Littérature scientifique sur le sujet « 3D Remote Sensing data »
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Articles de revues sur le sujet "3D Remote Sensing data"
Rautji, Surbhi, Deepak Gaur et Karan Khare. « Immersive 3D Visualization of Remote Sensing Data ». Signal & ; Image Processing : An International Journal 4, no 5 (novembre 2013) : 61–73. http://dx.doi.org/10.5121/sipij.2013.4505.
Texte intégralXu, Yuanjin. « Application of Remote Sensing Image Data Scene Generation Method in Smart City ». Complexity 2021 (28 janvier 2021) : 1–13. http://dx.doi.org/10.1155/2021/6653841.
Texte intégralGuo, Xirong, Peng Huang et Wenyi Zhang. « 3D Visualization Management System of Remote Sensing Satellite Data ». Procedia Environmental Sciences 10 (2011) : 1059–64. http://dx.doi.org/10.1016/j.proenv.2011.09.169.
Texte intégralLi, Xiao Jing, Tong Pan, Ting Ting Liu et Hao Peng Wang. « Research on Remote-Sensing Data Syncretizing of Vegetation-Virtual-Reality-Simulation ». Applied Mechanics and Materials 336-338 (juillet 2013) : 1426–29. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1426.
Texte intégralLindberg, Eva, et Johan Holmgren. « Individual Tree Crown Methods for 3D Data from Remote Sensing ». Current Forestry Reports 3, no 1 (7 février 2017) : 19–31. http://dx.doi.org/10.1007/s40725-017-0051-6.
Texte intégralZhang, Haiming, Mingchang Wang, Fengyan Wang, Guodong Yang, Ying Zhang, Junqian Jia et Siqi Wang. « A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data ». Remote Sensing 13, no 3 (27 janvier 2021) : 440. http://dx.doi.org/10.3390/rs13030440.
Texte intégralXuhan, Huijun Yang, Qiufeng Shen, Jiangtao Yang, Huihui Liang, Cancan Bao et Shuang Cang. « Automatic Terrain Debris Recognition Network Based on 3D Remote Sensing Data ». Computers, Materials & ; Continua 65, no 1 (2020) : 579–96. http://dx.doi.org/10.32604/cmc.2020.011262.
Texte intégralSchröter, Kai, Stefan Lüdtke, Richard Redweik, Jessica Meier, Mathias Bochow, Lutz Ross, Claus Nagel et Heidi Kreibich. « Flood loss estimation using 3D city models and remote sensing data ». Environmental Modelling & ; Software 105 (juillet 2018) : 118–31. http://dx.doi.org/10.1016/j.envsoft.2018.03.032.
Texte intégralLatifi, Hooman, et Ruben Valbuena. « Current Trends in Forest Ecological Applications of Three-Dimensional Remote Sensing : Transition from Experimental to Operational Solutions ? » Forests 10, no 10 (9 octobre 2019) : 891. http://dx.doi.org/10.3390/f10100891.
Texte intégralLi Xusheng, 栗旭升, 陈冬花 Chen Donghua, 刘赛赛 Liu Saisai, 张乃明 Zhang Naiming et 李虎 Li Hu. « Tree-Species Identification of Multisource Remote-Sensing Data using Improved 3D-CNN ». Laser & ; Optoelectronics Progress 57, no 24 (2020) : 242804. http://dx.doi.org/10.3788/lop57.242804.
Texte intégralThèses sur le sujet "3D Remote Sensing data"
Alkhadour, Wissam M. « Reconstruction of 3D scenes from pairs of uncalibrated images. Creation of an interactive system for extracting 3D data points and investigation of automatic techniques for generating dense 3D data maps from pairs of uncalibrated images for remote sensing applications ». Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4933.
Texte intégralAl-Baath University
The appendices files and images are not available online.
Dayananda, Supriya [Verfasser]. « Evaluation of remote sensing based spectral and 3D point cloud data for crop biomass estimation in southern India / Supriya Dayananda ». Kassel : Universitätsbibliothek Kassel, 2019. http://d-nb.info/1202727409/34.
