles. High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core High-resolution grain size distribution of sediment core with 2 hyperspectral imaging Study of pansharpening methods applied to hyperspectral images of sediment cores." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAA024.
Повний текст джерелаThe evolution of the environment and climate are, currently, the focus of all attention. The impacts of the activities of present and past societies on the environment are in particular questioned in order to better anticipate the implications of our current activities on the future. Better describing past environments and their evolutions are possible thanks to the study of many natural recorders (sediments, speleothems, tree rings, corals). Thanks to them, it is possible to characterize biological-physical-chemical evolutions at di erent temporal resolutions and for di erent periods. The high resolution understood here as the su cient resolution for the study of the environment in connection with the evolution of societies constitutes the main lock of the study of these natural archives in particular because of the analytical capacity devices that can only rarely see ne inframillimetre structures. This work is built on the assumption that the use of hyperspectral sensors (VNIR, SWIR, LIF) coupled with relevant statistical methods should allow access to the spectral and therefore biological-physical-chemical contained in these natural archives at a spatial resolution of a few tens of micrometers and, therefore, to propose methods to reach the high temporal resolution (season). Besides, to obtain reliable estimates, several imaging sensors and linear spectroscopy (XRF, TRES) are used with their own characteristics (resolutions, spectral ranges, atomic/molecular interactions). These analytical methods are used for surface characterization of sediment cores. These micrometric spectral analyses are mapped to usual millimeter geochemical analyses. Optimizing the complementarity of all these data involves developing methods to overcome the di culty inherent in coupling data considered essentially dissimilar (resolutions, spatial shifts, spectral non-recovery). Thus, four methods were developed. The rst consists in combining hyperspectral and usual methods for the creation of quantitative predictive models. The second allows the spatial registration of di erent hyperspectral images at the lowest resolution. The third focuses on their merging with the highest of the resolutions. Finally, the last one focuses on deposits in sediments (laminae, oods, tephras) to add a temporal dimension to our studies. Through all this information and methods, multivariate predictive models were estimated for the study of organic matter, textural parameters and particle size distribution. The laminated and instantaneous deposits within the samples were characterized. These made it possible to estimate oods chronicles, as well as biological-physical-chemical variations at the season scale. Hyperspectral imaging coupled with data analysis methods are therefore powerful tools for the study of natural archives at ne temporal resolutions. The further development of the approaches proposed in this work will make it possible to study multiple archives to characterize evolutions at the scale of one or more watershed(s)
Benhalouche, Fatima Zohra. "Méthodes de démélange et de fusion des images multispectrales et hyperspectrales de télédétection spatiale." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30083/document.
Повний текст джерелаIn this thesis, we focused on two main problems of the spatial remote sensing of urban environments which are: "spectral unmixing" and "fusion". In the first part of the thesis, we are interested in the spectral unmixing of hyperspectral images of urban scenes. The developed methods are designed to unsupervisely extract the spectra of materials contained in an imaged scene. Most often, spectral unmixing methods (methods known as blind source separation) are based on the linear mixing model. However, when facing non-flat landscape, as in the case of urban areas, the linear mixing model is not valid any more, and must be replaced by a nonlinear mixing model. This nonlinear model can be reduced to a linear-quadratic/bilinear mixing model. The proposed spectral unmixing methods are based on matrix factorization with non-negativity constraint, and are designed for urban scenes. The proposed methods generally give better performance than the tested literature methods. The second part of this thesis is devoted to the implementation of methods that allow the fusion of multispectral and hyperspectral images, in order to improve the spatial resolution of the hyperspectral image. This fusion consists in combining the high spatial resolution of multispectral images and high spectral resolution of hyperspectral images. The implemented methods are designed for urban remote sensing data. These methods are based on linear-quadratic spectral unmixing techniques and use the non-negative matrix factorization. The obtained results show that the developed methods give good performance for hyperspectral and multispectral data fusion. They also show that these methods significantly outperform the tested literature approaches
Wahrman, Spencer A. "Time Series Analysis of Vegetation Change using Hyperspectral and Multispectral Data." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17473.
Повний текст джерелаGrand Lake, Colorado has experienced a severe mountain pine beetle outbreak over the past twenty years. The aim of this study was to map lodgepole pine mortality and health decline due to mountain pine beetle. Multispectral data spanning a five-year period from 2006 to 2011 were used to assess the progression from live, green trees to dead, gray-brown trees. IKONOS data from 2011 were corrected to reflectance and validated against an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset, also collected during 2011. These data were used along with additional reflectance-corrected multispectral datasets (IKONOS from 2007 and QuickBird from 2006 and 2009) to create vegetation classification maps using both library spectra and regions of interest. Two sets of classification maps were produced using Mixture-Tuned Matched Filtering. The results were assessed visually and mathematically. Through visual inspection of the classification maps, increasing lodgepole pine mortality over time was observed. The results were quantified using confusion matrices comparing the classification results of the AVIRIS classified data and the IKONOS and QuickBird classified data. The comparison showed that change could be seen over time, but due to the short time period of the data the change was not as significant as expected.
