Academic literature on the topic 'Multitemporel'
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Journal articles on the topic "Multitemporel":
Octariady, J., A. Hikmat, E. Widyaningrum, R. Mayasari, and M. K. Fajari. "VERTICAL ACCURACY COMPARISON OF DIGITAL ELEVATION MODEL FROM LIDAR AND MULTITEMPORAL SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 419–23. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-419-2017.
Huang, Liang, Qiuzhi Peng, and Xueqin Yu. "Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering." Journal of Spectroscopy 2020 (March 23, 2020): 1–9. http://dx.doi.org/10.1155/2020/2725186.
Zhang, Xiaokang, Wenzhong Shi, Zhiyong Lv, and Feifei Peng. "Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model." Remote Sensing 11, no. 23 (November 26, 2019): 2787. http://dx.doi.org/10.3390/rs11232787.
Fosbury, Adam M. "Estimation with Multitemporal Measurements." Journal of Guidance, Control, and Dynamics 33, no. 5 (September 2010): 1518–26. http://dx.doi.org/10.2514/1.47984.
Zhu, Wei, Qian Du, and James E. Fowler. "Multitemporal Hyperspectral Image Compression." IEEE Geoscience and Remote Sensing Letters 8, no. 3 (May 2011): 416–20. http://dx.doi.org/10.1109/lgrs.2010.2081661.
Oliveira Soares, Eduardo. "A MULTITEMPORAL VILA ITORORÓ." PIXO - Revista de Arquitetura, Cidade e Contemporaneidade 7, no. 24 (March 23, 2023): 140–51. http://dx.doi.org/10.15210/pixo.v7i24.3220.
Shu, Chang, and Lihui Sun. "Automatic target recognition method for multitemporal remote sensing image." Open Physics 18, no. 1 (June 5, 2020): 170–81. http://dx.doi.org/10.1515/phys-2020-0015.
Gutierrez, Laura, Elías Haro, and Natalia Díaz. "Multitemporal Analysis of Potential Geographic Distribution of Lama Guanicoe." Revista Ciencia y Tecnología 20, no. 1 (March 8, 2024): 89–100. http://dx.doi.org/10.17268/rev.cyt.2024.01.07.
Ilteralp, Melike, Sema Ariman, and Erchan Aptoula. "A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images." Remote Sensing 14, no. 1 (December 22, 2021): 18. http://dx.doi.org/10.3390/rs14010018.
Cheng, Xinglu, Yonghua Sun, Wangkuan Zhang, Yihan Wang, Xuyue Cao, and Yanzhao Wang. "Application of Deep Learning in Multitemporal Remote Sensing Image Classification." Remote Sensing 15, no. 15 (August 3, 2023): 3859. http://dx.doi.org/10.3390/rs15153859.
Dissertations / Theses on the topic "Multitemporel":
Alvarez, padilla Francisco Javier. "AIMM - Analyse d'Images nucléaires dans un contexte Multimodal et Multitemporel." Thesis, Reims, 2019. http://www.theses.fr/2019REIMS017/document.
This work focuses on the proposition of cancerous tumor segmentation strategies in a multimodal and multitemporal context. Multimodal scope refers to coupling PET/CT data in order to jointly exploit both information sources with the purpose of improving segmentation performance. Multitemporal scope refers to the use of images acquired at different dates, which limits a possible spatial correspondence between them.In a first method, a tree is used to process and extract information dedicated to feed a random walker segmentation. A set of region-based attributes is used to characterize tree nodes, filter the tree and then project data into the image space for building a vectorial image. A random walker guided by vectorial tree data on image lattice is used to label voxels for segmentation.The second method is geared toward multitemporality problem by changing voxel-to-voxel for node-to-node paradigm. A tree structure is thus applied to model two hierarchical graphs from PET and contrast-enhanced CT, respectively, and compare attribute distances between their nodes to match those assumed similar whereas discarding the others.In a third method, namely an extension of the first one, the tree is directly involved as the data-structure for algorithm application. A tree structure is built on the PET image, and CT data is then projected onto the tree as contextual information. A node stability algorithm is applied to detect and prune unstable attribute nodes. PET-based seeds are projected into the tree to assign node seed labels (tumor and background) and propagate them by hierarchy. The uncertain nodes, with region-based attributes as descriptors, are involved in a vectorial random walker method to complete tree labeling and build the segmentation
BAPPEL, Eric Albert. "Apport de la teledetection aerospatiale pour l'a ide à la gestion de la sole canniere reunionnaise." Phd thesis, Université de la Réunion, 2005. http://tel.archives-ouvertes.fr/tel-00489730.
