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Relevant bibliographies by topics / Multi-temporal images

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Academic literature on the topic 'Multi-temporal images'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Multi-temporal images"

1

Cheng, K. S., C. Wei, and S. C. Chang. "Locating landslides using multi-temporal satellite images." Advances in Space Research 33, no. 3 (2004): 296–301. http://dx.doi.org/10.1016/s0273-1177(03)00471-x.

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AlMamun, Md, Md Nazrul Islam Mondal, and Boshir Ahmed. "Evaluating Temporal Uncertainty of Multi-temporal Images for Geographical Deviance." International Journal of Computer Applications 103, no. 14 (2014): 14–18. http://dx.doi.org/10.5120/18141-9339.

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Wang, Caiqiong, Lei Zhao, Wangfei Zhang, Xiyun Mu, and Shitao Li. "Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning." PeerJ 10 (January 19, 2022): e12805. http://dx.doi.org/10.7717/peerj.12805.

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Abstract Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other applications. For this, a new algorithm for multi-temporal PolSAR image segmentation is proposed in this paper. Firstly, the over-segmentation of single-temporal PolSAR images is carried out by the mean-shift algorithm, and the over-segmentation results of single-temporal PolSAR are combined to get

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Bu, Lijing, Jiayu Zhang, Zhengpeng Zhang, Yin Yang, and Mingjun Deng. "Enhancing RABASAR for Multi-Temporal SAR Image Despeckling through Directional Filtering and Wavelet Transform." Sensors 23, no. 21 (2023): 8916. http://dx.doi.org/10.3390/s23218916.

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The presence of speckle noise severely hampers the interpretability of synthetic aperture radar (SAR) images. While research on despeckling single-temporal SAR images is well-established, there remains a significant gap in the study of despeckling multi-temporal SAR images. Addressing the limitations in the acquisition of the “superimage” and the generation of ratio images within the RABASAR despeckling framework, this paper proposes an enhanced framework. This enhanced framework proposes a direction-based segmentation approach for multi-temporal SAR non-local means filtering (DSMT-NLM) to obt

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Peng, Mingyuan, Canhai Li, Xiaoqing Zhou, and Guoyuan Li. "A Long-time-series Spatio-Temporal-Spectral Fusion Method via Multi-task Learning." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 567–72. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-567-2024.

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Abstract. Due to the limitations of sensor hardware, clouds and fog, and data transmission limitations, it is difficult for the data obtained by spaceborne remote sensing imager to achieve high temporal, spatial and spectral resolution at the same time, which limits its application in long-time-series high-frequency monitoring. At present, there are several spatio-temporal-spectral algorithms that can realize the fusion of temporal, spatial and spectral resolution, but most of them are based on one to two discrete images, and the integrated fusion at the multi-dimensional level has not yet bee

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Chen, Dan, Xianyun Fei, Jing Li, et al. "Refined Classification of Mountainous Vegetation Based on Multi-Source and Multi-Temporal High-Resolution Images." Forests 16, no. 4 (2025): 707. https://doi.org/10.3390/f16040707.

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Distinguishing vegetation types from satellite images has long been a goal of remote sensing, and the combination of multi-source and multi-temporal remote sensing images for vegetation classification is currently a hot topic in the field. In species-rich mountainous environments, this study selected four remote sensing images from different seasons (two aerial images, one WorldView-2 image, and one UAV image) and proposed a vegetation classification method integrating hierarchical extraction and object-oriented approaches for 11 vegetation types. This method innovatively combines the Random F

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Nguyen Hung An. "AN APPROACH FOR IMPROVING ACCURACY OF CHANGE DETECTION IN MULTI-TEMOPRAL SAR IMAGES." Journal of Military Science and Technology, no. 66A (May 6, 2020): 47–54. http://dx.doi.org/10.54939/1859-1043.j.mst.66a.2020.47-54.

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Algorithms of change detection in multi-temporal SAR images have received great interests for recent decades, and been widely applied in natural resource supervision activities. However, these algorithms still expose the limitation of detection accuracy due to inhenrent presence of speckle noise in SAR images. This paper developed a novel approach of change detection in multi-temporal SAR images of sea surface. The algorithm has increased accuracy of change detection in multi-temporal SAR images of sea surface compared with recent other methods.

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Jia, Junhao, Mingzhong Pan, Yaowei Li, et al. "GLTF-Net: Deep-Learning Network for Thick Cloud Removal of Remote Sensing Images via Global–Local Temporality and Features." Remote Sensing 15, no. 21 (2023): 5145. http://dx.doi.org/10.3390/rs15215145.