Hall, William D. "Exploration of Data Fusion between Polarimetric Radar and Multispectral Image Data." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17375.
Повний текст джерелаTypically, analysis of remote sensing data is limited to one sensor at a time which usually contains data from the same general portion of the electromagnetic spectrum. SAR and visible near infrared data of Monterey, CA, were analyzed and fused with the goal of achieving improved land classification results. A common SAR decomposition, the Pauli decomposition was performed and inspected. The SAR Pauli decomposition and the multispectral reflectance data were fused at the pixel level, then analyzed using multispectral classification techniques. The results were compared to the multispectral classifications using the SAR decomposition results for a basis of interpreting the changes. The combined dataset resulted in little to no quantitative improvement in land cover classification capability, however inspection of the classification maps indicated an improved classification ability with the combined data. The most noticeable increases in classification accuracy occurred in spatial regions where the land features were parallel to the SAR flight line. This dependence on orientation makes this fusion process more ideal for datasets with more consistent features throughout the scene.
PISCINI, ALESSANDRO. "Neural-Network approach to multispectral and hyperspectral data analysis for volcanic monitoring." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2015. http://hdl.handle.net/2108/214160.
Повний текст джерелаAdams, Andrew J. "Multispectral persistent surveillance /." Online version of thesis, 2008. http://hdl.handle.net/1850/7070.
Повний текст джерелаJahan, Farah. "Fusion of Hyperspectral and LiDAR Data for Land Cover Classification." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386555.
Повний текст джерелаThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Sharma, Rajeev. "Using multispectral and hyperspectral satellite data for early detection of mountain pine beetle damage." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31064.
Повний текст джерелаForestry, Faculty of
Graduate
Книги з теми "Hyperspectral and multispectral data fusion"
Giovanni, Motta, Rizzo Francesco, and Storer James A. 1953-, eds. Hyperspectral data compression. New York: Springer, 2005.
Знайти повний текст джерелаChaudhuri, Subhasis. Hyperspectral Image Fusion. New York, NY: Springer New York, 2013.
Знайти повний текст джерела1963-, Arora M. K., ed. Advanced image processing techniques for remotely sensed hyperspectral data. Berlin: Springer, 2004.
Знайти повний текст джерелаHyperspectral data compression. New York, NY: Springer, 2006.
Знайти повний текст джерелаHyperspectral data, analysis techniques, and applications. Dehradun: Bishen Singh Mahendra Pal Singh, 2011.
Знайти повний текст джерелаChaudhuri, Subhasis, and Ketan Kotwal. Hyperspectral Image Fusion. Springer, 2013.
Знайти повний текст джерелаChaudhuri, Subhasis, and Ketan Kotwal. Hyperspectral Image Fusion. Springer, 2015.
Знайти повний текст джерелаChang, Chein-I. Hyperspectral Data Exploitation: Theory and Applications. Wiley & Sons, Incorporated, John, 2006.
Знайти повний текст джерелаHyperspectral data exploitation: Theory and applications. Hoboken, NJ: Wiley-Interscience, 2007.
Знайти повний текст джерелаMultispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-1265-5.
Повний текст джерелаЧастини книг з теми "Hyperspectral and multispectral data fusion"
Vibhute, Amol D., Sandeep V. Gaikwad, Rajesh K. Dhumal, Ajay D. Nagne, Amarsinh B. Varpe, Dhananjay B. Nalawade, Karbhari V. Kale, and Suresh C. Mehrotra. "Hyperspectral and Multispectral Remote Sensing Data Fusion for Classification of Complex-Mixed Land Features Using SVM." In Communications in Computer and Information Science, 345–62. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9181-1_31.
Повний текст джерелаYang, Jingxiang, Yong-Qiang Zhao, and Jonathan Cheung-Wai Chan. "Hyperspectral–Multispectral Image Fusion Enhancement Based on Deep Learning." In Hyperspectral Image Analysis, 407–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38617-7_14.
Повний текст джерелаChen, Wenjing, and Xiaoqiang Lu. "Unregistered Hyperspectral and Multispectral Image Fusion with Synchronous Nonnegative Matrix Factorization." In Pattern Recognition and Computer Vision, 602–14. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60633-6_50.
Повний текст джерелаGao, Jianhao, Jie Li, Qiangqiang Yuan, Jiang He, and Xin Su. "Self-supervised Hyperspectral and Multispectral Image Fusion in Deep Neural Network." In Lecture Notes in Computer Science, 425–36. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87361-5_35.
Повний текст джерелаWang, Tingting, Yang Xu, Zebin Wu, and Zhihui Wei. "Spatial Spectral Joint Correction Network for Hyperspectral and Multispectral Image Fusion." In Lecture Notes in Computer Science, 16–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02444-3_2.
Повний текст джерелаSingh, Harpinder, Ajay Roy, R. K. Setia, and Brijendra Pateriya. "Simulation of Multispectral Data Using Hyperspectral Data for Crop Stress Studies." In Lecture Notes in Electrical Engineering, 43–52. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7698-8_5.
