Дисертації з теми "Remote Sensing Image Data Analysis"
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Fountanas, Leonidas. "Principal components based techniques for hyperspectral image data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FFountanas.pdf.
Повний текст джерелаCheriyadat, Anil Meerasa. "Limitations of principal component analysis for dimensionality-reduction for classification of hyperspectral data." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-11072003-133109.
Повний текст джерелаRobert, Denis J. "Selection and analysis of optimal textural features for accurate classification of monochrome digitized image data /." Online version of thesis, 1989. http://hdl.handle.net/1850/11364.
Повний текст джерелаKHALIQ, ALEEM. "Advancements in Multi-temporal Remote Sensing Data Analysis Techniques for Precision Agriculture." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2839838.
Повний текст джерелаMarcellin, Michael W., Naoufal Amrani, Serra-Sagristà Joan, Valero Laparra, and Jesus Malo. "Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data." IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016. http://hdl.handle.net/10150/621311.
Повний текст джерелаFischer, Manfred M., Sucharita Gopal, Petra Staufer-Steinnocher, and Klaus Steinocher. "Evaluation of Neural Pattern Classifiers for a Remote Sensing Application." WU Vienna University of Economics and Business, 1995. http://epub.wu.ac.at/4184/1/WSG_DP_4695.pdf.
Повний текст джерелаSeries: Discussion Papers of the Institute for Economic Geography and GIScience
Linden, Sebastian van der. "Investigating the potential of hyperspectral remote sensing data for the analysis of urban imperviousness." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15757.
Повний текст джерелаUrbanization is one of the most powerful and irreversible processes by which humans modify the Earth''s surface. Optical remote sensing is a main source of Earth observation products which help to better understand this dynamic process and its consequences. This work investigates the potential of airborne hyperspectral data to provide information on urban imperviousness that is needed for an integrated analysis of the coupled natural and human systems therein. For this purpose the complete processing workflow from preprocessing of the raw image to the generation of geocoded maps on land cover and impervious surface coverage is performed using Hyperspectral Mapper data acquired over Berlin, Germany. The traditional workflow for hyperspectral data is extended or modified at several points: a normalization of brightness gradients that are caused by directional reflectance properties of urban surfaces is included into radiometric preprocessing; support vector machines are used to classify five spectrally complex land cover classes without previous feature extraction or the definition of sub-classes. A detailed assessment of such maps is performed based on various reference products. Results show that the accuracy of derived maps depends on several steps within the processing workflow. For example, the support vector machine classification of hyperspectral data itself is accurate but geocoding without detailed terrain information introduces critical errors; impervious surface estimates correlate well with ground data but trees covering impervious surface below generally causes offsets; image segmentation does not enhance spectral classification accuracy of the spatially heterogeneous area but offers an interesting way of data compression and more time effective processing. Findings from this work help judging the reliability of data products and in doing so advance a possible extension of urban remote sensing approaches to areas where only little additional data exists.
Parshakov, Ilia. "Automatic class labeling of classified imagery using a hyperspectral library." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Geography, c2012, 2012. http://hdl.handle.net/10133/3372.
Повний текст джерелаvii, 93 leaves : ill., maps (some col.) ; 29 cm
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.
Повний текст джерелаIl 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.
Gapper, Justin J. "Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images." Chapman University Digital Commons, 2019. https://digitalcommons.chapman.edu/cads_dissertations/2.
Повний текст джерелаYang, Hsien-Min 1957. "PRINCIPAL COMPONENTS AND TEXTURE ANALYSIS OF THE NS-001 THEMATIC MAPPER SIMULATOR DATA IN THE ROSEMONT MINING DISTRICT, ARIZONA (GEOLOGIC, DIGITAL IMAGE PROCESSING, TEXTURE EXTRACTION)." Thesis, The University of Arizona, 1985. http://hdl.handle.net/10150/275436.
Повний текст джерелаRichter, Nicole. "Pedogenic iron oxide determination of soil surfaces from laboratory spectroscopy and HyMap image data." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2010. http://dx.doi.org/10.18452/16119.