Texte intégralAlkhadour, Wissam Mohamad. « Reconstruction of 3D scenes from pairs of uncalibrated images : creation of an interactive system for extracting 3D data points and investigation of automatic techniques for generating dense 3D data maps from pairs of uncalibrated images for remote sensing applications ». Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4933.
Texte intégralKaynak, Burcak. « Assimilation of trace gas retrievals obtained from satellite (SCIAMACHY), aircraft and ground observations into a regional scale air quality model (CMAQ-DDM/3D) ». Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/37134.
Texte intégralMagnini, Luigi. « Remote sensing e object-based image analysis : metodologie di approccio per la creazione di standard archeologici ». Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3423260.
Texte intégralIl campo del remote sensing ha vissuto un incredibile sviluppo negli ultimi anni per merito della crescente qualità e varietà dei sensori e dell’abbattimento dei costi strumentali. Le potenzialità archeologiche sono state ben presto evidenti. Finora, l’interpretazione dei dati è rimasta però prerogativa dell’operatore umano, mediata dalle sue competenze e dalla sua esperienza. Il progressivo aumento di volume dei dataset (cd. “big data explosion”) e la necessità di lavorare su progetti territoriali ad ampia scala hanno reso ora indispensabile una revisione delle modalità di studio tradizionalmente impiegate in ambito archeologico. In questo senso, la ricerca presentata di seguito contribuisce alla valutazione delle potenzialità e dei limiti dell’emergente campo d’indagine dell’object-based image analysis (OBIA). Il lavoro si è focalizzato sulla definizione di protocolli OBIA per il trattamento di dati tridimensionali acquisiti tramite laser scanner aviotrasportato e terrestre attraverso l’elaborazione di un variegato spettro di casi di studio in grado di esemplificare le possibilità offerte dal metodo in archeologia. I risultati ottenuti hanno consentito di identificare, mappare e quantificare in modo automatico e semi-automatico le tracce del paesaggio di guerra nell’area intorno a Forte Luserna (TN) e il tessuto osteologico ricalcificato sui crani di due inumati della necropoli protostorica dell’Olmo di Nogara (VR). Infine, il metodo è stato impiegato per lo sviluppo di un modello predittivo per la localizzazione dei “punti di controllo” in ambiente montano, che è stato studiato per l’area occidentale dell’Altopiano di Asiago (VI) e in seguito riapplicato con successo nella conca di Bressanone (BZ). L’accuratezza dei risultati, verificati di volta in volta tramite ricognizioni a terra, validazione incrociata tramite analisi da remoto e comparazione con i dati editi in letteratura, ha confermato il potenziale della metodologia, consentendo di introdurre il concetto di Archaeological Object-Based Image Analysis (ArchaeOBIA), per rimarcare le specificità delle applicazioni object-based nell’ambito della disciplina archeologica.
GENTILE, VINCENZO. « Integrazione di indagini geofisiche, dati satellitari e tecniche di rilievo 3D presso il sito archeologico di Egnazia ». Doctoral thesis, Università degli studi del Molise, 2017. http://hdl.handle.net/11695/72901.
Texte intégralEgnazia is an important archaeological site located in Puglia on the Adriatic coast between Bari and Brindisi. The oldest human settlement is dated back at the Bronze age (XVI century B.C.). The first urban system was created between the IV and III century B.C. and the typical roman structures were built between the Augustan age and the I century A.C. After the half of the IV century the settlement reduces its size in the old acropolis and it lasts until the XIII century A.C. In the roman city there is a complex road system characterized by a main road that travels through Egnazia towards North West-South East; it separates the public, productive and economic areas from the residential zone and it proceeds in the direction of Brindisi becoming an important point in the organization of the territory. This road has access to secondary axis which join or unite all the sectors of the city. In this thesis the results of a multidisciplinary research are presented. It was carried out with the purpose of understanding the road system of the city through the study of historical and modern maps, the analysis of multispectral, multi-temporal, multi-scalar aerial and satellite images (MIVIS, QuickBird, Google™ earth images), electromagnetic geophysical data and tridimensional survey (laser scanner) of an important structure like the cryptoporticus. The integration of different methodologies has enhanced the probability of success of the research since has provided objective information through the evaluation of diverse parameters describing the same situation. This scientific, technological and innovative multidisciplinary research was transferred and applied in different archaeological sites (the roman city of Doclea (Montenegro), the fortification of Ighram Aousser (Morocco), the archaeological site of Tell El Maskhuta (Egypt), Umm ar-Rasas (Jordan) and Gur (Iran) located in international countries and characterized by different geological and geographical conditions, with the collaboration between Italian and international institution of research.
