Literatura científica selecionada sobre o tema "Imagerie Subsalt"
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Artigos de revistas sobre o assunto "Imagerie Subsalt"
Wang, Lin, Hisao-Chi Li, Bai Xue e Chein-I. Chang. "Constrained Band Subset Selection for Hyperspectral Imagery". IEEE Geoscience and Remote Sensing Letters 14, n.º 11 (novembro de 2017): 2032–36. http://dx.doi.org/10.1109/lgrs.2017.2749209.
Texto completo da fonteWang, Lin, Chein-I. Chang, Li-Chien Lee, Yulei Wang, Bai Xue, Meiping Song, Chuanyan Yu e Sen Li. "Band Subset Selection for Anomaly Detection in Hyperspectral Imagery". IEEE Transactions on Geoscience and Remote Sensing 55, n.º 9 (setembro de 2017): 4887–98. http://dx.doi.org/10.1109/tgrs.2017.2681278.
Texto completo da fonteZhao, Yong-Qiang, Lei Zhang e Seong G. Kong. "Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification". IEEE Transactions on Geoscience and Remote Sensing 49, n.º 2 (fevereiro de 2011): 747–56. http://dx.doi.org/10.1109/tgrs.2010.2059707.
Texto completo da fonteMorenikeji, G. B., O. O. Idowu, B. M. Adeleye, O. R. Bankole e T. W. Anjide. "Effects of Population Increase on Peri-Urban Land Growth in Asa Local Government Area, Kwara State". Environmental Technology and Science Journal 14, n.º 1 (1 de agosto de 2023): 180–88. http://dx.doi.org/10.4314/etsj.v14i1.19.
Texto completo da fonteYe, Bei, Shufang Tian, Qiuming Cheng e Yunzhao Ge. "Application of Lithological Mapping Based on Advanced Hyperspectral Imager (AHSI) Imagery Onboard Gaofen-5 (GF-5) Satellite". Remote Sensing 12, n.º 23 (6 de dezembro de 2020): 3990. http://dx.doi.org/10.3390/rs12233990.
Texto completo da fonteWilliams, Sarah E., e Jennifer Cumming. "Measuring Athlete Imagery Ability: The Sport Imagery Ability Questionnaire". Journal of Sport and Exercise Psychology 33, n.º 3 (junho de 2011): 416–40. http://dx.doi.org/10.1123/jsep.33.3.416.
Texto completo da fonteLiu, Yufei, Xiaorun Li, Ziqiang Hua e Liaoying Zhao. "EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection". Remote Sensing 13, n.º 18 (9 de setembro de 2021): 3602. http://dx.doi.org/10.3390/rs13183602.
Texto completo da fonteDi, Wei, Quan Pan, Yong-qiang Zhao e Lin He. "Anomaly Target Detection in Hyperspectral Imagery Based on Band Subset Fusion by Fuzzy Integral". Journal of Electronics & Information Technology 30, n.º 2 (24 de fevereiro de 2011): 267–71. http://dx.doi.org/10.3724/sp.j.1146.2006.01140.
Texto completo da fonteCorlett, John T., John Anton, Steve Kozub e Michel Tardif. "Is Locomotor Distance Estimation Guided by Visual Imagery?" Perceptual and Motor Skills 69, n.º 3_suppl (dezembro de 1989): 1267–72. http://dx.doi.org/10.2466/pms.1989.69.3f.1267.
Texto completo da fonteCorlett, John T., John Anton, Steve Kozub e Michel Tardif. "Is Locomotor Distance Estimation Guided by Visual Imagery?" Perceptual and Motor Skills 69, n.º 3-2 (dezembro de 1989): 1267–72. http://dx.doi.org/10.1177/00315125890693-237.
Texto completo da fonteTeses / dissertações sobre o assunto "Imagerie Subsalt"
Khazraj, Kaoutar. "Paramétrisation hybride champ/objet et inversion full-wave hybride de données sismiques de puits dans un contexte subsalt". Electronic Thesis or Diss., CY Cergy Paris Université, 2024. http://www.theses.fr/2024CYUN1267.
Texto completo da fonteSeismic imaging techniques play a crucial role in the exploration and understanding of subsurface structures. In the field of petroleum exploration, subsalt zones present a challenge for conventional imaging techniques and full-wave inversion (FWI). The application of FWI to seismic well data is expected to overcome these challenges. The primary goal is to characterize hydrocarbon reservoirs that may be located beneath and alongside salt bodies. However, the context of well seismic imaging, combined with the challenges of imaging beneath and around salt bodies, requires the introduction of strong constraints into the geophysical inverse problem due to its underdetermined nature.This thesis presents a three-step approach to tackle these challenges. Firstly, it suggests incorporating extit{a priori} geological information into the inversion process by defining geological objects bounded by discontinuities. Secondly, it aims to formalize and compute the gradient with respect to the geometric parameters that define these discontinuities. Thirdly, it proposes the implementation of a hybrid full-wave inversion algorithm that combines field and object-based approaches. This hybrid FWI utilizes both the gradient of physical fields and the gradient relative to geometric parameters.The thesis content is divided into four distinct chapters. The first chapter introduces the fundamental concepts used in the hybrid FWI algorithm. It highlights the approach based on a dual representation of interfaces (explicit/implicit) using deformable unstructured meshes for the explicit discretization of discontinuities and the level-set method for the implicit representation of the geological objects in the inverse problem. Chapter 2 describes the development steps of a software platform for the numerical implementation of these approaches and the execution of hybrid FWI tests. This software platform includes a wave propagation modeling code based on the spectral elements method and an inversion code based on the gradient computation using the Green's function method, with a probabilistic approach to the inverse problem. The third chapter outlines the various stages of the geometric FWI algorithm and its application to well seismic data to estimate the position of salt/sediment interfaces in 2D environments. Finally, the fourth chapter presents the hybrid inversion algorithm and its implementation with well seismic data to estimate the velocities of compression and shear waves, as well as the position of salt body boundaries in 2D environments. The results of the presented numerical tests are promising, validating our hybrid inversion approach
Lu, Meng-Han, e 呂孟翰. "Band Subset Selection Approaches Based On Sparse Representation for Hyperspectral Imagery". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/un37mu.
