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Статті в журналах з теми "HYPER/MULTISPECTRAL IMAGERY"
Undrajavarapu, Jemima, and M. Chandra Sekhar. "Hyper Spectral Remote Sensing for Mapping Species and Characteristics of Mangroves in Krishna Delta Region." Current World Environment 15, no. 3 (December 30, 2020): 613–18. http://dx.doi.org/10.12944/cwe.15.3.25.
Повний текст джерелаHu, Ting, Hongyan Zhang, Huanfeng Shen, and Liangpei Zhang. "Robust Registration by Rank Minimization for Multiangle Hyper/Multispectral Remotely Sensed Imagery." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 6 (June 2014): 2443–57. http://dx.doi.org/10.1109/jstars.2014.2311585.
Повний текст джерелаZhou, Jing, Biwen Wang, Jiahao Fan, Yuchi Ma, Yi Wang, and Zhou Zhang. "A Systematic Study of Estimating Potato N Concentrations Using UAV-Based Hyper- and Multi-Spectral Imagery." Agronomy 12, no. 10 (October 17, 2022): 2533. http://dx.doi.org/10.3390/agronomy12102533.
Повний текст джерелаChen Shan-Jing, Hu Yi-Hua, Sun Du-Juan, and Xu Shi-Long. "A simulation method by air and space integrated fusion based on hyper-/multispectral imagery." Acta Physica Sinica 62, no. 20 (2013): 204201. http://dx.doi.org/10.7498/aps.62.204201.
Повний текст джерелаSharif, I., and S. Khare. "Comparative Analysis of Haar and Daubechies Wavelet for Hyper Spectral Image Classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 937–41. http://dx.doi.org/10.5194/isprsarchives-xl-8-937-2014.
Повний текст джерелаOlivetti, Diogo, Rejane Cicerelli, Jean-Michel Martinez, Tati Almeida, Raphael Casari, Henrique Borges, and Henrique Roig. "Comparing Unmanned Aerial Multispectral and Hyperspectral Imagery for Harmful Algal Bloom Monitoring in Artificial Ponds Used for Fish Farming." Drones 7, no. 7 (June 21, 2023): 410. http://dx.doi.org/10.3390/drones7070410.
Повний текст джерелаHonkavaara, E., R. Näsi, R. Oliveira, N. Viljanen, J. Suomalainen, E. Khoramshahi, T. Hakala, et al. "USING MULTITEMPORAL HYPER- AND MULTISPECTRAL UAV IMAGING FOR DETECTING BARK BEETLE INFESTATION ON NORWAY SPRUCE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 429–34. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-429-2020.
Повний текст джерелаDWIJESH H P, JAYANTH, SANDEEP S. V, and RASHMI S. "Computerized or Automated Object Recognition and Analysis of Pattern Matching in Runways Using Surface Track Data." Journal of University of Shanghai for Science and Technology 23, no. 11 (November 6, 2021): 159–65. http://dx.doi.org/10.51201/jusst/21/10867.
Повний текст джерелаPham, Tien Dat, Junshi Xia, Nam Thang Ha, Dieu Tien Bui, Nga Nhu Le, and Wataru Tekeuchi. "A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018." Sensors 19, no. 8 (April 24, 2019): 1933. http://dx.doi.org/10.3390/s19081933.
Повний текст джерелаPereira-Sandoval, Marcela, Ana Ruescas, Patricia Urrego, Antonio Ruiz-Verdú, Jesús Delegido, Carolina Tenjo, Xavier Soria-Perpinyà, Eduardo Vicente, Juan Soria, and José Moreno. "Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data." Remote Sensing 11, no. 12 (June 21, 2019): 1469. http://dx.doi.org/10.3390/rs11121469.
Повний текст джерелаДисертації з теми "HYPER/MULTISPECTRAL IMAGERY"
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.
Повний текст джерелаMartínez, Usó Adolfo. "Unsupervised Band Selection and Segmentation in Hyper/Multispectral Images." Doctoral thesis, Universitat Jaume I, 2008. http://hdl.handle.net/10803/10483.
Повний текст джерелаSecondly, the problem of segmentation strictly speaking is still a challenging question whatever the input image would be.
This thesis is focused on solving the whole process by means of building an image processing method that analyses and optimises the information acquired by a multispectral device. After that, it detects the main regions that are present in the scene in an image segmentation procedure. Therefore, this work will be divided into two parts. In the first part, an approach for selecting the most relevant subset of input bands will be presented. In the second part, this reduced representation of the initial bands will be the input data of a segmentation method.
