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Dissertations / Theses on the topic 'Remote sensing Image processing Remote sensing Remote sensing Computer algorithms'
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Cisz, Adam. "Performance comparison of hyperspectral target detection algorithms /." Online version of thesis, 2006. https://ritdml.rit.edu/dspace/handle/1850/3020.
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Wang, Zhen. "Modeling wildland fire radiance in synthetic remote sensing scenes /." Online version of thesis, 2007. http://hdl.handle.net/1850/5787.
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Ientilucci, Emmett J. "Hyperspectral sub-pixel target detection using hybrid algorithms and physics based modeling /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1185.
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Doster, Timothy J. "Mathematical methods for anomaly grouping in hyperspectral images /." Online version of thesis, 2009. http://hdl.handle.net/1850/11575.
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Schuetter, Jared Michael. "Cairn Detection in Southern Arabia Using a Supervised Automatic Detection Algorithm and Multiple Sample Data Spectroscopic Clustering." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1269567071.
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Shah, 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.
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Story, Michael Haun. "Comparison of accuracy and efficiency of five digital image classification algorithms." Thesis, This resource online, 1987. http://scholar.lib.vt.edu/theses/available/etd-04122010-083611/.
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Li, Feng Engineering & Information Technology Australian Defence Force Academy UNSW. "Development of super resolution techniques for finer scale remote sensing image mapping." Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/44098.
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In this thesis, methods for achieving finer scale multi-spectral classification through the use of super resolution (SR) techniques are investigated. A new super resolution algorithm Maximum a Posteriori based on the universal hidden Markov tree model (MAP-uHMT) is developed which can be applied successfully to super-resolve each multi-spectral channel before classification by standard methods. It is believed that this is the first time that a true super resolution algorithm has been applied to multi-spectral classification, and results are shown to be excellent. Image registration is an important step for SR in which misalignment can be measured for each of many low resolution images; therefore, a new and computationally efficient image registration is developed for this particular application. This improved elastic image registration method can deal with a global affine warping and local shift translations based on coarse to fine pyramid levels. The experimental results show that it can provide good registration accuracy in less computational time than comparable methods. Maximum a posteriori (MAP) is adopted to deal with the ill-conditioned problem of super resolution, wherein a prior is constructed based on the universal hidden Markov tree (uHMT) model in the wavelet domain. In order to test this prior for MAP estimation, it is first tested in the simpler and typically ill-conditioned problem of image denoising. Experimental results illustrate that this new image denoising method achieves good performance for the test images. The new prior is then extended to SR. By combining with the new elastic image registration algorithm, MAP-uHMT can super resolve both some natural video frames and remote sensing images. Test results with both synthetic data and real data show that this method achieves super resolution both visually and quantitatively. In order to show that MAPuHMT is also applicable more widely, it is tested on a sequence of long-range surveillance images captured under conditions of atmospheric turbulence distortion. The results suggest that super resolution may have been achieved in this application also.
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Van, der Westhuizen Lynette. "Concise analysis and testing of a software model of a satellite remote sensing system used for image generation." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/96029.
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Thesis (MEng) -- Stellenbosch University, 2014.ENGLISH ABSTRACT: The capability of simulating the output image of earth observation satellite sensors is of great value, as it reduces the dependency on extensive field tests when developing, testing and calibrating satellite sensors. The aim of this study was to develop a software model to simulate the data acquisition process used by passive remote sensing satellites for the purpose of image generation. To design the software model, a comprehensive study was done of a physical real world satellite remote sensing system in order to identify and analyse the different elements of the data acquisition process. The different elements were identified as being the target, the atmosphere, the sensor and satellite, and radiation. These elements and a signature rendering equation are used to model the target-atmosphere-sensor relationship of the data acquisition process. The signature rendering equation is a mathematical model of the different solar and self-emitted thermal radiance paths that contribute to the radiance reaching the sensor. It is proposed that the software model be implemented as an additional space remote sensing application in the Optronics Sensor Simulator (OSSIM) simulation environment. The OSSIM environment provides the infrastructure and key capabilities upon which this specialist work builds. OSSIM includes a staring array sensor model, which was adapted and expanded in this study to operate as a generic satellite sensor. The OSSIM signature rendering equation was found to include all the necessary terms required to model the at-sensor radiance for a satellite sensor with the exception of an adjacency effect term. The equation was expanded in this study to include a term to describe the in-field-of-view adjacency effect due to aerosol scattering. This effect was modelled as a constant value over the sensor field of view. Models were designed to simulate across-track scanning mirrors, the satellite orbit trajectory and basic image processing for geometric discontinuities. Testing of the software model showed that all functions operated correctly within the set operating conditions and that the in-field-of-view adjacency effect can be modelled effectively by a constant value over the sensor field of view. It was concluded that the satellite remote sensing software model designed in this study accurately simulates the key features of the real world system and provides a concise and sound framework on which future functionality can be expanded.
