Relevant bibliographies by topics / 3D Remote Sensing data

Academic literature on the topic '3D Remote Sensing data'

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Published: 10 March 2023

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Journal articles on the topic "3D Remote Sensing data"

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Rautji, Surbhi, Deepak Gaur, and Karan Khare. "Immersive 3D Visualization of Remote Sensing Data." Signal & Image Processing : An International Journal 4, no. 5 (November 2013): 61–73. http://dx.doi.org/10.5121/sipij.2013.4505.

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Xu, Yuanjin. "Application of Remote Sensing Image Data Scene Generation Method in Smart City." Complexity 2021 (January 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/6653841.

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Remote sensing image simulation is a very effective method to verify the feasibility of sensor devices for ground observation. The key to remote sensing image application is that simultaneous interpreting of remote sensing images can make use of the different characteristics of different data, eliminate the redundancy and contradiction between different sensors, and improve the timeliness and reliability of remote sensing information extraction. The hotspots and difficulties in this direction are based on remote sensing image simulation of 3D scenes on the ground. Therefore, constructing the 3D scene model on the ground rapidly and accurately is the focus of current research. Because different scenes have different radiation characteristics, therefore, when using MATLAB to write a program generated by 3D scenes, 3D scenes must be saved as different text files according to different scene types, and then extension program of the scene is written to solve the defect that the calculation efficiency is not ideal due to the huge amount of data. This paper uses POV ray photon reverse tracking software to simulate the imaging process of remote sensing sensors, coordinate transformation is used to convert a triangle text file to POV ray readable information and input the RGB value of the base color based on the colorimetry principle, and the final 3D scene is visualized. This paper analyzes the thermal radiation characteristics of the scene and proves the rationality of the scene simulation. The experimental results show that introducing the chroma in the visualization of the scene model makes the whole scene have not only fidelity, but also radiation characteristics in shape and color. This is indispensable in existing 3D modeling and visualization studies. Compared with the complex radiation transmission method, using the multiple angle two-dimensional image generated by POV rays to analyze the radiation characteristics of the scene, the result is intuitive and easy to understand.
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Guo, Xirong, Peng Huang, and 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.

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Li, Xiao Jing, Tong Pan, Ting Ting Liu, and Hao Peng Wang. "Research on Remote-Sensing Data Syncretizing of Vegetation-Virtual-Reality-Simulation." Applied Mechanics and Materials 336-338 (July 2013): 1426–29. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1426.

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Discussed the key effects and basic principles of data fusion of remote sensing for realizing plants virtual reality simulation. Focused on researching and presenting the general methods of data fusion of remote sensing, application interfaces, and 3D visual display of virtual plants. Through the research, the visual display of visual plants will be realized with full remote sensing interfaces of real-time transferring and intervening.
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Lindberg, Eva, and Johan Holmgren. "Individual Tree Crown Methods for 3D Data from Remote Sensing." Current Forestry Reports 3, no. 1 (February 7, 2017): 19–31. http://dx.doi.org/10.1007/s40725-017-0051-6.

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Zhang, Haiming, Mingchang Wang, Fengyan Wang, Guodong Yang, Ying Zhang, Junqian Jia, and 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 (January 27, 2021): 440. http://dx.doi.org/10.3390/rs13030440.