Gimenez, Rollin. "Exploitation de données optiques multimodales pour la cartographie des espèces végétales suivant leur sensibilité aux impacts anthropiques." Electronic Thesis or Diss., Toulouse, ISAE, 2023. http://www.theses.fr/2023ESAE0030.
Anthropogenic impacts on vegetated soils are difficult to characterize using optical remote sensing devices. However, these impacts can lead to serious environmental consequences. Their indirect detection is made possible by the induced alterations to biocenosis and plant physiology, which result in optical property changes at plant and canopy levels. The objective of this thesis is to map plant species based on their sensitivity to anthropogenic impacts using multimodal optical remote sensing data. Various anthropogenic impacts associated with past industrial activities are considered (presence of hydrocarbons in the soil, polymetallic chemical contamination, soil reworking and compaction, etc.) in a complex plant context (heterogeneous distribution of multiple species from different strata). Spectral, temporal and/or morphological information is used to identify genera and species and characterise their health status to define and map their sensitivity to the various anthropogenic impacts. Hyperspectral airborne images, Sentinel-2 time series and digital elevation models are then used independently or combined. The proposed scientific approach consists of three stages. The first one involves mapping anthropogenic impacts at site level by combining optical remote sensing data with data supplied by the site operator (soil analyses, activity maps, etc.). The second stage seeks to develop a vegetation mapping method using optical remote sensing data suitable to complex contexts like industrial sites. Finally, the variations in biodiversity and functional response traits derived from airborne hyperspectral images and digital elevation models are analysed in relation to the impact map during the third stage. The species identified as invasive species, as well as those related to agricultural and forestry practices, and biodiversity measures provide information about biological impacts. Vegetation strata mapping and characterisation of tree height, linked to secondary succession, are used to detect physical impacts (soil reworking, excavations). Finally, the consequences of induced stress on the spectral signature of susceptible species allow the identification of chemical impacts. Specifically, in the study context, the spectral signatures of Quercus spp., Alnus glutinosa, and grass mixtures vary with soil acidity, while those of Platanus x hispanica and shrub mixtures exhibit differences due to other chemical impacts
BECCATI, Alan. "Multi-sensor Evolution Analysis: an advanced GIS for interactive time series analysis and modelling based on satellite data." Doctoral thesis, Università degli studi di Ferrara, 2011. http://hdl.handle.net/11392/2388733.
BAPPEL, Eric Albert. "APPORT DE LA TELEDETECTION AEROSPATIALE POUR L'AIDE A LA GESTION DE LA SOLE CANNIERE REUNIONNAISE." Phd thesis, Université de la Réunion, 2005. http://tel.archives-ouvertes.fr/tel-00009403.
Lê, Thu Trang. "Extraction d'informations de changement à partir des séries temporelles d'images radar à synthèse d'ouverture." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAA020/document.
A large number of successfully launched and operated Synthetic Aperture Radar (SAR) satellites has regularly provided multitemporal SAR and polarimetric SAR (PolSAR) images with high and very high spatial resolution over immense areas of the Earth surface. SAR system is appropriate for monitoring tasks thanks to the advantage of operating in all-time and all-weather conditions. With multitemporal data, both spatial and temporal information can simultaneously be exploited to improve the results of researche works. Change detection of specific features within a certain time interval has to deal with a complex processing of SAR data and the so-called speckle which affects the backscattered signal as multiplicative noise.The aim of this thesis is to provide a methodology for simplifying the analysis of multitemporal SAR data. Such methodology can benefit from the advantages of repetitive SAR acquisitions and be able to process different kinds of SAR data (i.e. single, multipolarization SAR, etc.) for various applications. In this thesis, we first propose a general framework based on a spatio-temporal information matrix called emph{Change Detection Matrix} (CDM). This matrix contains temporal neighborhoods which are adaptive to changed and unchanged areas thanks to similarity cross tests. Then, the proposed method is used to perform three different tasks:1) multitemporal change detection with different kinds of changes, which allows the combination of multitemporal pair-wise change maps to improve the performance of change detection result;2) analysis of change dynamics in the observed area, which allows the investigation of temporal evolution of objects of interest;3) nonlocal temporal mean filtering of SAR/PolSAR image time series, which allows us to avoid smoothing change information in the time series during the filtering process.In order to illustrate the relevancy of the proposed method, the experimental works of the thesis is performed on four datasets over two test-sites: Chamonix Mont-Blanc, France and Merapi volcano, Indonesia, with different types of changes (i.e., seasonal evolution, glaciers, volcanic eruption, etc.). Observations of these test-sites are performed on four SAR images time series from single polarization to full polarization, from medium to high, very high spatial resolution: Sentinel-1, ALOS-PALSAR, RADARSAT-2 and TerraSAR-X time series
Lima, Elaine de Cacia de. "Qualidade multitemporal da paisagem." reponame:Repositório Institucional da UFPR, 2013. http://hdl.handle.net/1884/26113.