AFRIKAANSE OPSOMMING: Dit is nuttig om ’n sagteware program te besit wat die gegenereerde beelde van ’n satellietsensor vir aarde-waarneming kan naboots. So ’n sagteware program sal die afhanklikheid van breedvoerige veldwerktoetse verminder gedurende die ontwerp, toetsing en kalibrasie fases van die ontwikkeling van ’n satellietsensor. Die doel van hierdie studie was om ’n sagteware model te ontwerp wat die dataverwerwingsproses van ’n passiewe satelliet afstandswaarnemingstelsel kan naboots, met die doel om beelde te genereer. Om die sagteware model te ontwerp het ’n omvattende studie van ’n fisiese regte wêreld satelliet afstandswaarnemingstelsel geverg, om die verskillende elemente van die dataverwerwingsproses te identifiseer en te analiseer. Die verskillende elemente is geïdentifiseer as die teiken, die atmosfeer, die sensor en satelliet, en vloed. Hierdie elemente, tesame met ’n duimdrukvergelyking, is gebruik om die teiken-atmosfeer-sensor verhouding van die dataverwerwingsproses te modelleer. Die duimdrukvergelyking is ’n wiskundige model van die verskillende voortplantingspaaie van gereflekteerde sonvloed en self-stralende termiese vloed wat bydra tot die totale vloed wat die sensor bereik. Dit is voorgestel dat die sagteware model as ’n addisionele ruimte afstandswaarnemingtoepassing in die ‘Optronics sensor Simulator’ (OSSIM) simulasie-omgewing geïmplementeer word. Die OSSIM simulasie-omgewing voorsien die nodige infrastruktuur en belangrike funksies waarop hierdie spesialis werk gebou kan word. OSSIM het ’n starende-skikking sensor model wat in hierdie studie aangepas is en uitgebrei is om as ’n generiese satellietsensor te funksioneer. Die OSSIM duimdrukvergelyking bevat al die nodige radiometriese terme, behalwe ’n nabyheids-verstrooiing term, om die vloed by die satellietsensor te modeleer. Die duimdrukvergelyking is uitgebrei in hierdie studie om ’n term in te sluit wat die verstrooiing van vloed vanaf naby-geleë voorwerpe, as gevolg van aerosol verstrooiing, kan beskryf. Die nabyheids-verstrooiing is gemodeleer as ’n konstante waarde oor die sigveld van die sensor. Modelle is ontwerp om die beweging van oor-baan skandering-spieëls en die satelliet wentelbaan trajek te bereken. ’n Basiese beeldverwerkings model is ook ontwerp om diskontinuïteite in geometriese vorms in die sensor beelde reg te stel. Toetsing van die sagteware model het gewys dat al die funksies korrek gefunksioneer het binne die limiete van die vasgestelde operasionele voorwaardes. Die toets resultate het ook bewys dat die in-sig-veld nabyheids-verstrooiing akkuraat gemodeleer kan word as ’n konstante waarde oor die sensor sigveld. Daar is tot die gevolgtrekking gekom dat die satelliet afstandswaarneming sagteware model wat in hierdie studie ontwerp is al die belangrikste kenmerke van die werklike wêreld stelsel kan simuleer. Die model vorm ’n beknopte en stewige raamwerk waarop toekomstige werk uitgebrei kan word.
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Lavalle, Marco. "Full and Compact Polarimetric Radar Interferometry for Vegetation Remote Sensing." Phd thesis, Université Rennes 1, 2009. http://tel.archives-ouvertes.fr/tel-00480972.