Повний текст джерелаLiu, Zhe, Yinqiang Zheng, and Xian-Hua Han. "Unsupervised Multispectral and Hyperspectral Image Fusion with Deep Spatial and Spectral Priors." In Computer Vision – ACCV 2020 Workshops, 31–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69756-3_3.
Повний текст джерелаZhang, Rui, Peng Fu, Leilei Geng, and Quansen Sun. "Hyperspectral and Multispectral Image Fusion Based on Unsupervised Feature Mixing and Reconstruction Network." In Pattern Recognition and Computer Vision, 189–200. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18916-6_16.
Повний текст джерелаChakravortty, Somdatta, and Anil Bhondekar. "Spatial and Spectral Quality Assessment of Fused Hyperspectral and Multispectral Data." In Biologically Rationalized Computing Techniques For Image Processing Applications, 133–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61316-1_7.
Повний текст джерелаAnuja Srivastava, Vikrant Bhateja, and Aisha Moin. "Combination of PCA and Contourlets for Multispectral Image Fusion." In Proceedings of the International Conference on Data Engineering and Communication Technology, 577–85. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1678-3_55.
Повний текст джерелаТези доповідей конференцій з теми "Hyperspectral and multispectral data fusion"
Winter, Michael E., Edwin M. Winter, Scott G. Beaven, and Anthony J. Ratkowski. "High-performance fusion of multispectral and hyperspectral data." In Defense and Security Symposium, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2006. http://dx.doi.org/10.1117/12.668622.
Повний текст джерелаLobato, Michaela, William Robert Norris, Rakesh Nagi, Ahmet Soylemezoglu, and Dustin Nottage. "Machine Learning for Soil Moisture Prediction Using Hyperspectral and Multispectral Data." In 2021 IEEE 24th International Conference on Information Fusion (FUSION). IEEE, 2021. http://dx.doi.org/10.23919/fusion49465.2021.9627067.
Повний текст джерелаYokoya, Naoto, and Akira Iwasaki. "Hyperspectral and multispectral data fusion mission on hyperspectral imager suite (HISUI)." In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723731.
Повний текст джерелаTakeyama, Saori, Shunsuke Ono, and Itsuo Kumazawa. "Hyperspectral and Multispectral Data Fusion by a Regularization Considering." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683646.
Повний текст джерелаYokoya, Naoto, Jocelyn Chanussot, and Akira Iwasaki. "Hyperspectral and multispectral data fusion based on nonlinear unmixing." In 2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2012. http://dx.doi.org/10.1109/whispers.2012.6874237.
Повний текст джерелаDu, Qian, John Ball, and Chiru Ge. "Hyperspectral and LiDAR data fusion using collaborative representation." In Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVI, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2020. http://dx.doi.org/10.1117/12.2558967.
Повний текст джерелаXie, Jinchi, Ying Wang, and Jie Li. "Hyperspectral and Multispectral Data Fusion with 1D-Convolution on Spectrum." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884077.
Повний текст джерелаHao, Junbo, Ying Wang, Jie Li, and Xinbo Gao. "Non-Local Compressive Network for Hyperspectral and Multispectral Data Fusion." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8900108.
Повний текст джерелаMurphy, James M., and Mauro Maggioni. "Diffusion geometric methods for fusion of remotely sensed data." In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2018. http://dx.doi.org/10.1117/12.2305274.
Повний текст джерелаSun, Airong, and Yihua Tan. "Hyperspectral data classification using image fusion based on curvelet transform." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Henri Maître, Hong Sun, Jianguo Liu, and Enmin Song. SPIE, 2007. http://dx.doi.org/10.1117/12.750049.
Повний текст джерелаЗвіти організацій з теми "Hyperspectral and multispectral data fusion"
Bissett, W. P., and David D. Kohler. High Resolution Multispectral and Hyperspectral Data Fusion for Advanced Geospatial Information Products. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada630662.
Повний текст джерелаBissett, W. P., and David D. Kohler. High Resolution Multispectral and Hyperspectral Data Fusion for Advanced Geospatial Information Products. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada465229.
Повний текст джерелаKey, Gary, and Mark Schmalz. Surface and Buried Mine Detection with Variance-Based Multispectral Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, November 2000. http://dx.doi.org/10.21236/ada392027.
Повний текст джерелаWest, Roger, David Yocky, John Vander Laan, Dylan Anderson, and Brian Redman. Data Fusion of Very High Resolution Hyperspectral and Polarimetric SAR Imagery for Terrain Classification. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1813672.
Повний текст джерелаMobley, Curtis D. Continued Development of the Look-up-table (LUT) Methodology for Interpretation of Remotely Sensed Ocean Color Data and Fusion of Hyperspectral Imagery with LIDAR Bathymetry. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada630665.
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 джерелаBurks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.
Повний текст джерелаHodul, M., H. P. White, and A. Knudby. A report on water quality monitoring in Quesnel Lake, British Columbia, subsequent to the Mount Polley tailings dam spill, using optical satellite imagery. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330556.
Повний текст джерела