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Remote sensing images are very vulnerable to cloud interference during the imaging process. Cloud occlusion, especially thick cloud occlusion, significantly reduces the imaging quality of remote sensing images, which in turn affects a variety of subsequent tasks using the remote sensing images. The remote sensing images miss ground information due to thick cloud occlusion. The thick cloud removal method based on a temporality global–local structure is initially suggested as a solution to this problem. This method includes two stages: the global multi-temporal feature fusion (GMFF) stage and th

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Yuhendra and Eva Yulianti. "Multi-Temporal Sentinel-2 Images for Classification Accuracy." Journal of Computer Science 15, no. 2 (2019): 258–68. http://dx.doi.org/10.3844/jcssp.2019.258.268.

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Arvind, C. S., Ashoka Vanjare, S. N. Omkar, J. Senthilnath, V. Mani, and P. G. Diwakar. "Flood Assessment using Multi-temporal Modis Satellite Images." Procedia Computer Science 89 (2016): 575–86. http://dx.doi.org/10.1016/j.procs.2016.06.017.

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Dissertations / Theses on the topic "Multi-temporal images"

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Shrestha, Bijay. "Parallel compositing of multi-temporal satellite imagery using temporal map algebra." Master's thesis, Mississippi State : Mississippi State University, 2005. http://sun.library.msstate.edu/ETD-db/ETD-browse/browse.

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Sencan, Secil. "Decision Tree Classification Of Multi-temporal Images For Field-based Crop Mapping." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605503/index.pdf.

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ABSTRACT DECISION TREE CLASSIFICATION OF MULTI-TEMPORAL IMAGES FOR FIELD-BASED CROP MAPPING Sencan, Se&ccedil<br>il M. Sc., Department of Geodetic and Geographic Information Technologies Supervisor: Assist. Prof. Dr. Mustafa T&uuml<br>rker August 2004, 125 pages A decision tree (DT) classification approach was used to identify summer (August) crop types in an agricultural area near Karacabey (Bursa), Turkey from multi-temporal images. For the analysis, Landsat 7 ETM+ images acquired in May, July, and August 2000 were used. In addition to the original bands, NDVI, PCA, and Tasselled Cap

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Li, Mao Li. "Spatial-temporal classification enhancement via 3-D iterative filtering for multi-temporal Very-High-Resolution satellite images." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1514939565470669.

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Premier, Valentina. "Development of Novel Approaches to Snow Parameter Retrieval in Alpine Areas by Using Multi-temporal and Multi-sensor Remote Sensing Images." Doctoral thesis, Università degli studi di Trento, 2022. https://hdl.handle.net/11572/356729.

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Snow represents an important resource in mountainous regions. Monitoring its extent and amount is relevant for several applications, such as hydrology, ecology, avalanche monitoring, or hydropower production. However, a correct understanding of the high spatial and temporal variability of snow accumulation, redistribution and ablation processes requires its monitoring in a spatialized and detailed way. Recently, the launch of the Sentinel missions has opened the doors to new approaches that mainly exploit high resolution (HR) data having a spatial detail of few dozens of m. In this thesis, we

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Lobry, Sylvain. "Modèles Markoviens pour les images SAR : application à la détection de l'eau dans les images satellitaires SWOT et analyse multi-temporelle de zones urbaines." Electronic Thesis or Diss., Paris, ENST, 2017. http://www.theses.fr/2017ENST0056.

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Afin d’obtenir une meilleure couverture, à la fois spatiale et temporelle de leurs mesures les hydrologues utilisent des données spatiales en plus de celles acquises sur place. Fruit d’une collaboration entre les agences spatiales française (le CNES) et américaine (JPL, NASA), la future mission SWOT a notamment pour but de fournir des mesures de hauteur des surfaces d’eau continentales en utilisant l’interférométrie radar à synthèse d’ouverture (SAR). Dans cette thèse, nous nous intéressons au problème de la détection de l’eau dans les images d’amplitude SWOT qui est ici un prérequis au traite

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Saha, Sudipan. "Advanced deep learning based multi-temporal remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/263814.

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Multi-temporal image analysis has been widely used in many applications such as urban monitoring, disaster management, and agriculture. With the development of the remote sensing technology, the new generation remote sensing satellite images with High/ Very High spatial resolution (HR/VHR) are now available. Compared to the traditional low/medium spatial resolution images, the detailed information of ground objects can be clearly analyzed in the HR/VHR images. Classical methods of multi-temporal image analysis deal with the images at pixel level and have worked well on low/medium resolution i

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Saha, Sudipan. "Advanced deep learning based multi-temporal remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/263814.