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Cette thèse aborde principalement le rôle que jouent les radars polarimétrique et interférométrique dans les applications de géosciences, tout particulièrement sur les forêts. Il est démontré que les modèles actuels simples de la corrélation spatiale des milieux naturels sont capables d'estimer de manière robuste la hauteur de la forêt et sa biomasse; lorsque la topographie est peu prononcée. La corrélation temporelle y est traitée plus précisément en définissant une fonction de corrélation temporelle dépendant de la hauteur de la canopée. Les effets de cette amélioration sur la modélisation directe et inverse sont discutés. Une expression simplifiée de ces modèles est proposée et validée dans le cas des basses fréquences. Nous utilisons à la fois des données polarimétriques satellitales, ainsi que des simulations numériques de rétrodiffusion afin d'illustrer les résultats. Pour les radars en polarimétrie compacte, la pseudo reconstruction est généralisée au cas interférométrique et son efficacité est démontrée seulement pour certaines combinaisons entre les composantes volumiques et les composantes du sol. Enfin, la qualité des données est abordée, en montrant que la rotation de Faraday peut être estimée et corrigée à partir des échos radar non focalisés et que les trièdres maillées peuvent servir d'étalonnage radiométrique des données à double polarisation.
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Lao, Yin, and 劉然. "Image matching of running vehicles." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30278806.
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O'Donnell, Erin. "Detection and identification of effluent gases using invariant hyperspectral algorithms /." Link to online verson, 2005. https://ritdml.rit.edu/dspace/handle/1850/1124.
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Kinda, Bazile. "Acoustic remote sensing of Arctic Sea Ice from long term soundscape measurements." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00940393.
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La fonte rapide des glaces de l'Arctique dans le contexte actuel du réchauffement climatique est un sujet scientifique majeur de ces 30 dernières années. L'Arctique joue un rôle fondamental dans l'équilibre du climat et requiert une attention particulière. Les régions arctiques sont alors surveillées par des observations satellitaires et des mesures in-situ. L'impact climatique de la fonte totale de la glace arctique est encore mal connu. Des recherches sont donc nécessaires pour le suivi à long terme de l'Océan Arctique, en particulier la dynamique spatio-temporelle de la couverture de glace et ses conséquences sur les écosystèmes. La disparition de la banquise en Arctique sera progressivement accompagnée de l'installation d'activités industrielles saisonnières ou pérennes. Ceci a pour conséquences une modification des paysages acoustiques sousmarins de ces environnements jusqu'alors préservés des sources sonores anthropiques. La présente étude, portée sur les paysages acoustiques sous-marins des régions océaniques polaires du Canada, s'inscrit dans ce contexte sous deux axes. Le premier axe de notre étude concerne donc les conséquences directes de la disparition de la glace sur les paysages acoustiques sous-marins de l'Arctique et des mers subarctiques du Canada. Nous avons alors examiné le bruit ambiant, ses variations saisonnières ainsi ses pilotes environnementaux. Un algorithme dédié à l'estimation de cette composante du bruit océanique a été développé à cet effet, afin de constituer des séries temporelles longue durée. A travers des analyses statistiques, nous avons établi que les variables environnementaux responsables de la production du bruit de fond dépendent de l'état de la surface océanique et que pendant la période hivernale, ce bruit est piloté par les mêmes variables environnementales qui gouvernent la circulation océanique à grande échelle de l'Arctique. Le second axe abordé au cours de ce travail de thèse vise à évaluer le potentiel de l'acoustique passive comme moyen de monitorage de la dynamique spatio-temporelle de la banquise. Pour ce faire, nous avons identifié des événements acoustiques signant de phénomènes physiques sous la glace afin d'améliorer la compréhension de leur mécanisme de production. Nous avons ainsi pu lier divers transitoires acoustiques à des processus de déformation de la banquise.
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Jonsson, Patrik. "Surface Status Classification, Utilizing Image Sensor Technology and Computer Models." Doctoral thesis, Mittuniversitetet, Avdelningen för elektronikkonstruktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-24828.