Qi, Jiaguo. « Compositing multitemporal remote sensing data ». Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186327.
Texte intégralLguensat, Redouane. « Learning from ocean remote sensing data ». Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0050/document.
Texte intégralReconstructing geophysical fields from noisy and partial remote sensing observations is a classical problem well studied in the literature. Data assimilation is one class of popular methods to address this issue, and is done through the use of classical stochastic filtering techniques, such as ensemble Kalman or particle filters and smoothers. They proceed by an online evaluation of the physical modelin order to provide a forecast for the state. Therefore, the performanceof data assimilation heavily relies on the definition of the physical model. In contrast, the amount of observation and simulation data has grown very quickly in the last decades. This thesis focuses on performing data assimilation in a data-driven way and this without having access to explicit model equations. The main contribution of this thesis lies in developing and evaluating the Analog Data Assimilation(AnDA), which combines analog methods (nearest neighbors search) and stochastic filtering methods (Kalman filters, particle filters, Hidden Markov Models). Through applications to both simplified chaotic models and real ocean remote sensing case-studies (sea surface temperature, along-track sea level anomalies), we demonstrate the relevance of AnDA for missing data interpolation of nonlinear and high dimensional dynamical systems from irregularly-sampled and noisy observations. Driven by the rise of machine learning in the recent years, the last part of this thesis is dedicated to the development of deep learning models for the detection and tracking of ocean eddies from multi-source and/or multi-temporal data (e.g., SST-SSH), the general objective being to outperform expert-based approaches
Amrani, Naoufal. « Spectral decorrelation for coding remote sensing data ». Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/402237.
Texte intégralToday remote sensing is essential for many applications addressed to Earth Observation. The potential capability of remote sensing in providing valuable information enables a better understanding of Earth characteristics and human activities. Recent advances in satellite sensors allow recovering large areas, producing images with unprecedented spatial, spectral and temporal resolution. This amount of data implies a need for efficient compression techniques to improve the capabilities of storage and transmissions. Most of these techniques are dominated by transforms or prediction methods. This thesis aims at deeply analyzing the state-of-the-art techniques and at providing efficient solutions that improve the compression of remote sensing data. In order to understand the non-linear independence and data compaction of hyperspectral images, we investigate the improvement of Principal Component Analysis (PCA) that provides optimal independence for Gaussian sources. We analyse the lossless coding efficiency of Principal Polynomial Analysis (PPA), which generalizes PCA by removing non-linear relations among components using polynomial regression. We show that principal components are not able to predict each other through polynomial regression, resulting in no improvement of PCA at the cost of higher complexity and larger amount of side information. This analysis allows us to understand better the concept of prediction in the transform domain for compression purposes. Therefore, rather than using expensive sophisticated transforms like PCA, we focus on theoretically suboptimal but simpler transforms like Discrete Wavelet Transform (DWT). Meanwhile, we adopt predictive techniques to exploit any remaining statistical dependence. Thus, we introduce a novel scheme, called Regression Wavelet Analysis (RWA), to increase the coefficient independence in remote sensing images. The algorithm employs multivariate regression to exploit the relationships among wavelet-transformed components. The proposed RWA has many important advantages, like the low complexity and no dynamic range expansion. Nevertheless, the most important advantage consists of its performance for lossless coding. Extensive experimental results over a wide range of sensors, such as AVIRIS, IASI and Hyperion, indicate that RWA outperforms the most prominent transforms like PCA and wavelets, and also the best recent coding standard, CCSDS-123. We extend the benefits of RWA to progressive lossy-to-lossless. We show that RWA can attain a rate-distortion performance superior to those obtained with the state-of-the-art techniques. To this end, we propose a Prediction Weighting Scheme that captures the prediction significance of each transformed components. The reason of using a weighting strategy is that coefficients with similar magnitude can have extremely different impact on the reconstruction quality. For a deeper analysis, we also investigate the bias in the least squares parameters, when coding with low bitrates. We show that the RWA parameters are unbiased for lossy coding, where the regression models are used not with the original transformed components, but with the recovered ones, which lack some information due to the lossy reconstruction. We show that hyperspectral images with large size in the spectral dimension can be coded via RWA without side information and at a lower computational cost. Finally, we introduce a very low-complexity version of RWA algorithm. Here, the prediction is based on only some few components, while the performance is maintained. When the complexity of RWA is taken to an extremely low level, a careful model selection is necessary. Contrary to expensive selection procedures, we propose a simple and efficient strategy called \textit{neighbor selection} for using small regression models. On a set of well-known and representative hyperspectral images, these small models maintain the excellent coding performance of RWA, while reducing the computational cost by about 90\%.