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Multi-temporal image analysis has been widely used in many applications such as urban monitoring, disaster management, and agriculture. With the development of the remote sensing technology, the new generation remote sensing satellite images with High/ Very High spatial resolution (HR/VHR) are now available. Compared to the traditional low/medium spatial resolution images, the detailed information of ground objects can be clearly analyzed in the HR/VHR images. Classical methods of multi-temporal image analysis deal with the images at pixel level and have worked well on low/medium resolution i

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Carroll, Melvyn Joseph. "Applications of statistical change detection to multi-temporal multi-spectral nuclear medicine image data." Thesis, City University London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446320.

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Zhang, Xiaohu, and 张啸虎. "Automatic detection of land cover changes using multi-temporal polarimetric SAR imagery." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/193496.

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Dramatic land-cover changes have occurred in a broad range of spatial and temporal scales over the last decades. Satellite remote sensing, which can observe the earth's surface in a consistent manner, has been playing an important role in monitoring and evaluating land-cover changes. Meanwhile, optical remote sensing, a common approach to acquiring land-cover information, is limited by weather conditions and thus is greatly constrained in areas with frequent cloud cover and rainfall. Recent advances in polarimetric SAR (PolSAR) provide a promising means to extract timely information of land-co

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Pantaleoni, Eva. "Assessing Coastal Plain Wetland Composition using Advanced Spaceborne Thermal Emission and Reflection Radiometer Imagery." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28419.

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Establishing wetland gains and losses, delineating wetland boundaries, and determining their vegetative composition are major challenges that can be improved through remote sensing studies. In this study, we used the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to separate wetlands from uplands in a study of 870 locations on the Virginia Coastal Plain. We used the first five bands from each of two ASTER scenes (6 March 2005 and 16 October 2005), covering the visible to the short-wave infrared region (0.52-2.185υm). We included GIS data layers for soil survey, to

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Books on the topic "Multi-temporal images"

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International Workshop on the Analysis of Multi-temporal Remote Sensing Images (2007 Leuven, Belgium). 2007 International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Leuven, Belgium, 18 - 20 July 2007. IEEE, 2007.

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International Workshop on the Analysis of Multi-temporal Remote Sensing Images (3rd 2005 Biloxi, Miss.). Proceedings of the Third International Workshop on the Analysis of Multi-temporal Remote Sensing Images: Multi Temp 2005, 16-18 May 2005, Beau Rivage Resort and Casino, Biloxi, Mississippi USA. Edited by King Roger L, Younan Nicolas H, and Institute of Electrical and Electronics Engineers. IEEE, 2005.

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Lorenzo, Bruzzone, and Smits Paul, eds. Proceedings of the First International Workshop on the Analysis of Multi-temporal Remote Sensing Images: University of Trento, Italy, 13-14 September 2001. World Scientific, 2002.

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International, Workshop on the Analysis of Multi-Temporal Remote Sensing Images (2nd 2003 Ispra Italy). Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003. World Scientific, 2004.

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United States. National Aeronautics and Space Administration., ed. Temporal evolution of SL-9 impact sites on Jupiter and global maps of Jupiter from multi-observatory visible and infrared images: Period: 1/1/96-12/31/96 : final report on NASA grant NAG-4988. National Aeronautics and Space Administration, Lewis Research Center, 1996.

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United States. National Aeronautics and Space Administration., ed. Temporal evolution of SL-9 impact sites on Jupiter and global maps of Jupiter from multi-observatory visible and infrared images: Period: 1/1/96-12/31/96 : final report on NASA grant NAG-4988. National Aeronautics and Space Administration, Lewis Research Center, 1996.

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United States. National Aeronautics and Space Administration., ed. Temporal evolution of SL-9 impact sites on Jupiter and global maps of Jupiter from multi-observatory visible and infrared images: Period: 1/1/96-12/31/96 : final report on NASA grant NAG-4988. National Aeronautics and Space Administration, Lewis Research Center, 1996.

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US GOVERNMENT. Proceedings of the Third International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multi Temp 2005, 16-18 May 2005, Beau Rivage. Institute of Electrical & Electronics Enginee, 2005.

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Smits, Paul. Analysis of Multi-Temporal Remote Sensing Images: Proceedings of the First International Workshop on Multitemp. World Scientific Publishing Co Pte Ltd, 2002.

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Smits, Paul. Analysis of Multi-Temporal Remote Sensing Images Vol. 2: Proceedings of the First International Workshop on Multitemp 2001. World Scientific Publishing Co Pte Ltd, 2002.