Texto completo da fonte國立中山大學
機械與機電工程學系研究所
107
With the advancement of remote sensing technology, the applications of hyperspectral imagery (HSI) are more and more popular. Despite of many success achieved by HSI techniques, there are still some problems to be solved. For instance, HSI data provides huge information which usually contains a lot of redundancy because of its inherent nature. It imposes difficulties for classification because of the curse of dimensionality issue. The enormous data volume of HSI also causes the issues of data storage and long processing time. Therefore, how to select the representative bands from the original image cube without significant loss of information is one of the most important topics in remote sensing society. We call it Band Selection (BS). In this thesis, we combine a new concept, Band Subset Selection (BSS), with self-sparse representation (SSR) model as the objective function, to create an effective BS method for data dimensionality reduction. This method is called self-sparse representation based BSS (SpaBSS). In order to efficiently implement SpaBSS, two iterative algorithms are developed: successive SpaBSS (SC-SpaBSS) and sequential SpaBSS (SQ-SpaBSS). Unlike many existing BS approaches which may only find the locally optimal solution by a single path, the proposed SpaBSS can obtain the nearly globally optimal solution by continuously updating the band subset based on minimizing the reconstruction error of SSR model. The experiments conducted on three real hyperspectral datasets demonstrate that both SpaBSS methods can find appropriate band subsets for effective hyperspectral image classification and endmember extraction.
Capítulos de livros sobre o assunto "Imagerie Subsalt"
Le Bris, Arnaud, Nesrine Chehata, Xavier Briottet e Nicolas Paparoditis. "Spectral Optimization of Airborne Multispectral Camera for Land Cover Classification: Automatic Feature Selection and Spectral Band Clustering". In Geographic Information Systems in Geospatial Intelligence. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.88507.
Texto completo da fonteSaltzstein, Jennifer. "Rural Landscapes and the Pastourelle". In Song, Landscape, and Identity in Medieval Northern France, 163—C5T4. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/oso/9780197547779.003.0006.
Texto completo da fonteKocian, Dean F., e H. Lee Task. "Visually Coupled Systems Hardware and the Human Interface". In Virtual Environments and Advanced Interface Design. Oxford University Press, 1995. http://dx.doi.org/10.1093/oso/9780195075557.003.0014.
Texto completo da fonteDublish, Mani, Dr Anita Pati Mishra, Vinod Kumar e Rahul Kumar. "MORPHOLOGICAL IMAGE PROCESSING AND IMAGE REGISTRATION". In Futuristic Trends in Network & Communication Technologies Volume 2 Book 19, 169–211. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2023. http://dx.doi.org/10.58532/v2bs19p2ch7.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Imagerie Subsalt"
Aldeghlawi, Maher, e Miguel Velez-Reyes. "A comparison of column subset selection methods for hyperspectral band subset selection (Conference Presentation)". In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, editado por David W. Messinger e Miguel Velez-Reyes. SPIE, 2017. http://dx.doi.org/10.1117/12.2264291.
Texto completo da fonteVelez-Reyes, Miguel, e Maher Aldeghlawi. "Using a column subset selection method for endmember extraction in hyperspectral unmixing". In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, editado por David W. Messinger e Miguel Velez-Reyes. SPIE, 2018. http://dx.doi.org/10.1117/12.2309867.
Texto completo da fonteVelez-Reyes, M., e L. O. Jimenez. "Subset selection analysis for the reduction of hyperspectral imagery". In IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). IEEE, 1998. http://dx.doi.org/10.1109/igarss.1998.691622.
Texto completo da fonteAldeghlawi, Maher, e Miguel Velez-Reyes. "A Comparison of Column Subset Selection Methods for Unsupervised Band Subset Selection in Hyperspectral Imagery". In 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). IEEE, 2018. http://dx.doi.org/10.1109/ssiai.2018.8470360.
Texto completo da fonteAbrams, Austin, Emily Feder e Robert Pless. "Exploratory analysis of time-lapse imagery with fast subset PCA". In 2011 IEEE Workshop on Applications of Computer Vision (WACV). IEEE, 2011. http://dx.doi.org/10.1109/wacv.2011.5711523.
Texto completo da fonteWei, Qingguo, Xiaorong Gao e Shangkai Gao. "Feature Extraction and Subset Selection for Classifying Single-Trial ECoG during Motor Imagery". In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.260561.
Texto completo da fonteWei, Qingguo, Xiaorong Gao e Shangkai Gao. "Feature Extraction and Subset Selection for Classifying Single-Trial ECoG during Motor Imagery". In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.4397720.
Texto completo da fonteAlkhatib, Mohammed Q., e Miguel Velez-Reyes. "Using Band Subset Selection For Dimensionality Reduction In Superpixel Segmentation Of Hyperspectral Imagery". In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9190710.
Texto completo da fonteMeng, Jianjun, Guangquan Liu, Gan Huang e Xiangyang Zhu. "Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system". In 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.5420462.
Texto completo da fonteCedillo, G., A. Ortego, C. Ilizaliturri, A. C. Shrivastava, E. Ruiz, A. Kulkarni, J. Wadsworth et al. "Discovery and Integration by Increasing Resolution, Obtaining Borehole Images while Drilling: First Multivendor LWD Success Story from Gulf of Mexico". In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215066-ms.
Texto completo da fonte