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Building Change Detection (BCD) is one of the core issues in earth observation and has received extensive attention in recent years. With the rapid development of earth observation technology, the data source of remote sensing change detection is continuously enriched, which provides the possibility to describe the spatial details of the ground objects more finely and to characterize the ground objects with multiple perspectives and levels. However, due to the different physical mechanisms of multi-source remote sensing data, BCD based on heterogeneous data is a challenge. Previous studies mostly focused on the BCD of homogeneous remote sensing data, while the use of multi-source remote sensing data and considering multiple features to conduct 2D and 3D BCD research is sporadic. In this article, we propose a novel and general squeeze-and-excitation W-Net, which is developed from U-Net and SE-Net. Its unique advantage is that it can not only be used for BCD of homogeneous and heterogeneous remote sensing data respectively but also can input both homogeneous and heterogeneous remote sensing data for 2D or 3D BCD by relying on its bidirectional symmetric end-to-end network architecture. Moreover, from a unique perspective, we use image features that are stable in performance and less affected by radiation differences and temporal changes. We innovatively introduced the squeeze-and-excitation module to explicitly model the interdependence between feature channels so that the response between the feature channels is adaptively recalibrated to improve the information mining ability and detection accuracy of the model. As far as we know, this is the first proposed network architecture that can simultaneously use multi-source and multi-feature remote sensing data for 2D and 3D BCD. The experimental results in two 2D data sets and two challenging 3D data sets demonstrate that the promising performances of the squeeze-and-excitation W-Net outperform several traditional and state-of-the-art approaches. Moreover, both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed network. This demonstrates that the proposed network and method are practical, physically justified, and have great potential application value in large-scale 2D and 3D BCD and qualitative and quantitative research.
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Xuhan, Huijun Yang, Qiufeng Shen, Jiangtao Yang, Huihui Liang, Cancan Bao, and 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.

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Schröter, Kai, Stefan Lüdtke, Richard Redweik, Jessica Meier, Mathias Bochow, Lutz Ross, Claus Nagel, and Heidi Kreibich. "Flood loss estimation using 3D city models and remote sensing data." Environmental Modelling & Software 105 (July 2018): 118–31. http://dx.doi.org/10.1016/j.envsoft.2018.03.032.

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Latifi, Hooman, and Ruben Valbuena. "Current Trends in Forest Ecological Applications of Three-Dimensional Remote Sensing: Transition from Experimental to Operational Solutions?" Forests 10, no. 10 (October 9, 2019): 891. http://dx.doi.org/10.3390/f10100891.

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The alarming increase in the magnitude and spatiotemporal patterns of changes in composition, structure and function of forest ecosystems during recent years calls for enhanced cross-border mitigation and adaption measures, which strongly entail intensified research to understand the underlying processes in the ecosystems as well as their dynamics. Remote sensing data and methods are nowadays the main complementary sources of synoptic, up-to-date and objective information to support field observations in forest ecology. In particular, analysis of three-dimensional (3D) remote sensing data is regarded as an appropriate complement, since they are hypothesized to resemble the 3D character of most forest attributes. Following their use in various small-scale forest structural analyses over the past two decades, these sources of data are now on their way to be integrated in novel applications in fields like citizen science, environmental impact assessment, forest fire analysis, and biodiversity assessment in remote areas. These and a number of other novel applications provide valuable material for the Forests special issue “3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function”, which shows the promising future of these technologies and improves our understanding of the potentials and challenges of 3D remote sensing in practical forest ecology worldwide.
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Li Xusheng, 栗旭升, 陈冬花 Chen Donghua, 刘赛赛 Liu Saisai, 张乃明 Zhang Naiming, and 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.

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Dissertations / Theses on the topic "3D Remote Sensing data"

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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.