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Book chapters on the topic "Multi-temporal images"

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Kumar, Anil, Priyadarshi Upadhyay, and Uttara Singh. "Remote-Sensing Images." In Multi-Sensor and Multi-Temporal Remote Sensing. CRC Press, 2023. http://dx.doi.org/10.1201/9781003373216-1.

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Yamazaki, Fumio, and Wen Liu. "Urban Change Monitoring: Multi-temporal SAR Images." In Encyclopedia of Earthquake Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-36197-5_227-1.

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Yamazaki, Fumio, and Wen Liu. "Urban Change Monitoring: Multi-temporal SAR Images." In Encyclopedia of Earthquake Engineering. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35344-4_227.

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Chen, Hua-mei, and Pramod K. Varshney. "MI Based Registration of Multi-Sensor and Multi-Temporal Images." In Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05605-9_8.

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Ferretti, Roberta, and Silvana Dellepiane. "Color Spaces in Data Fusion of Multi-temporal Images." In Image Analysis and Processing — ICIAP 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_55.

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Kavats, Olena, Volodymyr Hnatushenko, Yuliya Kibukevych, and Yurii Kavats. "Flood Monitoring Using Multi-temporal Synthetic Aperture Radar Images." In Advances in Intelligent Systems and Computing IV. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33695-0_5.

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Körner, Marco, and Joachim Denzler. "Temporal Self-Similarity for Appearance-Based Action Recognition in Multi-View Setups." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40261-6_19.

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El Bahi, Omaima, Ali Omari Alaoui, Youssef Qaraai, and Ahmad El Allaoui. "Deep Multi-temporal Matching of Satellite Images for Agricultural Dams." In Sustainable and Green Technologies for Water and Environmental Management. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52419-6_5.

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Menze, Bjoern H., and Jason A. Ur. "Multi-Temporal Classification of Multi-Spectral Images for Settlement Survey in Northeastern Syria." In SpringerBriefs in Archaeology. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6074-9_18.

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Xing, Wenpeng, and Jie Chen. "Temporal-MPI: Enabling Multi-plane Images for Dynamic Scene Modelling via Temporal Basis Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19784-0_19.

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Conference papers on the topic "Multi-temporal images"

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Peng, Jiahui, Tao Dapeng, Jun Ni, and Carlos López-Martínez. "Triple Temporal Vision Transformer for the Coverage Classification with Multi-Temporal Polsar Images." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641582.

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Amoros-Lopez, J., L. Gomez-Chova, L. Guanter, L. Alonso, J. Moreno, and G. Camps-Valls. "Multitemporal fusion of Landsat and MERIS images." 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.2011.6005053.

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BUJOR, F. T., E. TROUVÉ, L. VALET, Ph BOLON, J. M. NICOLAS, and J. P. RUDANT. "FEATURE DETECTION IN MULTI-TEMPORAL SAR IMAGES." In Proceedings of the Second International Workshop on the Multitemp 2003. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702630_0004.

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Zillmann, Erik, Horst Weichelt, Enrique Montero Herrero, Thomas Esch, Manfred Keil, and Joeri van Wolvelaer. "Mapping of grassland using seasonal statistics derived from multi-temporal satellite images." In MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2013. http://dx.doi.org/10.1109/multi-temp.2013.6866017.

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Hachicha, Sofiane, Charles-Alban Deledalle, Ferdaous Chaabane, and Florence Tupin. "Multi-temporal SAR classification according to change detection operators." 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.2011.6005066.

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Aiazzi, B., L. Alparone, S. Baronti, A. Garzelli, and C. Zoppetti. "A robust change detection feature for Cosmo-SkyMed detected SAR images." 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.2011.6005064.

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Zhang, Chengyue, Zhiwei Li, Qing Cheng, Xinghua Li, and Huanfeng Shen. "Cloud removal by fusing multi-source and multi-temporal images." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127522.

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Satalino, Giuseppe, Donato Impedovo, Anna Balenzano, and Francesco Mattia. "Land cover classification by using multi-temporal COSMO-SkyMed data." 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.2011.6005036.

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Colditz, Rene R., and Ricardo M. Llamas. "Generation of 250m MODIS LAI time series by temporal regression." 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.2011.6005059.

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Abileah, Ron, and Stefano Vignudelli. "Bathymetry from fusion of multi-temporal Landsat and radar altimetery." 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.2011.6005080.

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Reports on the topic "Multi-temporal images"

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Du, Y., P. M. Teillet, and J. Cihlar. Radiometric Normalization of Multi-temporal High Resolution Satellite Images with Quality Control for Land Cover Change Detection. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219814.

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Keifer, Jarrett. Agricultural Classification of Multi-Temporal MODIS Imagery in Northwest Argentina Using Kansas Crop Phenologies. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2100.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti

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