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There is a great need to develop systems that can continuously provide correct information about road surface status depending on the prevailing weather conditions. This will minimize accidents and optimize transportation. In this thesis different methods for the determination of the road surface status have been studied and analyzed, and suggestions of new technology are proposed. Information about the road surface status is obtained traditionally from various sensors mounted directly in the road surface. This information must then be analyzed to create automated warning systems for road users and road maintenance personnel. The purpose of this thesis is to investigate how existing technologies can be used to obtain a more accurate description of the current road conditions. Another purpose is also to investigate how existing technologies can be used to obtain a more accurate description of the current road conditions. Furthermore, the aim is to develop non-contact technologies able to determine and classify road conditions over a larger area, since there is no system available today that can identify differences in road surface status in the wheel tracks and between the wheel tracks. Literature studies have been carried out to find the latest state of the art research and technology, and the research work is mainly based on empirical studies. A large part of the research has involved planning and setting up laboratory experiments to test and verify hypotheses that have emerged from the literature studies. Initially a few traditional road-mounted sensors were analyzed regarding their ability to determine the road conditions and the impact on their measured values when the sensors were exposed to contamination agents such as glycol and oil. Furthermore, non-contact methods for determining the status of the road surface have been studied. Images from cameras working in the visible range, together data from the Swedish Transportation Administration road weather stations, have been used to develop computerized road status classification models that can distinguish between a dry, wet, icy and snowy surface. Field observations have also been performed to get the ground truth for developing these models. In order to improve the ability to accurately distinguish between different surface statuses, measurement systems involving sensors working in the Near-Infrared (NIR) range have been utilized. In this thesis a new imaging method for determining road conditions with NIR camera technology is developed and described. This method was tested in a field study performed during the winter 2013-2014 with successful results. The results show that some traditional sensors could be used even with future user-friendly de-icing chemicals. The findings from using visual camera systems and meteorological parameters to determine the road status showed that they provide previously unknown information about road conditions. It was discovered that certain road conditions such as black ice is not always detectable using this technology. Therefore, research was performed that utilized the NIR region where it proved to be possible to detect and distinguish different road conditions, such as black ice. NIR camera technology was introduced in the research since the aim of the thesis was to find a method that provides information on the status of the road over a larger area. The results show that if several images taken in different spectral bands are analyzed with the support of advanced computer models, it is possible to distinguish between a dry, wet, icy and snowy surface. This resulted in the development of a NIR camera system that can distinguish between different surface statuses. Finally, two of these prototype systems for road condition classification were evaluated. These systems were installed at E14 on both sides of the border between Sweden and Norway. The results of these field tests show that this new road status classification, based on NIR imaging spectral analysis, provides new information about the status of the road surface, compared to what can be obtained from existing measurement systems, particularly for detecting differences in and between the wheel tracks.
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Lloyd, Timothy Brian. "Surface extraction from coordinate measurement data to facilitate dimensional inspection." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15815.
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Wolters, Dustin Joseph. "Assessment of Corn Plant Population at Emergence from Processed Color Aerial Imagery." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437666741.
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Gallego, Bonet Guillermo. "Variational image processing algorithms for the stereoscopic space-time reconstruction of water waves." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39480.
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A novel video observational method for the
space-time stereoscopic reconstruction of
dynamic surfaces representable as graphs, such
as ocean waves, is developed. Variational
optimization algorithms combining image
processing, computer vision and partial
differential equations are designed to address
the problem of the recovery of the shape of an
object's surface from sequences of synchronized
multi-view images. Several theoretical and numerical paths are discussed to solve the
problem. The variational stereo method
developed in this thesis has several advantages
over existing 3-D reconstruction algorithms.
Our method follows a top-down approach or
object-centered philosophy in which an explicit
model of the target object in the scene is
devised and then related to image
measurements. The key advantages of our
method are the coherence (smoothness) of the
reconstructed surface caused by a coherent
object-centered design, the robustness to noise
due to a generative model of the observed
images, the ability to handle surfaces with
smooth textures where other methods typically
fail to provide a solution, and the higher
resolution achieved due to a suitable graph
representation of the object's surface. The
method provides competitive results with
respect to existing variational reconstruction
algorithms. However, our method is based upon
a simplified but complete physical model of the
scene that allows the reconstruction process to
include physical properties of the object's
surface that are otherwise difficult to take into
account with existing reconstruction
algorithms. Some initial steps are taken toward
incorporating the physics of ocean waves in the
stereo reconstruction process. The developed
method is applied to empirical data of ocean
waves collected at an off-shore oceanographic
platform located off the coast of Crimea,
Ukraine. An empirically-based physical model
founded upon current ocean engineering
standards is used to validate the results. Our
findings suggest that this remote sensing
observational method has a broad impact on
off-shore engineering to enrich the
understanding of sea states, enabling improved
design of off-shore structures. The exploration
of ways to incorporate dynamical properties,
such as the wave equation, in the
reconstruction process is discussed for future
research.
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Cai, Shangshu. "Hyperspectral image visualization using double and multiple layers." Diss., Mississippi State : Mississippi State University, 2009. http://library.msstate.edu/etd/show.asp?etd=etd-12082008-112300.