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Much research effort has been devoted to producing algorithms that contribute directly or indirectly to the extraction of 3D information from a wide variety of types of scenes and conditions of image capture. The research work presented in this thesis is aimed at three distinct applications in this area: interactively extracting 3D points from a pair of uncalibrated images in a flexible way; finding corresponding points automatically in high resolution images, particularly those of archaeological scenes captured from a freely moving light aircraft; and improving a correlation approach to dense disparity mapping leading to 3D surface reconstructions. The fundamental concepts required to describe the principles of stereo vision, the camera models, and the epipolar geometry described by the fundamental matrix are introduced, followed by a detailed literature review of existing methods. An interactive system for viewing a scene via a monochrome or colour anaglyph is presented which allows the user to choose the level of compromise between amount of colour and ghosting perceived by controlling colour saturation, and to choose the depth plane of interest. An improved method of extracting 3D coordinates from disparity values when there is significant error is presented. Interactive methods, while very flexible, require significant effort from the user finding and fusing corresponding points and the thesis continues by presenting several variants of existing scale invariant feature transform methods to automatically find correspondences in uncalibrated high resolution aerial images with improved speed and memory requirements. In addition, a contribution to estimating lens distortion correction by a Levenberg Marquard based method is presented; generating data strings for straight lines which are essential input for estimating lens distortion correction. The remainder of the thesis presents correlation based methods for generating dense disparity maps based on single and multiple image rectifications using sets of automatically found correspondences and demonstrates improvements obtained using the latter method. Some example views of point clouds for 3D surfaces produced from pairs of uncalibrated images using the methods presented in the thesis are included.
Al-Baath University
The appendices files and images are not available online.
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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.

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Alkhadour, 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.

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Much research effort has been devoted to producing algorithms that contribute directly or indirectly to the extraction of 3D information from a wide variety of types of scenes and conditions of image capture. The research work presented in this thesis is aimed at three distinct applications in this area: interactively extracting 3D points from a pair of uncalibrated images in a flexible way; finding corresponding points automatically in high resolution images, particularly those of archaeological scenes captured from a freely moving light aircraft; and improving a correlation approach to dense disparity mapping leading to 3D surface reconstructions. The fundamental concepts required to describe the principles of stereo vision, the camera models, and the epipolar geometry described by the fundamental matrix are introduced, followed by a detailed literature review of existing methods. An interactive system for viewing a scene via a monochrome or colour anaglyph is presented which allows the user to choose the level of compromise between amount of colour and ghosting perceived by controlling colour saturation, and to choose the depth plane of interest. An improved method of extracting 3D coordinates from disparity values when there is significant error is presented. Interactive methods, while very flexible, require significant effort from the user finding and fusing corresponding points and the thesis continues by presenting several variants of existing scale invariant feature transform methods to automatically find correspondences in uncalibrated high resolution aerial images with improved speed and memory requirements. In addition, a contribution to estimating lens distortion correction by a Levenberg Marquard based method is presented; generating data strings for straight lines which are essential input for estimating lens distortion correction. The remainder of the thesis presents correlation based methods for generating dense disparity maps based on single and multiple image rectifications using sets of automatically found correspondences and demonstrates improvements obtained using the latter method. Some example views of point clouds for 3D surfaces produced from pairs of uncalibrated images using the methods presented in the thesis are included.
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Kaynak, 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.

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A major opportunity for using satellite observations of tropospheric chemical concentrations is to improve our scientific understanding of atmospheric processes by integrated analysis of satellite, aircraft, and ground-based observations with global and regional scale models. One endpoint of such efforts is to reduce modeling biases and uncertainties. The idea of coupling these observations with a regional scale air quality model was the starting point of this research. The overall objective of this research was to improve the NOₓ emission inventories by integrating observations from different platforms and regional air quality modeling. Specific objectives were: 1) Comparison of satellite NO₂ retrievals with simulated NO₂ by the regional air quality model. Comparison of simulated tropospheric gas concentrations simulated by the regional air quality model, with aircraft and ground-based observations; 3) Assessment of the uncertainties in comparing satellite NO₂ retrievals with NOₓ emissions estimates and model simulations; 4) Identification of biases in emission inventories by data assimilation of satellite NO₂ retrievals, and ground-based NO, NO₂ and O₃ observations with an iterative inverse method using the regional air quality model coupled with sensitivity calculations; 5) Improvement of our understanding of NOₓ emissions, and the interaction between regional and global air pollution by an integrated analysis of satellite NO₂ retrievals with the regional air quality model. Along with these objectives, a lightning NOₓ emission inventory was prepared for two months of summer 2004 to account for a significant upper level NOₓ source. Spatially-resolved weekly NO₂ variations from satellite retrievals were compared with estimated NOₓ emissions for different region types. Data assimilation of satellite NO₂ retrievals, and ground-based NO, NO₂ and O₃ observations were performed to evaluate the NOₓ emission inventory. This research contributes to a better understanding of the use of satellite NO₂ retrievals in air quality modeling, and improvements in the NOₓ emission inventories by correcting some of the inconsistencies that were found in the inventories. Therefore, it may provide groups that develop emissions estimates guidance on areas for improvement. In addition, this research indicates the weaknesses and the strengths of the satellite NO₂ retrievals and offers suggestions to improve the quality of the retrievals for further use in the tropospheric air pollution research.
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Magnini, 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.

