Literatura académica sobre el tema "HYPER SPECTRAL IMAGE CLASSIFICATION"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "HYPER SPECTRAL IMAGE CLASSIFICATION".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "HYPER SPECTRAL IMAGE CLASSIFICATION"
HUANG Hong, 黄. 鸿., 陈美利 CHEN Mei-li, 段宇乐 DUAN Yu-le y 石光耀 SHI Guang-yao. "Hyper-spectral image classification using spatial-spectral manifold reconstruction". Optics and Precision Engineering 26, n.º 7 (2018): 1827–36. http://dx.doi.org/10.3788/ope.20182607.1827.
Texto completoJavadi, P. "USE SATELLITE IMAGES AND IMPROVE THE ACCURACY OF HYPERSPECTRAL IMAGE WITH THE CLASSIFICATION". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (11 de diciembre de 2015): 343–49. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-343-2015.
Texto completoAlhayani, Bilal y Haci Ilhan. "Hyper spectral Image classification using Dimensionality Reduction Techniques". IJIREEICE 5, n.º 4 (15 de abril de 2017): 71–74. http://dx.doi.org/10.17148/ijireeice.2017.5414.
Texto completoSharif, I. y 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 (28 de noviembre de 2014): 937–41. http://dx.doi.org/10.5194/isprsarchives-xl-8-937-2014.
Texto completoShanmugapriya, G. y . "An Efficient Spectral Spatial Classification for Hyper Spectral Images". International Journal of Engineering & Technology 7, n.º 3.12 (20 de julio de 2018): 1050. http://dx.doi.org/10.14419/ijet.v7i3.12.17630.
Texto completoBanit', Ibtissam, N. A. ouagua, Mounir Ait Kerroum, Ahmed Hammouch y Driss Aboutajdine. "Band selection by mutual information for hyper-spectral image classification". International Journal of Advanced Intelligence Paradigms 8, n.º 1 (2016): 98. http://dx.doi.org/10.1504/ijaip.2016.074791.
Texto completoTANG Yan-hui, 唐艳慧, 赵鹏 ZHAO Peng y 王承琨 WANG Cheng-kun. "Texture classification algorithm of wood hyper-spectral image based on multi-fractal spectra". Chinese Journal of Liquid Crystals and Displays 34, n.º 12 (2019): 1182–90. http://dx.doi.org/10.3788/yjyxs20193412.1182.
Texto completoLavanya, K., R. Jaya Subalakshmi, T. Tamizharasi, Lydia Jane y Akila Victor. "Unsupervised Unmixing and Segmentation of Hyper Spectral Images Accounting for Soil Fertility". Scalable Computing: Practice and Experience 23, n.º 4 (23 de diciembre de 2022): 291–301. http://dx.doi.org/10.12694/scpe.v23i4.2031.
Texto completoZhang, Tianxiang, Wenxuan Wang, Jing Wang, Yuanxiu Cai, Zhifang Yang y Jiangyun Li. "Hyper-LGNet: Coupling Local and Global Features for Hyperspectral Image Classification". Remote Sensing 14, n.º 20 (20 de octubre de 2022): 5251. http://dx.doi.org/10.3390/rs14205251.
Texto completoLi, Runya y Shenglian Li. "Multimedia Image Data Analysis Based on KNN Algorithm". Computational Intelligence and Neuroscience 2022 (12 de abril de 2022): 1–8. http://dx.doi.org/10.1155/2022/7963603.
Texto completoTesis sobre el tema "HYPER SPECTRAL IMAGE CLASSIFICATION"
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.
Texto completoFalco, Nicola. "Advanced Spectral and Spatial Techniques for Hyperspectral Image Analysis and Classification". Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/369072.
Texto completoFalco, Nicola. "Advanced Spectral and Spatial Techniques for Hyperspectral Image Analysis and Classification". Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1421/1/PhD_Nicola_Trento.pdf.
Texto completoJia, Xiuping Electrical Engineering Australian Defence Force Academy UNSW. "Classification techniques for hyperspectral remote sensing image data". Awarded by:University of New South Wales - Australian Defence Force Academy. School of Electrical Engineering, 1996. http://handle.unsw.edu.au/1959.4/38713.
Texto completoPrasert, Sunyaruk. "Multi angle imaging with spectral remote sensing for scene classification". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Mar%5FPrasert.pdf.
Texto completoThesis Advisor(s): Richard C. Olsen. Includes bibliographical references (p. 95-97). Also available online.
Alam, Fahim Irfan. "Deep Feature Learning for Spectral-Spatial Classification of Hyperspectral Remote Sensing Images". Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386535.