Повний текст джерелаAbstract The knowledge of the soil condition and development is decisive when characterizing and monitoring the change of ecosystems. The global presence of iron oxides and their highly variable concentration and mineralogy reflecting different soil conditions make them a suitable indicator. Optical remote sensing methods are employed to determine and map the soil iron oxide concentrations on the example of the Cabo de Gata-Níjar Natural Park, a semi-arid ecosystem in SE Spain. In an initial laboratory spectroscopy study, a methodology is developed that links iron oxide content (Fed, citrate-dithionite extractable iron oxides) with iron spectral absorption bands. Texture-dependent Fed prediction models are developed for sand- and clay-silt-dominated samples. They yield highly accurate estimations with less than 15 % prediction error. Similar accuracies are achieved from texture-independent models. Texture-independent models are applied to the HyMap image data because a pixel-wise determination of the predominating soil texture is not possible. However, the spatial distribution of Fed concentration in the study area is determined with comparable accuracy as in the laboratory. Laboratory analysis of vegetation vitality and density impact on the soil reflectance spectra and Fed prediction accuracy has shown that reliable estimations are possible until about 20 % leaf cover. Accordingly, three Fed prediction accuracy levels are defined based on the joint detectability of vegetation and iron absorption features. The final Fed prediction map is used to evaluate the current soil conditions and identify potentially eroded soils surfaces. The present method has due to low complexity a high potential for the global monitoring of such sensitive areas from current and future spaceborne sensors.
Yang, Bo. "Spatio-temporal Analysis of Urban Heat Island and Heat Wave Evolution using Time-series Remote Sensing Images: Method and Applications." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552398782461458.
Повний текст джерелаShah, Vijay Pravin. "A wavelet-based approach to primitive feature extraction, region-based segmentation, and identification for image information mining." Diss., Mississippi State : Mississippi State University, 2007. http://library.msstate.edu/etd/show.asp?etd=etd-07062007-134150.
Повний текст джерелаColadello, Leandro Fernandes. "Integration of heterogeneous data in time series : a study of the evolution of aquatic macrophytes in eutrophic reservoirs based on multispectral images and meteorological data /." Presidente Prudente, 2020. http://hdl.handle.net/11449/192672.
Повний текст джерелаResumo: O represamento de rios para a produção de energia elétrica usualmente provoca atividades antrópicas que impactam um ecossistema aquático fortemente. Uma das consequências de se instalar pequenos reservatórios em regiões sujeitas à intensos processos de urbanização e industrialização é a abundância de macrófitas, resultante do despejo de nutrientes em grandes concentrações no ecossistema aquático. Recentemente, o grande volume de images multitemporais de sensoriamento remoto disponíveis em bancos de dados gratuitos, bem como a alta performance computacional que permite a mineração de grandes volumes de dados, fazem com que o monitoramento de fenômenos ambientais seja um objeto de estudo recorrente. O propósito desse estudo é desenvolver uma metodologia baseada na integração de dados heterogêneos, fornecidos por séries temporais de coleções de imagens multiespectrais e multitemporais Landsat e coleções de dados climáticos históricos, para investigar a evolução e comportamento espacial de macrófitas aquáticas em lagos e reservatórios eutrofizados. A extensa coleção temporal de imagens de superfície de reflectância Landsat disponível e também dados de variáveis ambientais permitiram a construção e análise de séries temporais para investigar a recorrente abundância de macrófitas no reservatório de Salto Grande, localizado na região metropolitana de Campinas, São Paulo, Brasil. Inicialmente, foi encontrado que as imagens Landsat possuem a qualidade radiométrica necessária para se r... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: River damming for electric power production usually triggers anthropic activities that strongly impact on aquatic ecosystem. One of the consequences of installing small reservoirs in regions subject to an intense process of urbanization and industrialization is the overabundance of macrophytes, resulting from the input of nutrients in high concentration into the aquatic ecosystem. Currently, the large volume of multitemporal remote sensing images available in open data sources, as well as the high computational performance that allow the mining of large volumes of data has made the monitoring of environmental phenomena a recurrent object of analysis. The aim of this study is to develop a methodology based on the integration of heterogeneous data, provided by time series of multispectral and multitemporal Landsat images and collections of historical climatic data, to investigate the evolution and spatial behavior of aquatic macrophytes in lakes and eutrophic reservoirs. So, the extensive temporal collection of the Landsat surface reflectance images made available as well as environmental variables data permitted the construction and analysis of time series to investigate the recurrent over-abundance of macrophytes in Salto Grande reservoir, located in the metropolitan region of Campinas, São Paulo, Brazil. Initially, it was found that the the Landsat images have the necessary radiometric quality to perform the time series analyses, through an assessment based on information ab... (Complete abstract click electronic access below)
Doutor
Evans, Ben Richard. "Data-driven prediction of saltmarsh morphodynamics." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/276823.