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In recent years, the field of remote sensing experienced an incredible growth thanks to the increasing quality and variety of sensors and the reduction of instrumental costs. The benefits for archaeology were soon apparent. So far, data interpretation remains essentially a prerogative of the human operator and is mediated by his skills and experiences. The continuous increase of datasets volume, i.e. the Big Data Explosion, and the increasing necessity to work on large scale projects require an overall revision of the methods traditionally used in archeology. In this sense, the research presented hereinafter contributes to assess the limits and potential of the emerging field of object-based image analysis (OBIA). The work focused on the definition of OBIA protocols for the treatment of three-dimensional data acquired by airborne and terrestrial laser scanning through the development of a wide range of case studies, used to illustrate the possibilities of the method in archeology. The results include a new, automated approach to identify, map and quantify traces of the First World War landscape around Fort Lusern (Province of Trento, Italy) and the recalcified osteological tissue on the skulls of two burials in the protohistoric necropolis of Olmo di Nogara (Province of Verona, Italy). Moreover, the method was employed to create a predictive model to locate “control places” in mountainous environments; the simulation was built for the Western Asiago Plateau (Province of Vicenza, Italy) and then re-applied with success in basin of Bressanone (Province of Bolzano, Italy). The accuracy of the results was verified thanks to respectively ground surveys, remote cross-validation and comparison with published literature. This confirmed the potential of the methodology, giving reasons to introduce the concept of Archaeological Object-Based Image Analysis (ArchaeOBIA), used to highlight the role of object-based applications in archaeology.
Il 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.
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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.

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Egnazia è un importante sito archeologico localizzato in Puglia sul litorale adriatico tra Bari e Brindisi. La frequentazione più antica del territorio è datata all’età del Bronzo (XVI secolo a.C.). Il centro si dota della prima sistemazione di tipo urbano e fra l’età di Augusto e il I secolo d.C. si realizzano gli spazi e le strutture tipiche di una città romana. Alla fine del VI secolo l’abitato torna a restringersi entro l’antica acropoli e perdura fino al XIII secolo. All’interno del sito è presente un articolato sistema viario caratterizzato da un’arteria principale, la via Traiana, che attraversa Egnazia in direzione Nord Ovest-Sud Est, separa le aree pubbliche dagli spazi residenziali e produttivi e prosegue alla volta di Brindisi, divenendo un asse di rilievo anche nell’organizzazione del territorio. Da quest’ultima si snodano degli assi viari secondari con la funzione di collegare tutti i settori della città. In questa tesi vengono presentati i risultati di una ricerca multidisciplinare effettuata con lo scopo di comprendere l’articolato sistema viario della città tramite lo studio integrato di mappe storiche e moderne, l’analisi di immagini aeree e satellitari multispettrali, multi temporali, multi scalari (MIVIS, QuickBird e Google Earth), prospezioni geofisiche elettromagnetiche e rilievi tridimensionali (laser scanner) di un importante struttura quale il criptoportico. L’integrazione di più metodologie ha aumentato la probabilità di successo della ricerca poiché ha fornito informazioni oggettive tramite la valutazione della convergenza di più parametri che descrivono la stessa situazione. Questo tipo di approccio, scientifico, tecnologico ed innovativo, grazie alla collaborazione con diversi istituti di ricerca nazionali ed internazionali, è stato trasferito ed applicato infine in numerosi contesti archeologici localizzati in territorio internazionale (città romana di Doclea (Montenegro), la Fortezza di Ighram Aousser (Marocco) e i siti archeologici di Tell El Maskhuta (Egitto), Umm ar-Rasas (Giordania) e Gur (Iran)) caratterizzati da peculiari condizioni geologiche e geografiche.
Egnazia 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.
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Qi, Jiaguo. "Compositing multitemporal remote sensing data." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186327.

