Littérature scientifique sur le sujet « Multispectral pansharpening »
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Articles de revues sur le sujet "Multispectral pansharpening"
Choi, Jaewan, Honglyun Park et Doochun Seo. « Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery ». Remote Sensing 11, no 6 (15 mars 2019) : 633. http://dx.doi.org/10.3390/rs11060633.
Texte intégralLiu, Junmin, Jing Ma, Rongrong Fei, Huirong Li et Jiangshe Zhang. « Enhanced Back-Projection as Postprocessing for Pansharpening ». Remote Sensing 11, no 6 (25 mars 2019) : 712. http://dx.doi.org/10.3390/rs11060712.
Texte intégralWang, Wenqing, Zhiqiang Zhou, Xiaoqiao Zhang, Tu Lv, Han Liu et Lili Liang. « DiTBN : Detail Injection-Based Two-Branch Network for Pansharpening of Remote Sensing Images ». Remote Sensing 14, no 23 (2 décembre 2022) : 6120. http://dx.doi.org/10.3390/rs14236120.
Texte intégralPérez-Bueno, Fernando, Miguel Vega, Javier Mateos, Rafael Molina et Aggelos K. Katsaggelos. « Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors ». Sensors 20, no 18 (16 septembre 2020) : 5308. http://dx.doi.org/10.3390/s20185308.
Texte intégralHe, Lin, Dahan Xi, Jun Li et Jiawei Zhu. « A Spectral-Aware Convolutional Neural Network for Pansharpening ». Applied Sciences 10, no 17 (22 août 2020) : 5809. http://dx.doi.org/10.3390/app10175809.
Texte intégralGuo, Yecai, Fei Ye et Hao Gong. « Learning an Efficient Convolution Neural Network for Pansharpening ». Algorithms 12, no 1 (8 janvier 2019) : 16. http://dx.doi.org/10.3390/a12010016.
Texte intégralYang, Yong, Wei Tu, Shuying Huang et Hangyuan Lu. « PCDRN : Progressive Cascade Deep Residual Network for Pansharpening ». Remote Sensing 12, no 4 (19 février 2020) : 676. http://dx.doi.org/10.3390/rs12040676.
Texte intégralCao, Xiangyong, Yang Chen et Wenfei Cao. « Proximal PanNet : A Model-Based Deep Network for Pansharpening ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 1 (28 juin 2022) : 176–84. http://dx.doi.org/10.1609/aaai.v36i1.19892.
Texte intégralLi, X. J., H. W. Yan, S. W. Yang, L. Kang et X. M. Lu. « MULTISPECTRAL PANSHARPENING APPROACH USING PULSE-COUPLED NEURAL NETWORK SEGMENTATION ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (30 avril 2018) : 961–65. http://dx.doi.org/10.5194/isprs-archives-xlii-3-961-2018.
Texte intégralLi, Xiaojun, Haowen Yan, Weiying Xie, Lu Kang et Yi Tian. « An Improved Pulse-Coupled Neural Network Model for Pansharpening ». Sensors 20, no 10 (12 mai 2020) : 2764. http://dx.doi.org/10.3390/s20102764.
Texte intégralThèses sur le sujet "Multispectral pansharpening"
Vivone, Gemine. « Multispectral and hyperspectral pansharpening ». Doctoral thesis, Universita degli studi di Salerno, 2014. http://hdl.handle.net/10556/1604.
Texte intégralRemote 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.
Jacq, 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.
Texte intégralThe 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)
Arienzo, Alberto. « Multi-sensor Model-based Data Fusion for Remote Sensing Applications ». Doctoral thesis, 2022. http://hdl.handle.net/2158/1272763.
Texte intégralChapitres de livres sur le sujet "Multispectral pansharpening"
Amro, Israa, et Javier Mateos. « Multispectral Image Pansharpening Based on the Contourlet Transform ». Dans Information Optics and Photonics, 247–61. New York, NY : Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7380-1_20.
Texte intégralLiu, Bin, Weijie Liu et Longxiang Xu. « Construction of Sampling Two-Channel Nonseparable Wavelet Filter Bank and Its Fusion Application for Multispectral Image Pansharpening ». Dans Advances in Multimedia Information Processing – PCM 2017, 859–68. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77383-4_84.
Texte intégral« Pansharpening of Multispectral Images ». Dans Remote Sensing Image Fusion, 169–202. CRC Press, 2015. http://dx.doi.org/10.1201/b18189-14.
Texte intégralActes de conférences sur le sujet "Multispectral pansharpening"
Aly, Hussein A., et Gaurav Sharma. « Joint multichannel pansharpening for multispectral imagery ». Dans ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638036.
Texte intégralKaplan, N. H., et I. Erer. « Bilateral pyramid based pansharpening of multispectral satellite images ». Dans IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6351017.
Texte intégralYao, Hong-Yu, Peng Wang, Xun Shen, Lixin Shi et Chunlei Zhao. « Multispectral Pansharpening Based on High-Pass Modulation Regression ». Dans IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883745.
Texte intégralAkoguz, Alper, Burak Kurt et Sedef K. Pinar. « Pansharpening of multispectral images using filtering in Fourier domain ». Dans SPIE Remote Sensing, sous la direction de Lorenzo Bruzzone, Jon Atli Benediktsson et Francesca Bovolo. SPIE, 2014. http://dx.doi.org/10.1117/12.2067255.
Texte intégralXiao, Shi-Shi, Cheng Jin, Tian-Jing Zhang, Ran Ran et Liang-Jian Deng. « Progressive Band-Separated Convolutional Neural Network for Multispectral Pansharpening ». Dans IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554024.
Texte intégralTarverdiyev, Vazirkhan, Isin Erer, Nur Huseyin Kaplan et Nebiye Musaoglu. « Target Detection in Multispectral Images via Detail Enhanced Pansharpening ». Dans IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884355.
Texte intégralPeng, Siran, Liang-Jian Deng, Jin-Fan Hu et Yuwei Zhuo. « Source-Adaptive Discriminative Kernels based Network for Remote Sensing Pansharpening ». Dans Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California : International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/179.
Texte intégralBendoumi, Mohamed Amine, Tarek Benlefki et Riad Saadi. « Pansharpening Multispectral Images Based on Unconstrained Least Square Spectral Unmixing ». Dans 2018 International Conference on Signal, Image, Vision and their Applications (SIVA ). IEEE, 2018. http://dx.doi.org/10.1109/siva.2018.8660988.
Texte intégralQu, Ying, Hairong Qi, Bulent Ayhan, Chiman Kwan et Richard Kidd. « DOES multispectral / hyperspectral pansharpening improve the performance of anomaly detection ? » Dans 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8128408.
Texte intégralAiazzi, Bruno, Luciano Alparone, Alberto Arienzo, Andrea Garzelli et Simone Lolli. « Fast multispectral pansharpening based on a hyper-ellipsoidal color space ». Dans Image and Signal Processing for Remote Sensing XXV, sous la direction de Lorenzo Bruzzone, Francesca Bovolo et Jon Atli Benediktsson. SPIE, 2019. http://dx.doi.org/10.1117/12.2533481.
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