Wende, Jon T. « Predicting soil strength with remote sensing data ». Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/5174.
Texte intégralPredicting soil strength from hyperspecral imagery enables amphibious planners to determine trafficability in the littorals. Trafficability maps can then be generated and used during the intelligence preparation of the battlespace allowing amphibious planners to select a suitable landing zone. In February and March 2010, the Naval Research Laboratory sponsored a multi-sensor remote sensing and field calibration and field validation campaign (CNMI'10). The team traveled to the islands of Pagan, Tinian, and Guam located in the Marianas archipelago. Airborne hyperspectral imagery along with ground truth data was collected from shallow water lagoons, beachfronts, vegetation, and anomalies such as World War II relics. In this thesis, beachfront hyperspectral data obtained on site was used as a reference library for evaluation against airborne hyperspectral data and ground truth data in order to determine soil strength for creating trafficability maps. Evaluation of the airborne hyperspectral images was accomplished by comparing the reference library spectra to the airborne images. The spectral angle between the reference library and airborne images was calculated producing the trafficability maps amphibious planners can use during the intelligence preparation of the battlespace.
Livres sur le sujet "3D Remote Sensing data"
The application of airborne Lidar data in the modelling of 3D urban landscape ecology. Newcastle upon Tyne : Cambridge Scholars Publishing, 2017.
Trouver le texte intégralR, Oliver William, Society of Photo-optical Instrumentation Engineers. et AIPR Executive Committee., dir. 3D visualization for data exploration and decision making : 28th AIPR Workshop : 13-15 October, 1999, Washington, D.C. Bellingham, Washington : SPIE, 2000.
Trouver le texte intégralWeixi, Wang, dir. Yao gan ying xiang ji he jiu zheng yu san wei chong jian : Geometric correction and 3D reconstruction for remote sensing images. Beijing Shi : Ce hui chu ban she, 2011.
Trouver le texte intégralProcessing of remote sensing data. Lisse : Balkema, 2003.
Trouver le texte intégralGirard, Michel-Claude. Processing of remote sensing data. Lisse : Balkema, 2001.
Trouver le texte intégralThe remote sensing data book. Cambridge : Cambridge University Press, 1999.
Trouver le texte intégralKanellopoulos, Ioannis, Graeme G. Wilkinson, Fabio Roli et James Austin, dir. Neurocomputation in Remote Sensing Data Analysis. Berlin, Heidelberg : Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59041-2.
Texte intégralJ, Qu John, dir. Earth science satellite remote sensing data. Berlin : Springer, 2006.
Trouver le texte intégralJohnson, AI, et CB Pettersson, dir. Geotechnical Applications of Remote Sensing and Remote Data Transmission. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959 : ASTM International, 1988. http://dx.doi.org/10.1520/stp967-eb.
Texte intégralE, Goodison B., International Association of Hydrological Sciences. et International Union of Geodesy and Geophysics., dir. Hydrological applications of remote sensing and remote data transmission. Wallingford : IAHS Press, Institute of Hydrology, 1985.