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In order to reduce the problems of clouds, atmospheric variations, view angle effects, and the soil background variations in the high temporal frequency AVHRR data, a compositing technique is usually employed. Current compositing techniques use a single pixel selection criterion of outputting the input pixel of maximum value NDVI. Problems, however, exist due to the use of the NDVI classifier and to the imperfection of the pixel selection criteria of the algorithm itself. The NDVI was found not to have the maximum value under an ideal observation condition, while the single pixel selection criterion favors the large off-nadir sensor view angles. Consequently, the composited data still consist of substantial noise. To further reduce the noise, several data sets were obtained to study these external factor effects on the NDVI classifier and other vegetation indices. On the basis of the studies of these external factors, a new classifier was developed to further reduce the soil noise. Then, a new set of pixel selection criteria was proposed for compositing. The new compositing algorithm with the new classifier was used to composite two AVHRR data sets. The alternative approach showed that the high frequency noises were greatly reduced, while more valuable data were retained. The proposed alternative compositing algorithm not only further reduced the external factor related noises, but also retained more valuable data. In this dissertation, studies of external factor effects on remote sensing data and derived vegetation indices are presented in the first four chapters. Then the development of the new classifier and the alternative compositing algorithm were described. Perspectives and limitations of the proposed algorithms are also discussed.
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Lguensat, 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.

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Reconstruire des champs géophysiques à partir d'observations bruitées et partielles est un problème classique bien étudié dans la littérature. L'assimilation de données est une méthode populaire pour aborder ce problème, et se fait par l'utilisation de techniques classiques, comme le filtrage de Kalman d’ensemble ou des filtres particulaires qui procèdent à une évaluation online du modèle physique afin de fournir une prévision de l'état. La performance de l'assimilation de données dépend alors fortement de du modèle physique. En revanche, la quantité de données d'observation et de simulation a augmenté rapidement au cours des dernières années. Cette thèse traite l'assimilation de données d'une manière data-driven et ce, sans avoir accès aux équations explicites du modèle. Nous avons développé et évalué l'assimilation des données par analogues (AnDA), qui combine la méthode des analogues et des méthodes de filtrage stochastiques (filtres Kalman, filtres à particules, chaînes de Markov cachées). Des applications aux modèles chaotiques simplifiés et à des études de cas de télédétection réelle (température de surface de lamer, anomalies du niveau de la mer), nous démontrons la pertinence d'AnDA pour l'interpolation de données manquantes des systèmes dynamiques non linéaires et à haute dimension à partir d'observations irrégulières et bruyantes.Motivé par l'essor du machine learning récemment, la dernière partie de cette thèse est consacrée à l'élaboration de modèles deep learning pour la détection et de tourbillons océaniques à partir de données de sources multiples et/ou multi temporelles (ex: SST-SSH), l'objectif général étant de surpasser les approches dites expertes
Reconstructing 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
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Amrani, Naoufal. "Spectral decorrelation for coding remote sensing data." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/402237.