Texto completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Hoarau, Romain. "Rendu interactif d'image hyper spectrale par illumination globale pour la prédiction de la signature infrarouge d'aéronefs". Electronic Thesis or Diss., Aix-Marseille, 2019. http://theses.univ-amu.fr.lama.univ-amu.fr/191219_HOARAU_358wfqq893efe918esmfu405fjhqvj_TH.pdf.
Texto completoSensor dimensioning is a major issue for the aircraft detection field. In this vein, it is appropriate to simulate these sensorsvia models and a consequent set of spectral images. The acquisition of these images via an airborne measure campaign is unfortunately costly and difficult. A robust and fast simulation of these data is hence very appealing.In order to answer these needs, global illumination methods in high spectral dimension are used. In these circumstances,these methods raise serious issues in term of memory consumption and of computing time. Our research project focuses on these problematics.In the first instance, we have focused on the Path Tracing method and its GPU parallelization for the spectral image rendering. We have investigated at first the issues of this kind of rendering on the GPU. Then we have proposed a new method and an efficient spectral parallelization pattern which allows us to reduce significantly the memory consumption and thecomputing time.In the second phase, we have investigated how to reduce the spectral computational load of the simulation. Inthat sense, we have proposed to generalize the stochastic spectral rendering of color (XYZ) image to the stochastic spectral image rendering. This new method renders directly the channels of a sensor which allows us to reduce the memory andthe computing requirements by reducing the spectral computational load of the simulation.To sum up, the works of this thesis allows us to simulate accurately multi, hyper and ultra spectral images. The interactive time can be achieved in our case in multi and hyper spectral resolution
Behmo, Régis. "Visual feature graphs and image recognition". Phd thesis, Ecole Centrale Paris, 2010. http://tel.archives-ouvertes.fr/tel-00545419.
Texto completoTso, Brandt C. K. "An investigation of alternative strategies for incorporating spectral, textural, and contextual information in remote sensing image classification". Thesis, University of Nottingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387663.
Texto completoRajadell, Rojas Olga. "Data selection and spectral-spatial characterisation for hyperspectral image segmentation. Applications to remote sensing". Doctoral thesis, Universitat Jaume I, 2013. http://hdl.handle.net/10803/669093.
Texto completoLately image analysis have aided many discoveries in research. This thesis focusses on the analysis of remote sensed images for aerial inspection. It tackles the problem of segmentation and classification according to land usage. In this field, the use of hyperspectral images has been the trend followed since the emergence of hyperspectral sensors. This type of images improves the performance of the task but raises some issues. Two of those issues are the dimensionality and the interaction with experts. We propose enhancements overcome them. Efficiency and economic reasons encouraged to start this work. The enhancements introduced in this work allow to tackle segmentation and classification of this type of images using less data, thus increasing the efficiency and enabling the design task specific sensors which are cheaper. Also, our enhacements allow to perform the same task with less expert collaboration which also decreases the costs and accelerates the process.
Libros sobre el tema "HYPER SPECTRAL IMAGE CLASSIFICATION"
Hyperspectral imaging: Techniques for spectral detection and classification. New York: Kluwer Academic/Plenum Publishers, 2003.
Buscar texto completoChang, Chein-I. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Springer, 2003.
Buscar texto completoCapítulos de libros sobre el tema "HYPER SPECTRAL IMAGE CLASSIFICATION"
Priyadharshini @ Manisha, K. y B. Sathya Bama. "Hyper-Spectral Image Classification with Support Vector Machine". En Advances in Automation, Signal Processing, Instrumentation, and Control, 587–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_51.
Texto completoYu, Yi, Yi-Fan Li, Jun-Bao Li, Jeng-Shyang Pan y Wei-Min Zheng. "The Election of Spectrum bands in Hyper-spectral image classification". En Advances in Intelligent Information Hiding and Multimedia Signal Processing, 3–10. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50212-0_1.
Texto completoVaddi, Radhesyam y Prabukumar Manoharan. "Probabilistic PCA Based Hyper Spectral Image Classification for Remote Sensing Applications". En Advances in Intelligent Systems and Computing, 863–69. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16660-1_84.
Texto completoChi, Tao, Yang Wang, Ming Chen y Manman Chen. "Hyper-Spectral Image Classification by Multi-layer Deep Convolutional Neural Networks". En 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.
Texto completoYang, Ming-Der, Kai-Siang Huang, Ji-Yuan Lin y Pei Liu. "Application of Support Vector Machines to Airborne Hyper-Spectral Image Classification". En Lecture Notes in Electrical Engineering, 439–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12990-2_50.
Texto completoGadhave, Rajashree y R. R. Sedamkar. "Automated Classification of Hyper Spectral Image Using Supervised Machine Learning Approach". En Lecture Notes in Electrical Engineering, 763–75. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4831-2_63.