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Shor, Eric H. "3-D longwave infrared synthetic scene simulation /." Online version of thesis, 1990. http://hdl.handle.net/1850/11361.
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Kidane, Dawit K. "Rule-based land cover classification model : expert system integration of image and non-image spatial data." Thesis, Stellenbosch : Stellenbosch University, 2005. http://hdl.handle.net/10019.1/50445.
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Thesis (MSc)--Stellenbosch University, 2005.ENGLISH ABSTRACT: Remote sensing and image processing tools provide speedy and up-to-date information on land resources. Although remote sensing is the most effective means of land cover and land use mapping, it is not without limitations. The accuracy of image analysis depends on a number of factors, of which the image classifier used is probably the most significant. It is noted that there is no perfect classifier, but some robust classifiers achieve higher accuracy results than others. For certain land cover/uses, discrimination based only on spectral properties is extremely difficult and often produces poor results. The use of ancillary data can improve the classification process. Some classifiers incorporate ancillary data before or after the classification process, which limits the full utilization of the information contained in the ancillary data. Expert classification, on the other hand, makes better use of ancillary data by incorporating data directly into the classification process. In this study an expert classification model was developed based on spatial operations designed to identify a specific land cover/use, by integrating both spectral and available ancillary data. Ancillary data were derived either from the spectral channels or from other spatial data sources such as DEM (Digital Elevation Model) and topographical maps. The model was developed in ERDAS Imagine image-processing software, using the expert engineer as a final integrator of the different constituent spatial operations. An attempt was made to identify the Level I land cover classes in the South African National Land Cover classification scheme hierarchy. Rules were determined on the basis of expert knowledge or statistical calculations of mean and variance on training samples. Although rules could be determined by using statistical applications, such as the classification analysis regression tree (CART), the absence of adequate and accurate training data for all land cover classes and the fact that all land cover classes do not require the same predictor variables makes this option less desirable. The result of the accuracy assessment showed that the overall classification accuracy was 84.3% and kappa statistics 0.829. Although this level of accuracy might be suitable for most applications, the model is flexible enough to be improved further.
AFRIKAANSE OPSOMMING: Afstandswaameming-en beeldverwerkingstegnieke kan akkurate informasie oorbodemhulpbronne weergee. Alhoewel afstandswaameming die mees effektiewe manier van grondbedekking en grondgebruikkartering is, is dit nie sonder beperkinge nie. Die akkuraatheid van beeldverwerking is afhanklik van verskeie faktore, waarvan die beeld klassifiseerder wat gebruik word, waarskynlik die belangrikste faktor is. Dit is welbekend dat daar geen perfekte klassifiseerder is nie, alhoewel sekere kragtige klassifiseerders hoër akkuraatheid as ander behaal. Vir sekere grondbedekking en -gebruike is uitkenning gebaseer op spektrale eienskappe uiters moeilik en dikwels word swak resultate behaal. Die gebruik van aanvullende data, kan die klassifikasieproses verbeter. Sommige klassifiseerders inkorporeer aanvullende data voor of na die klassifikasieproses, wat die volle aanwending van die informasie in die aanvullende data beperk. Deskundige klassifikasie, aan die ander kant, maak beter gebruik van aanvullende data deurdat dit data direk in die klassifikasieproses inkorporeer. Tydens hierdie studie is 'n deskundige klassifikasiemodel ontwikkel gebaseer op ruimtelike verwerkings, wat ontwerp is om spesifieke grondbedekking en -gebruike te identifiseer. Laasgenoemde is behaal deur beide spektrale en beskikbare aanvullende data te integreer. Aanvullende data is afgelei van, óf spektrale eienskappe, óf ander ruimtelike bronne soos 'n DEM (Digitale Elevasie Model) en topografiese kaarte. Die model is ontwikkel in ERDAS Imagine beeldverwerking sagteware, waar die 'expert engineer' as finale integreerder van die verskillende samestellende ruimtelike verwerkings gebruik is. 'n Poging is aangewend om die Klas I grondbedekkingklasse, in die Suid-Afrikaanse Nasionale Grondbedekking klassifikasiesisteem te identifiseer. Reëls is vasgestel aan die hand van deskundige begrippe of eenvoudige statistiese berekeninge van die gemiddelde en variansie van opleidingsdata. Alhoewel reëls met behulp van statistiese toepassings, soos die 'classification analysis regression tree (CART)' vasgestel kon word, maak die afwesigheid van genoegsame en akkurate opleidingsdata vir al die grondbedekkingsklasse hierdie opsie minder aantreklik. Bykomend tot laasgenoemde, vereis alle grondbedekkingsklasse nie dieselfde voorspellingsveranderlikes nie. Die resultaat van hierdie akkuraatheidsskatting toon dat die algehele klassifikasie-akkuraatheid 84.3% was en die kappa statistieke 0.829. Alhoewel hierdie vlak van akkuraatheid vir die meeste toepassings geskik is, is die model aanpasbaar genoeg om verder te verbeter.