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Hoy en día, los datos de teledetección son esenciales para muchas aplicaciones dirigidas a la observación de la tierra. El potencial de los datos de teledetección en ofrecer información valiosa permite entender mejor las características de la tierra y las actividades humanas. Los desarrollos recientes en los sensores de satélites permiten cubrir amplias áreas geográficas, produciendo imágenes con resoluciones espaciales, espectrales y temporales sin precedentes. Esta cantidad de datos producidos implica una necesidad requiere técnicas de compresión eficientes para mejorar la transmisión y la capacidad de almacenamiento. La mayoría de estas técnicas se basan en las transformadas o en los métodos de predicción. Con el fin de entender la independencia no lineal y la compactación de datos para las imágenes hiperespectrales, empezaos por investigar la mejora de la transformada “Principa Component Analysis” (PCA) que proporciona una decorrelación optima para fuentes Gausianas. Analizamos la eficiencia en compresión sin perdida de “Principal Polynomial Analysis” (PPA) que generaliza PCA con la eliminación de las dependencias non lineales a través de regresión polinomial. Mostramos que las componentes principales no son capaces de predecirse con la regresión polinomial y por tanto no se mejora la independencia del PCA. Este análisis nos permite entender mejor el concepto de la predicción en el dominio de la transformada para fines de compresión. Por tanto, en lugar de utilizar transformadas sofisticadas y costosas como PCA, centramos nuestro interés en transformadas más simples como “CDiscrete Wavelet Transform”(DWT). Mientras tanto, adoptamos técnicas de predicción para explotar cualquier dependencia restante entre las componentes transformadas. Así, introducimos un nuevo esquema llamado “Regression Wavelet Analysis” (RWA) para aumentar la independencia entre los coeficientes de las imágenes hiperespectrales. El algoritmo utiliza la regresión multivariante para explotar las relaciones entre los coeficientes de las transformada DWT. El algoritmo RWA ofrece muchas ventajas, como el bajo coste computacional y la no expansión del rango dinámico. Sin embargo, la propiedad más importante es la eficiencia en compresión sin perdida. Experimentaos extensivos sobre un conjunto amplio de imanes indican que RWA supera las técnicas mas competitivas en el estado del arte com. PCA o el estándar CCSDS-123. Extendemos los beneficios de RWA para la compresión progresiva “ Lossy-to-lossless “. Mostramos que RWA puede alcanzar una relación rate-distorsión mejor que las obtenidas por otras técnicas del estado del arte como PCA. Para este fin, proponemos un esquema de pesos que captura la significancia predictiva de las componentes. Para un análisis más profundo, también analizamos el sesgo en los parámetros de regresión cuando se aplica una compresión con perdida. Mostramos que los parámetros de RWA no son sesgados cuando los modelos de regresión se aplican con los datos recuperados que carecen información. Finalmente, introducimos una versión del algoritmo RWA de muy bajo coste computacional. Con este nuevo enfoque, la predicción solo se basa en muy pocas componentes, mientras que el rendimiento se mantiene. Mientras que la complejidad de RWA se lleva a su bajo extremo, un método de selección eficiente es necesario. A diferencia de otros métodos de selección costosos, proponemos una estrategia simple pero eficiente llamada “ neighbor selection” para seleccionar modelos con pocas componentes predictivas. Sobre un amplio conjunto de imágenes hiperespectrales, estos modelos mantienen el excelente rendimiento de RWA con el modelo máximo, mientras que el coste computacional es reducido al
Today 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\%.
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Wende, Jon T. "Predicting soil strength with remote sensing data." Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/5174.

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Predicting 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.
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Books on the topic "3D Remote Sensing data"

1

The application of airborne Lidar data in the modelling of 3D urban landscape ecology. Newcastle upon Tyne: Cambridge Scholars Publishing, 2017.

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R, Oliver William, Society of Photo-optical Instrumentation Engineers., and AIPR Executive Committee., eds. 3D visualization for data exploration and decision making: 28th AIPR Workshop : 13-15 October, 1999, Washington, D.C. Bellingham, Washington: SPIE, 2000.