Повний текст джерелаHoxha, Genc. "IMAGE CAPTIONING FOR REMOTE SENSING IMAGE ANALYSIS." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/351752.
Повний текст джерелаJia, Xiuping Electrical Engineering Australian Defence Force Academy UNSW. "Classification techniques for hyperspectral remote sensing image data." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Electrical Engineering, 1996. http://handle.unsw.edu.au/1959.4/38713.
Повний текст джерелаBejiga, Mesay Belete. "Adversarial approaches to remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257100.
Повний текст джерелаBejiga, Mesay Belete. "Adversarial approaches to remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257100.
Повний текст джерелаAlam, Mohammad Tanveer. "Image Classification for Remote Sensing Using Data-Mining Techniques." Youngstown State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1313003161.
Повний текст джерелаGarner, Jamada J. "Scene classification using high spatial resolution multispectral data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FGarner.pdf.
Повний текст джерелаSlone, Ambrose J. (Abrose Jay). "Improved remote sensing data analysis using neural networks." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11461.
Повний текст джерелаIncludes bibliographical references (leaf 115).
by Ambrose J. Slone.
M.Eng.
Philipson, née Ammenberg Petra. "Environmental Applications of Aquatic Remote Sensing." Doctoral thesis, Uppsala University, Centre for Image Analysis, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3328.
Повний текст джерелаMany lakes, coastal zones and oceans are directly or indirectly influenced by human activities. Through the outlet of a vast amount of substances in the air and water, we are changing the natural conditions on local and global levels.
Remote sensing sensors, on satellites or airplanes, can collect image data, providing the user with information about the depicted area, object or phenomenon. Three different applications are discussed in this thesis. In the first part, we have used a bio-optical model to derive information about water quality parameters from remote sensing data collected over Swedish lakes. In the second part, remote sensing data have been used to locate and map wastewater plumes from pulp and paper industries along the east coast of Sweden. Finally, in the third part, we have investigated to what extent satellite data can be used to monitor coral reefs and detect coral bleaching.
Regardless of application, it is important to understand the limitations of this technique. The available sensors are different and limited in terms of their spatial, spectral, radiometric and temporal resolution. We are also limited with respect to the objects we are monitoring, as the concentration of some substances is too low or the objects are too small, to be identified from space. However, this technique gives us a possibility to monitor our environment, in this case the aquatic environment, with a superior spatial coverage. Other advantages with remote sensing are the possibility of getting updated information and that the data is collected and distributed in digital form and therefore can be processed using computers.
Sarton, Christopher J. "Autopilot using differential thrust for ARIES autonomous underwater vehicle." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FSarton.pdf.
Повний текст джерелаSaha, 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.
Повний текст джерелаSaha, 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.
Повний текст джерелаKressler, Florian. "The Integration of Remote Sensing and Ancillary Data." WU Vienna University of Economics and Business, 1996. http://epub.wu.ac.at/4256/1/WSG_RR_0896.pdf.
Повний текст джерелаSeries: Research Reports of the Institute for Economic Geography and GIScience
Humphrey, Matthew Donald. "Texture analysis of high resolution panchromatic imagery for terrain classification." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FHumphrey.pdf.