Trouver le texte intégralChapitres de livres sur le sujet "3D Remote Sensing data"
Baranov, Nikolay. « Algorithms of 3D Wind Field Reconstructing by Lidar Remote Sensing Data ». Dans Lecture Notes in Computer Science, 306–13. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40616-5_24.
Texte intégralLicciardello, Ing Cinzia. « Airborne and Spaceborne Remote Sensing Data Integration for 3D Monitoring of Complex Quarry Environments ». Dans Communications in Computer and Information Science, 405–22. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94426-1_29.
Texte intégralKarantzalos, Konstantinos. « Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data ». Dans Computational Approaches for Urban Environments, 237–72. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11469-9_10.
Texte intégralKhorram, Siamak, Frank H. Koch, Cynthia F. van der Wiele et Stacy A. C. Nelson. « Data Acquisition ». Dans Remote Sensing, 17–37. Boston, MA : Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3103-9_2.
Texte intégralKhorram, Siamak, Frank H. Koch, Cynthia F. van der Wiele et Stacy A. C. Nelson. « Data Processing Tools ». Dans Remote Sensing, 39–62. Boston, MA : Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3103-9_3.
Texte intégralEsbrí, Miguel Ángel. « Remote Sensing ». Dans Big Data in Bioeconomy, 49–61. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_4.
Texte intégralChristophe, Emmanuel. « Hyperspectral Data Compression Tradeoff ». Dans Optical Remote Sensing, 9–29. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14212-3_2.
Texte intégralSturm, B. « CZCS Data Processing Algorithms ». Dans Eurocourses : Remote Sensing, 95–116. Dordrecht : Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1791-3_5.
Texte intégralWeir, Michael J. C. « Data Input and Output ». Dans Eurocourses : Remote Sensing, 301–9. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-017-2879-9_16.
Texte intégralValenzuela, Carlos R. « Data Analysis and Modelling ». Dans Eurocourses : Remote Sensing, 335–48. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-017-2879-9_18.
Texte intégralActes de conférences sur le sujet "3D Remote Sensing data"
Kawata, Yoshiyuki, Satoshi Yoshii, Yukihiro Funatsu et Kazuya Takemata. « 3D campus modeling using LiDAR point cloud data ». Dans SPIE Remote Sensing, sous la direction de Ulrich Michel, Daniel L. Civco, Manfred Ehlers, Karsten Schulz, Konstantinos G. Nikolakopoulos, Shahid Habib, David Messinger et Antonino Maltese. SPIE, 2012. http://dx.doi.org/10.1117/12.973652.
Texte intégralHuang, Bormin, Hung-Lung Huang, Hao Chen, Alok Ahuja, Kevin Baggett, Timothy J. Schmit et Roger W. Heymann. « Data compression studies for NOAA Hyperspectral Environmental Suite (HES) using 3D integer wavelet transforms with 3D set partitioning in hierarchical trees ». Dans Remote Sensing, sous la direction de Lorenzo Bruzzone. SPIE, 2004. http://dx.doi.org/10.1117/12.511437.
Texte intégralAlmer, A., Cliff Banninger, Jose-Luis Fernandez-Turiel et J. F. Llorens. « Automatic 3D information extraction of open-cast mining infrastructure from simulated IKONOS data ». Dans Remote Sensing, sous la direction de Giovanna Cecchi, Edwin T. Engman et Eugenio Zilioli. SPIE, 1999. http://dx.doi.org/10.1117/12.373110.
Texte intégralChen, Zhipeng, Zenrong Lan, Huaping Long et Qingwu Hu. « 3D modeling of pylon from airborne LiDAR data ». Dans Remote Sensing of the Environment : 18th National Symposium on Remote Sensing of China, sous la direction de Qingxi Tong, Jie Shan et Boqin Zhu. SPIE, 2014. http://dx.doi.org/10.1117/12.2063873.