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Weixi, Wang, ed. 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.

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Processing of remote sensing data. Lisse: Balkema, 2003.

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Girard, Michel-Claude. Processing of remote sensing data. Lisse: Balkema, 2001.

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The remote sensing data book. Cambridge: Cambridge University Press, 1999.

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Kanellopoulos, Ioannis, Graeme G. Wilkinson, Fabio Roli, and James Austin, eds. Neurocomputation in Remote Sensing Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59041-2.

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J, Qu John, ed. Earth science satellite remote sensing data. Berlin: Springer, 2006.

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Johnson, AI, and CB Pettersson, eds. 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.

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E, Goodison B., International Association of Hydrological Sciences., and International Union of Geodesy and Geophysics., eds. Hydrological applications of remote sensing and remote data transmission. Wallingford: IAHS Press, Institute of Hydrology, 1985.

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Book chapters on the topic "3D Remote Sensing data"

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Baranov, Nikolay. "Algorithms of 3D Wind Field Reconstructing by Lidar Remote Sensing Data." In Lecture Notes in Computer Science, 306–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40616-5_24.

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Licciardello, Ing Cinzia. "Airborne and Spaceborne Remote Sensing Data Integration for 3D Monitoring of Complex Quarry Environments." In 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.

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Karantzalos, Konstantinos. "Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data." In Computational Approaches for Urban Environments, 237–72. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11469-9_10.

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Khorram, Siamak, Frank H. Koch, Cynthia F. van der Wiele, and Stacy A. C. Nelson. "Data Acquisition." In Remote Sensing, 17–37. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3103-9_2.

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Khorram, Siamak, Frank H. Koch, Cynthia F. van der Wiele, and Stacy A. C. Nelson. "Data Processing Tools." In Remote Sensing, 39–62. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3103-9_3.

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Esbrí, Miguel Ángel. "Remote Sensing." In Big Data in Bioeconomy, 49–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_4.

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AbstractIn this chapter we present the concepts of remote sensing and Earth Observation and, explain why several of their characteristics (volume, variety and velocity) make us consider Earth Observation as Big Data. Thereafter, we discuss the most commonly open data formats used to store and share the data. The main sources of Earth Observation data are also described, with particular focus on the constellation of Sentinel satellites, Copernicus Hub and its six thematic services, as well as other private initiatives like the five Copernicus-related Data and Information Access Services and Sentinel Hub. Next, we present an overview of representative software technologies for efficiently describing, storing, querying and accessing Earth Observation datasets. The chapter concludes with a summary of the Earth Observation datasets used in each DataBio pilot.
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Christophe, Emmanuel. "Hyperspectral Data Compression Tradeoff." In Optical Remote Sensing, 9–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14212-3_2.

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Sturm, B. "CZCS Data Processing Algorithms." In Eurocourses: Remote Sensing, 95–116. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1791-3_5.

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Weir, Michael J. C. "Data Input and Output." In Eurocourses: Remote Sensing, 301–9. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-017-2879-9_16.

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Valenzuela, Carlos R. "Data Analysis and Modelling." In Eurocourses: Remote Sensing, 335–48. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-017-2879-9_18.

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Conference papers on the topic "3D Remote Sensing data"

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Kawata, Yoshiyuki, Satoshi Yoshii, Yukihiro Funatsu, and Kazuya Takemata. "3D campus modeling using LiDAR point cloud data." In SPIE Remote Sensing, edited by Ulrich Michel, Daniel L. Civco, Manfred Ehlers, Karsten Schulz, Konstantinos G. Nikolakopoulos, Shahid Habib, David Messinger, and Antonino Maltese. SPIE, 2012. http://dx.doi.org/10.1117/12.973652.

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Huang, Bormin, Hung-Lung Huang, Hao Chen, Alok Ahuja, Kevin Baggett, Timothy J. Schmit, and Roger W. Heymann. "Data compression studies for NOAA Hyperspectral Environmental Suite (HES) using 3D integer wavelet transforms with 3D set partitioning in hierarchical trees." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2004. http://dx.doi.org/10.1117/12.511437.