Повний текст джерелаKumar, Mrityunjay. "Model based image fusion." Diss., Connect to online resource - MSU authorized users, 2008.
Знайти повний текст джерелаSun, Liqun, and 孙立群. "A comprehensive analysis of terrestrial surface features using remote sensing data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/208044.
Повний текст джерелаpublished_or_final_version
Civil Engineering
Doctoral
Doctor of Philosophy
Combrexelle, Sébastien. "Multifractal analysis for multivariate data with application to remote sensing." Phd thesis, Toulouse, INPT, 2016. http://oatao.univ-toulouse.fr/16477/1/Combrexelle.pdf.
Повний текст джерелаCastelletti, Davide. "Advanced regression and detection methods for remote sensing data analysis." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/368526.
Повний текст джерелаCastelletti, Davide. "Advanced regression and detection methods for remote sensing data analysis." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2765/2/Castelletti-thesis.pdf.
Повний текст джерелаMcLean, Andrew Lister. "Applications of maximum entropy data analysis." Thesis, University of Southampton, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319161.
Повний текст джерелаOrtega-García, José Antonio. "Forest stand delineation through remote sensing and Object-Based Image Analysis." Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-28005.
Повний текст джерелаNo
Ducey, Craig David. "Hierarchical Image Analysis and Characterization of Scaling Effects in Remote Sensing." PDXScholar, 2010. https://pdxscholar.library.pdx.edu/open_access_etds/399.
Повний текст джерелаCui, Yanwei. "Kernel-based learning on hierarchical image representations : applications to remote sensing data classification." Thesis, Lorient, 2017. http://www.theses.fr/2017LORIS448/document.
Повний текст джерелаHierarchical image representations have been widely used in the image classification context. Such representations are capable of modeling the content of an image through a tree structure. In this thesis, we investigate kernel-based strategies that make possible taking input data in a structured form and capturing the topological patterns inside each structure through designing structured kernels. We develop a structured kernel dedicated to unordered tree and path (sequence of nodes) structures equipped with numerical features, called Bag of Subpaths Kernel (BoSK). It is formed by summing up kernels computed on subpaths (a bag of all paths and single nodes) between two bags. The direct computation of BoSK yields a quadratic complexity w.r.t. both structure size (number of nodes) and amount of data (training size). We also propose a scalable version of BoSK (SBoSK for short), using Random Fourier Features technique to map the structured data in a randomized finite-dimensional Euclidean space, where inner product of the transformed feature vector approximates BoSK. It brings down the complexity from quadratic to linear w.r.t. structure size and amount of data, making the kernel compliant with the large-scale machine-learning context. Thanks to (S)BoSK, we are able to learn from cross-scale patterns in hierarchical image representations. (S)BoSK operates on paths, thus allowing modeling the context of a pixel (leaf of the hierarchical representation) through its ancestor regions at multiple scales. Such a model is used within pixel-based image classification. (S)BoSK also works on trees, making the kernel able to capture the composition of an object (top of the hierarchical representation) and the topological relationships among its subparts. This strategy allows tile/sub-image classification. Further relying on (S)BoSK, we introduce a novel multi-source classification approach that performs classification directly from a hierarchical image representation built from two images of the same scene taken at different resolutions, possibly with different modalities. Evaluations on several publicly available remote sensing datasets illustrate the superiority of (S)BoSK compared to state-of-the-art methods in terms of classification accuracy, and experiments on an urban classification task show the effectiveness of proposed multi-source classification approach
Zhu, Shuxiang. "Big Data System to Support Natural Disaster Analysis." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1592404690195316.
Повний текст джерелаZhang, Hongqin. "Color in scientific visualization : perception and image-based data display /." Online version of thesis, 2008. http://hdl.handle.net/1850/5805.
Повний текст джерелаMello, Marcio Pupin. "Spectral-temporal and Bayesian methods for agricultural remote sensing data analysis." Instituto Nacional de Pesquisas Espaciais (INPE), 2013. http://urlib.net/sid.inpe.br/mtc-m19/2013/09.17.18.58.