Texte intégralTolt, Gustav, Ulf Soderman et Simon Ahlberg. « 3D Urban Models from Laser Radar Data ». Dans 2007 Urban Remote Sensing Joint Event. IEEE, 2007. http://dx.doi.org/10.1109/urs.2007.371813.
Texte intégralWei, Haitao, Yunyan Du, Chunjin Zhang et Wang Xin. « Remote sensing data parallel processing base on cloud platform ». Dans 2011 International Conference on Photonics, 3D-imaging, and Visualization. SPIE, 2011. http://dx.doi.org/10.1117/12.907562.
Texte intégralTong, Hengjian, Yun Zhang et Zhenfeng Shao. « 3D remote sensing images data organization and web publication ». Dans International Conference on Earth Observation Data Processing and Analysis, sous la direction de Deren Li, Jianya Gong et Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815176.
Texte intégralKawata, Yoshiyuki, et Kohei Koizumi. « A GUI visualization system for airborne lidar image data to reconstruct 3D city model ». Dans SPIE Remote Sensing, sous la direction de Lorenzo Bruzzone. SPIE, 2015. http://dx.doi.org/10.1117/12.2193067.
Texte intégralZhu, Lingli, Juha Hyyppa, Antero Kukko, Anttoni Jaakkola, Matti Lehtomaki, Harri Kaartinen, Ruizhi Chen et al. « 3D city model for mobile phone using MMS data ». Dans 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137691.
Texte intégralBlinov, Alexander B. « Reconstruction of 3D-horizons from 3D-seismic data sets ». Dans Remote Sensing for Environmental Monitoring, GIS Applications, and Geology III. SPIE, 2004. http://dx.doi.org/10.1117/12.513165.
Texte intégralRapports d'organisations sur le sujet "3D Remote Sensing data"
Toutin, Th. Map Making with Remote Sensing Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/219719.
Texte intégralRencz, A. N. Working group 2 - Spatial data integration : remote sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/222363.
Texte intégralJessup, Andrew T., Robert A. Holman et Steve Elgar. DARLA : Data Assimilation and Remote Sensing for Littoral Applications. Fort Belvoir, VA : Defense Technical Information Center, septembre 2012. http://dx.doi.org/10.21236/ada572934.
Texte intégralJessup, Andrew T., Robert A. Holman et Steve Elgar. DARLA : Data Assimilation and Remote Sensing for Littoral Applications. Fort Belvoir, VA : Defense Technical Information Center, septembre 2013. http://dx.doi.org/10.21236/ada598022.
Texte intégralJessup, Andrew T., Chris Chickadel, Gordon Farquharson, Jim Thomson, Robert A. Holman, Merrick Haller, Alexander Kuropov, Tuba Ozkan-Haller, Steve Elgar et Britt Raubenheimer. DARLA : Data Assimilation and Remote Sensing for Littoral Applications. Fort Belvoir, VA : Defense Technical Information Center, septembre 2011. http://dx.doi.org/10.21236/ada557219.
Texte intégralDowns, Christine, Jason Heath et Teeratorn Kadeethum. Persistent homology-based feature detection from remote-sensing data. Office of Scientific and Technical Information (OSTI), septembre 2022. http://dx.doi.org/10.2172/1887492.
Texte intégralM.A. Ebadian, Ph D. REVIEW OF REMOTE SENSING TECHNOLOGIES AND DATA FOR DOE-EM APPLICATIONS. Office of Scientific and Technical Information (OSTI), janvier 1999. http://dx.doi.org/10.2172/772512.
Texte intégralSinghroy, V., J. E. Loehr et A. C. Correa. Landslide Risk Assessment with High Spatial Resolution Remote Sensing Satellite Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219716.
Texte intégralWilliam C. McLendon III et Randy C. Brost. Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs. Office of Scientific and Technical Information (OSTI), mai 2016. http://dx.doi.org/10.2172/1254282.
Texte intégralSaltus, Allen, Maygarden Jr., Saucier Benjamin et Roger T. Analysis and Technical Report of Remote Sensing Data for the USS Kinsman. Fort Belvoir, VA : Defense Technical Information Center, janvier 2000. http://dx.doi.org/10.21236/ada375666.
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