Qi, Jiaguo. "Compositing multitemporal remote sensing data." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186327.
Yousif, Osama. "Change Detection Using Multitemporal SAR Images." Licentiate thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123494.
QC 20130610
Vicente-Guijalba, Fernando. "Teledetección Multitemporal mediante Dinámica de Sistemas." Doctoral thesis, Universidad de Alicante, 2016. http://hdl.handle.net/10045/57626.
Books on the topic "Multitemporel":
Ban, Yifang, ed. Multitemporal Remote Sensing. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5.
Franco, Rodolfo. Análisis satelital multitemporal de los bosques del Carare-Opón. Bogotá, Colombia: Universidad Distrital Francisco José de Caldas, Centro de Investigaciones y Desarrollo Científico, 2004.
Howell, Signe, and Aud Talle. Returns to the field: Multitemporal research and contemporary anthropology. Bloomington: Indiana University Press, 2012.
Ager, Alan A. Characterizing meadow vegetation with multitemporal landsat thematic mapper remote sensing. Portland, OR]: U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2004.
United States. National Aeronautics and Space Administration., ed. Relating multitemporal meteorological satellite date to climatological data for Africa: Semi-annual report, August 1986 - March 1987. [Washington, D.C: National Aeronautics and Space Administration, 1987.
United States. National Aeronautics and Space Administration, ed. Relating multitemporal meteorological satellite date to climatological data for Africa: Semi-annual report, August 1986 - March 1987. [Washington, D.C: National Aeronautics and Space Administration, 1987.
Heering, Judith M. Multitemporale Luftbildauswertung zur Dokumentation und Analyse der Entwicklung postindustrieller Vegetation am Beispiel des Industriewaldstandortes Rheinelbe. Bochum: Geographisches Institut der Universität Bochum, 2008.
Heering, Judith M. Multitemporale Luftbildauswertung zur Dokumentation und Analyse der Entwicklung postindustrieller Vegetation am Beispiel des Industriewaldstandortes Rheinelbe. Bochum: Geographisches Institut der Universität Bochum, 2008.
Peng, Shikui. On the combination of multitemporal satellite and field data for forest inventories =: Moniaikaisen satelliitti- ja maastoaineiston yhteiskäyttö metsien inventoinnissa. Helsinki: Suomen Metsätieteellinen Seura, 1987.
Ecuador. Subsecretaría de Recursos Pesqueros. Estudio multitemporal de manglares, camaroneras y areas salinas de la costa ecuatoriana, mediante información de sensores remotos (1969-1984): Memoria técnica. Quito, Ecuador: La Subsecretaría, 1986.
Book chapters on the topic "Multitemporel":
Ban, Yifang. "Multitemporal Remote Sensing: Current Status, Trends and Challenges." In Multitemporal Remote Sensing, 1–18. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_1.
Rodrigues, Arlete, André R. S. Marcal, and Mário Cunha. "PhenoSat – A Tool for Remote Sensing Based Analysis of Vegetation Dynamics." In Multitemporal Remote Sensing, 195–215. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_10.
Verger, Aleixandre, Sivasathivel Kandasamy, and Frédéric Baret. "Temporal Techniques in Remote Sensing of Global Vegetation." In Multitemporal Remote Sensing, 217–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_11.
Gumbricht, Thomas. "Soil Moisture Dynamics Estimated from MODIS Time Series Images." In Multitemporal Remote Sensing, 233–53. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_12.
He, Tao, and Shunlin Liang. "Temporal Analysis of Remotely Sensed Land Surface Shortwave Albedo." In Multitemporal Remote Sensing, 255–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_13.
Menenti, Massimo, H. R. Ghafarian Malamiri, Haolu Shang, Silvia M. Alfieri, Carmine Maffei, and Li Jia. "Observing the Response of Terrestrial Vegetation to Climate Variability Across a Range of Time Scales by Time Series Analysis of Land Surface Temperature." In Multitemporal Remote Sensing, 277–315. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_14.
McNairn, Heather, and Jiali Shang. "A Review of Multitemporal Synthetic Aperture Radar (SAR) for Crop Monitoring." In Multitemporal Remote Sensing, 317–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_15.