Finally, the main contributions of this PhD work could be briefly summarised as follows. On the one hand, we have proposed a pre-processing stage with an unsupervised band selection approach based on information measures that reduces considerably the amount of data. This approach has been successfully compared with well-known algorithms of the literature, showing its good performance with regard to pixel image classification tasks. On the other hand, after the band selection stage, two unsupervised segmentation procedures for detecting the main parts in multispectral images have been also developed. Regarding to this segmentation part, we have mainly contributed with two measures of similarity among regions. An objective functional for selecting an optimal (or close to optimal) partition of the image is another relevant contribution too.
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
PATEL, RISHI. "MATERIAL CLASS MAPPING BY REFLECTANCE MATCHING OF HYPER/MULTISPECTRAL IMAGERY." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16037.
Повний текст джерелаКниги з теми "HYPER/MULTISPECTRAL IMAGERY"
Jia, Xiuping. Field Guide to Hyper/Multispectral Image Processing. SPIE, 2022.
Знайти повний текст джерелаЧастини книг з теми "HYPER/MULTISPECTRAL IMAGERY"
Kozma-Bognár, Veronika, and József Berke. "Determination of Optimal Hyper- and Multispectral Image Channels by Spectral Fractal Structure." In Lecture Notes in Electrical Engineering, 255–62. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06773-5_34.
Повний текст джерелаТези доповідей конференцій з теми "HYPER/MULTISPECTRAL IMAGERY"
Dian, Yuanyong, Zengyuan Li, and Yong Pang. "Forest tree species clssification based on airborne hyper-spectral imagery." In Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Jinwen Tian and Jie Ma. SPIE, 2013. http://dx.doi.org/10.1117/12.2030554.
Повний текст джерелаBernstein, L. S., S. M. Adler-Golden, R. L. Sundberg, and A. J. Ratkowski. "Improved reflectance retrieval from hyper- and multispectral imagery without prior scene or sensor information." In Remote Sensing, edited by James R. Slusser, Klaus Schäfer, and Adolfo Comerón. SPIE, 2006. http://dx.doi.org/10.1117/12.705038.
Повний текст джерелаPerkins, Timothy, Steven Adler-Golden, Michael Matthew, Alexander Berk, Gail Anderson, James Gardner, and Gerald Felde. "Retrieval of atmospheric properties from hyper and multispectral imagery with the FLAASH atmospheric correction algorithm." In Remote Sensing, edited by Klaus Schäfer, Adolfo Comerón, James R. Slusser, Richard H. Picard, Michel R. Carleer, and Nicolaos I. Sifakis. SPIE, 2005. http://dx.doi.org/10.1117/12.626526.
Повний текст джерелаConant, John A., and Kurt D. Annen. "Automated hyper/multispectral image analysis tool." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Sylvia S. Shen and Michael R. Descour. SPIE, 2001. http://dx.doi.org/10.1117/12.437003.
Повний текст джерелаMehta, Sanjeev, Kuhelika Bera, and R. M. Parmar. "Camera electronics for hyper-spectral imager." In Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications II. SPIE, 2008. http://dx.doi.org/10.1117/12.806225.
Повний текст джерелаAiazzi, Bruno, Luciano Alparone, Alberto Arienzo, Andrea Garzelli, and Simone Lolli. "Fast multispectral pansharpening based on a hyper-ellipsoidal color space." In Image and Signal Processing for Remote Sensing XXV, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2019. http://dx.doi.org/10.1117/12.2533481.
Повний текст джерелаGuérineau, Nicolas, Guillaume Druart, Frédéric Gillard, Yann Ferrec, Mathieu Chambon, Sylvain Rommeluère, Grégory Vincent, Riad Haïdar, Jean Taboury, and Manuel Fendler. "Compact designs of hyper- or multispectral imagers compatible with the detector dewar." In SPIE Defense, Security, and Sensing, edited by Bjørn F. Andresen, Gabor F. Fulop, and Paul R. Norton. SPIE, 2011. http://dx.doi.org/10.1117/12.883904.
Повний текст джерелаLin, Yu, Ningfang Liao, Xinquan Wang, Deqi Cui, Minyong Liang, and Yongdao Luo. "Simultaneous acquisition of hyper-spectral image using the computed tomography imaging interferometer." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Carl A. Nardell, Duane D. Smith, and Hangqing Lu. SPIE, 2007. http://dx.doi.org/10.1117/12.750221.
Повний текст джерелаSong, Rui, Shengping Xia, and Jianjun Liu. "RSOM tree and class specific hyper graph based distributed image retrieval." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Jianguo Liu, Kunio Doi, Aaron Fenster, and S. C. Chan. SPIE, 2009. http://dx.doi.org/10.1117/12.832355.
Повний текст джерелаGilchrist, John R., Christopher Durell, and Torbjorn Skauli. "IEEE P4001: progress update towards an international standard for push-broom hyper-spectral imagers." In Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2021. http://dx.doi.org/10.1117/12.2588466.
Повний текст джерелаЗвіти організацій з теми "HYPER/MULTISPECTRAL IMAGERY"
Burks, 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 джерела