Повний текст джерелаReliable agricultural statistics has become increasingly important to decision makers. Especially when timely obtained, agricultural information is highly relevant to the strategic planning of the country. Although remote sensing shows to be of great potential for agricultural mapping applications, with the benefit of further improving official agricultural statistics, its potential has not been fully explored. There are very few successful examples of operational remote sensing application for systematic mapping of agricultural crops, and they are strongly supported by visual image interpretation to allow accurate results. Indeed, despite the substantial advances in remote sensing data analysis, techniques to automate remote sensing data analysis focusing on agricultural mapping applications are highly valuable but have to maintain consistency and accuracy. In this context, there continues to be a demand for development and implementation of computer aided methods to automate the processes of analyzing remote sensing datasets for agriculture applications. Thus, the main objective of this thesis is to propose implementation of computer aided methodologies to automate, maintaining consistency and accuracy, processes of remote sensing data analyses focused on agricultural thematic mapping applications. This thesis was written as a collection of two papers related to a core theme, each addressing the following main points: (i) multitemporal, multispectral and multisensor image analysis that allow the description of spectral changes of agricultural targets over time; and (ii) artificial intelligence in modeling phenomena using remote sensing and ancillary data. Study cases of sugarcane harvest in São Paulo and soybean mapping in Mato Grosso were used to test the proposed methods named STARS and BayNeRD, respectively. The two methods developed and tested confirm that remotely sensed (and ancillary) data analysis can be automated with computer aided methods to model a range of cropland phenomena for agriculture applications, maintaining consistency and accuracy.
Brooks, Evan B. "Fourier Series Applications in Multitemporal Remote Sensing Analysis using Landsat Data." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23276.
Повний текст джерелаPh. D.
Laben, Craig A. "A comparison of methods for forming multitemporal composites from NOAA advanced very high resolution radiometer data /." Online version of thesis, 1993. http://hdl.handle.net/1850/12137.
Повний текст джерелаWelle, Paul. "Remotely Sensed Data for High Resolution Agro-Environmental Policy Analysis." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1012.
Повний текст джерелаRajadell, Rojas Olga. "Data selection and spectral-spatial characterisation for hyperspectral image segmentation. Applications to remote sensing." Doctoral thesis, Universitat Jaume I, 2013. http://hdl.handle.net/10803/669093.
Повний текст джерелаLately image analysis have aided many discoveries in research. This thesis focusses on the analysis of remote sensed images for aerial inspection. It tackles the problem of segmentation and classification according to land usage. In this field, the use of hyperspectral images has been the trend followed since the emergence of hyperspectral sensors. This type of images improves the performance of the task but raises some issues. Two of those issues are the dimensionality and the interaction with experts. We propose enhancements overcome them. Efficiency and economic reasons encouraged to start this work. The enhancements introduced in this work allow to tackle segmentation and classification of this type of images using less data, thus increasing the efficiency and enabling the design task specific sensors which are cheaper. Also, our enhacements allow to perform the same task with less expert collaboration which also decreases the costs and accelerates the process.
Yang, Bo. "Assimilation of multi-scale thermal remote sensing data using spatio-temporal cokriging method." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868463.
Повний текст джерелаMunechika, Curtis K. "Merging panchromatic and multispectral images for enhanced image analysis /." Online version of thesis, 1990. http://hdl.handle.net/1850/11366.
Повний текст джерелаRodriguez-Guerra, Edna Patricia. "Faulting evidence of isostatic uplift in the Rincon Mountains metamorphic core complex: An image processing analysis." Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/284275.
Повний текст джерелаThoué, Frédéric. "Quantification par imagerie tridimensionnelle de l'extension continentale et des déplacements associés : exemples au Kenya et au Yémen." Grenoble 1, 1993. http://www.theses.fr/1993GRE10200.
Повний текст джерелаAl-Rousan, Naief Mahmoud. "System calibration, geometric accuracy testing and validation of DEM and orthoimage data extracted from spot stereo-pairs using commercially available image processing systems." Thesis, University of Glasgow, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264262.
Повний текст джерела