Brown, Nicholas D. A., Trisalyn Nelson, Michael A. Wulder, Nicholas C. Coops, Thomas Hilker, Christopher W. Bater, Rachel Gaulton, and Gordon B. Stenhouse. "An Approach for Determining Relationships Between Disturbance and Habitat Selection Using Bi-weekly Synthetic Images and Telemetry Data." In Multitemporal Remote Sensing, 341–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_16.
Wang, Yeqiao, Shuhua Qi, and Jian Xu. "Multitemporal Remote Sensing for Inland Water Bodies and Wetland Monitoring." In Multitemporal Remote Sensing, 357–71. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_17.
Cao, Xin, Jun Chen, Anping Liao, Lijun Chen, and Jin Chen. "Global Land Surface Water Mapping and Analysis at 30 m Spatial Resolution for Years 2000 and 2010." In Multitemporal Remote Sensing, 373–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_18.
Conference papers on the topic "Multitemporel":
Jin, Huiran, Peijun Li, and Wenjie Fan. "Land Cover Classification using Multitemporal CHRIS/PROBA Images and Multitemporal Texture." In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4779829.
Dagurov, P. N., A. V. Dmitriev, T. N. Chimitdorzhiev, A. K. Baltukhaev, and I. I. Kirbizhekova. "Backscatter analysis of C-band radar signals using Sentinel-1 multitemporal data (test site near lake Baikal)." In Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.71.20.007.
Sigurdsson, Jakob, Magnus O. Ulfarsson, and Johannes R. Sveinsson. "Fast multitemporal hyperspectral unmixing." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8126933.
Schröder, Daniel, Katharina Anders, Lukas Winiwarter, and Daniel Wujanz. "Permanent terrestrial LiDAR monitoring in mining, natural hazard prevention and infrastructure protection – Chances, risks, and challenges: A case study of a rockfall in Tyrol, Austria." In 5th Joint International Symposium on Deformation Monitoring. Valencia: Editorial de la Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/jisdm2022.2022.13649.
Amitrano, Donato, Francesca Cecinati, Gerardo Di Martino, Antonio Iodice, Daniele Riccio, and Giuseppe Ruello. "Sentinel-1 multitemporal SAR products." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326695.
CARRANZA, MARIA LAURA, ALICIA ACOSTA, and CARLO RICOTTA. "MULTITEMPORAL PHENOLOGICAL CLASSIFICATION OF ARGENTINA." In Proceedings of the First International Workshop on Multitemp 2001. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777249_0026.
Coren, Franco. "Multitemporal Lidar Monitoring of Landslides." In 73rd EAGE Conference and Exhibition - Workshops 2011. Netherlands: EAGE Publications BV, 2011. http://dx.doi.org/10.3997/2214-4609.20144694.
Natali, S., A. Beccati, S. D'Elia, M. G. Veratelli, P. Campalani, M. Folegani, and S. Mantovani. "Multitemporal data management and exploitation infrastructure." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005087.
Kiang, Richard K. "Multitemporal multispectral classification of global vegetation." In BiOS '98 International Biomedical Optics Symposium, edited by Carol J. Cogswell, Jose-Angel Conchello, Jeremy M. Lerner, Thomas T. Lu, and Tony Wilson. SPIE, 1998. http://dx.doi.org/10.1117/12.310564.
Oliver, Christopher J., Ian McConnell, and Douglas G. Corr. "Multitemporal change detection for SAR imagery." In Remote Sensing, edited by Francesco Posa. SPIE, 1999. http://dx.doi.org/10.1117/12.373159.
Reports on the topic "Multitemporel":
McNairn, H., D. Wood, and R. J. Brown. Mapping Crop Characteristics Using Multitemporal RADARSAT Imager. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219318.
Ager, Alan A., and Karen E. Owens. Characterizing meadow vegetation with multitemporal Landsat thematic mapper remote sensing. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2004. http://dx.doi.org/10.2737/pnw-rn-544.
McNairn, H., R. J. Brown, and D. Wood. Incidence Angle Considerations for Crop Mapping Using Multitemporal RADARSAT Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219347.
Groeneveld, Davis, and Williams. L51974 Automated Detection of Encroachment Events Using Satellite Remote Sensing. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2002. http://dx.doi.org/10.55274/r0011300.
Puestow. L52194 Detection of Third Party Encroachment Using Satellite Based Remote Sensing Technologies. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), July 2015. http://dx.doi.org/10.55274/r0011045.
A multitemporal (1979-2009) land-use/land-cover dataset of the binational Santa Cruz Watershed. US Geological Survey, 2011. http://dx.doi.org/10.3133/ofr20111131.