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Almer, A., Cliff Banninger, Jose-Luis Fernandez-Turiel, and J. F. Llorens. "Automatic 3D information extraction of open-cast mining infrastructure from simulated IKONOS data." In Remote Sensing, edited by Giovanna Cecchi, Edwin T. Engman, and Eugenio Zilioli. SPIE, 1999. http://dx.doi.org/10.1117/12.373110.

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Chen, Zhipeng, Zenrong Lan, Huaping Long, and Qingwu Hu. "3D modeling of pylon from airborne LiDAR data." In Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, edited by Qingxi Tong, Jie Shan, and Boqin Zhu. SPIE, 2014. http://dx.doi.org/10.1117/12.2063873.

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Tolt, Gustav, Ulf Soderman, and Simon Ahlberg. "3D Urban Models from Laser Radar Data." In 2007 Urban Remote Sensing Joint Event. IEEE, 2007. http://dx.doi.org/10.1109/urs.2007.371813.

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Wei, Haitao, Yunyan Du, Chunjin Zhang, and Wang Xin. "Remote sensing data parallel processing base on cloud platform." In 2011 International Conference on Photonics, 3D-imaging, and Visualization. SPIE, 2011. http://dx.doi.org/10.1117/12.907562.

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Tong, Hengjian, Yun Zhang, and Zhenfeng Shao. "3D remote sensing images data organization and web publication." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815176.

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Kawata, Yoshiyuki, and Kohei Koizumi. "A GUI visualization system for airborne lidar image data to reconstruct 3D city model." In SPIE Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2015. http://dx.doi.org/10.1117/12.2193067.

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Zhu, Lingli, Juha Hyyppa, Antero Kukko, Anttoni Jaakkola, Matti Lehtomaki, Harri Kaartinen, Ruizhi Chen, et al. "3D city model for mobile phone using MMS data." In 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137691.

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Blinov, Alexander B. "Reconstruction of 3D-horizons from 3D-seismic data sets." In Remote Sensing for Environmental Monitoring, GIS Applications, and Geology III. SPIE, 2004. http://dx.doi.org/10.1117/12.513165.

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Reports on the topic "3D Remote Sensing data"

1

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.

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Rencz, 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.

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Jessup, Andrew T., Robert A. Holman, and Steve Elgar. DARLA: Data Assimilation and Remote Sensing for Littoral Applications. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada572934.

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Jessup, Andrew T., Robert A. Holman, and Steve Elgar. DARLA: Data Assimilation and Remote Sensing for Littoral Applications. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada598022.

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Jessup, Andrew T., Chris Chickadel, Gordon Farquharson, Jim Thomson, Robert A. Holman, Merrick Haller, Alexander Kuropov, Tuba Ozkan-Haller, Steve Elgar, and Britt Raubenheimer. DARLA: Data Assimilation and Remote Sensing for Littoral Applications. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557219.

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Downs, Christine, Jason Heath, and Teeratorn Kadeethum. Persistent homology-based feature detection from remote-sensing data. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1887492.

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M.A. Ebadian, Ph D. REVIEW OF REMOTE SENSING TECHNOLOGIES AND DATA FOR DOE-EM APPLICATIONS. Office of Scientific and Technical Information (OSTI), January 1999. http://dx.doi.org/10.2172/772512.

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Singhroy, V., J. E. Loehr, and 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.

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William C. McLendon III and Randy C. Brost. Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs. Office of Scientific and Technical Information (OSTI), May 2016. http://dx.doi.org/10.2172/1254282.

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Saltus, Allen, Maygarden Jr., Saucier Benjamin, and Roger T. Analysis and Technical Report of Remote Sensing Data for the USS Kinsman. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada375666.

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