Texto completoPanchal, Soumyashree M. y Shivaputra. "Object Classification from a Hyper Spectral Image Using Spectrum Bands with Wavelength and Feature Set". En Software Engineering and Algorithms, 340–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77442-4_29.
Texto completoVatsavayi, Valli Kumari, Saritha Hepsibha Pilli y Charishma Bobbili. "Performance Analysis of Discrete Wavelets in Hyper Spectral Image Classification: A Deep Learning Approach". En Proceedings of International Conference on Computational Intelligence and Data Engineering, 387–99. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0609-3_27.
Texto completoRaju, Kalidindi Kishore, G. P. Saradhi Varma y Davuluri Rajyalakshmi. "A Comprehensive Review on Effect of Band Selection on the Recital of Hyper-spectral Image Classification". En Lecture Notes in Electrical Engineering, 303–20. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3828-5_33.
Texto completoMei, Zhiming, Long Wang y Cen Guo. "Hyper-spectral Images Classification Based on 3D Convolution Neural Networks for Remote Sensing". En Communications in Computer and Information Science, 205–14. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5937-8_21.
Texto completoActas de conferencias sobre el tema "HYPER SPECTRAL IMAGE CLASSIFICATION"
Shabna, A. y R. Ganesan. "HSEG and PCA for hyper-spectral image classification". En 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). IEEE, 2014. http://dx.doi.org/10.1109/iccicct.2014.6992927.
Texto completoSharma, Sanatan, Akashdeep Goel, Omkar Gune, Biplab Banerjee y Subhasis Chaudhuri. "Class Specific Coders for Hyper-Spectral Image Classification". En 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451637.
Texto completoMahendren, Sutharsan, Tharindu Fernando, Sridha Sridharan, Peyman Moghadam y Clinton Fookes. "Reduction of Feature Contamination for Hyper Spectral Image Classification". En 2021 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2021. http://dx.doi.org/10.1109/dicta52665.2021.9647153.
Texto completoPrigent, Sylvain, Xavier Descombes, Didier Zugaj y Josiane Zerubia. "Spectral analysis and unsupervised SVM classification for skin hyper-pigmentation classification". En 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2010. http://dx.doi.org/10.1109/whispers.2010.5594917.
Texto completoThapliyal, Ankita. "Deep Intensified Archetypical CNN Approach for Hyper Spectral Image Classification". En 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2021. http://dx.doi.org/10.1109/asiancon51346.2021.9544875.
Texto completoThapliyal, Ankita. "Deep Intensified Archetypical CNN Approach for Hyper Spectral Image Classification". En 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2021. http://dx.doi.org/10.1109/asiancon51346.2021.9544875.
Texto completoSawant, Shrutika S. y M. Prabukumar. "Semi-supervised techniques based hyper-spectral image classification: A survey". En 2017 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, 2017. http://dx.doi.org/10.1109/ipact.2017.8244999.
Texto completoSu, Zhenyu y xiuying zhao. "Using deep learning in image hyper spectral segmentation, classification, and detection". En Fourth Seminar on Novel Optoelectronic Detection Technology and Application, editado por Weiqi Jin y Ye Li. SPIE, 2018. http://dx.doi.org/10.1117/12.2307376.
Texto completoMallapragada, Srivatsa y Chih-Cheng Hung. "Statistical Perspective of SOM and CSOM for Hyper-Spectral Image Classification". En IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9324200.
Texto completoUllah, Shan y Deok-Hwan Kim. "Benchmarking Jetson Platform for 3D Point-Cloud and Hyper-Spectral Image Classification". En 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2020. http://dx.doi.org/10.1109/bigcomp48618.2020.00-21.
Texto completoInformes sobre el tema "HYPER SPECTRAL IMAGE CLASSIFICATION"
Guindon, B. Combining Diverse Spectral, Spatial and Contextual Attributes in Segment-Based Image Classification. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219634.
Texto completoBurks, Thomas F., Victor Alchanatis y Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, octubre de 2009. http://dx.doi.org/10.32747/2009.7591739.bard.
Texto completoDelwiche, Michael, Yael Edan y Yoav Sarig. An Inspection System for Sorting Fruit with Machine Vision. United States Department of Agriculture, marzo de 1996. http://dx.doi.org/10.32747/1996.7612831.bard.
Texto completoBonfil, David J., Daniel S. Long y Yafit Cohen. Remote Sensing of Crop Physiological Parameters for Improved Nitrogen Management in Semi-Arid Wheat Production Systems. United States Department of Agriculture, enero de 2008. http://dx.doi.org/10.32747/2008.7696531.bard.
Texto completo