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Grant, Cameron S. "Incorporating Spatial Information into Gas Plume Detection in Hyperspectral Imagery." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/823.
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Detection of chemical plumes in hyperspectral data is a problem having solutions that focus on spectral information. These solutions neglect the presence of the spatial information in the scene. The spatial information is exploited in this work by assignment of prior probabilities to neighborhood configurations of signal presence or absence. These probabilities are leveraged in a total probability approach to testing for signal presence in a pixel of interest. The two new algorithms developed are named spatial information detection enhancement (SIDE) and bolt-on SIDE (B-SIDE).
The results are explored in comparison to the clutter matched filter (CMF), a standard spectral technique, and to several supervised machine learning techniques. The results show a great improvement of SIDE over these other techniques, in some cases showing the poorest performance of the SIDE filter being much better than the CMF at its best.
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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.
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Image classification is a fundamental information extraction procedure in remote sensing that is used in land-cover and land-use mapping. Despite being considered as a replacement for manual mapping, it still requires some degree of analyst intervention. This makes the process of image classification time consuming, subjective, and error prone. For example, in unsupervised classification, pixels are automatically grouped into classes, but the user has to manually label the classes as one land-cover type or another. As a general rule, the larger the number of classes, the more difficult it is to assign meaningful class labels. A fully automated post-classification procedure for class labeling was developed in an attempt to alleviate this problem. It labels spectral classes by matching their spectral characteristics with reference spectra. A Landsat TM image of an agricultural area was used for performance assessment. The algorithm was used to label a 20- and 100-class image generated by the ISODATA classifier. The 20-class image was used to compare the technique with the traditional manual labeling of classes, and the 100-class image was used to compare it with the Spectral Angle Mapper and Maximum Likelihood classifiers. The proposed technique produced a map that had an overall accuracy of 51%, outperforming the manual labeling (40% to 45% accuracy, depending on the analyst performing the labeling) and the Spectral Angle Mapper classifier (39%), but underperformed compared to the Maximum Likelihood technique (53% to 63%). The newly developed class-labeling algorithm provided better results for alfalfa, beans, corn, grass and sugar beet, whereas canola, corn, fallow, flax, potato, and wheat were identified with similar or lower accuracy, depending on the classifier it was compared with.vii, 93 leaves : ill., maps (some col.) ; 29 cm
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Santos, Jefersson Alex dos 1984. "Reconhecimento semi-automatico e vetorização de regiões em imagens de sensoriamento remoto." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276150.
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Orientador: Ricardo da Silva TorresDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: O uso de imagens de sensoriamento remoto (ISRs) como fonte de informação em aplicações voltadas para o agro-negócio e bastante comum. Nessas aplicações, saber como é a ocupação espacial é fundamental. Entretanto, reconhecer e diferenciar regiões de culturas agrícolas em ISRs ainda não é uma tarefa trivial. Embora existam métodos automáticos propostos para isso, os usuários preferem muitas vezes fazer o reconhecimento manualmente. Isso acontece porque tais métodos normalmente são feitos para resolver problemas específicos, ou quando são de propósito geral, não produzem resultados satisfatórios fazendo com que, invariavelmente, o usuário tenha que revisar os resultados manualmente. A pesquisa realizada objetivou a especificação e implementação parcial de um sistema para o reconhecimento semi-automático e vetorização de regiões em imagens de sensoriamento remoto. Para isso, foi usada uma estratégia interativa, chamada realimentação de relevância, que se baseia no fato de o sistema de classificação poder aprender quais são as regiões de interesse utilizando indicações de relevância feitas pelo usuário do sistema ao longo de iterações. A idéia é utilizar descritores de imagens para codificar informações espectrais e de textura de partições das imagens e utilizar realimentação de relevância com Programação Genética (PG) para combinar as características dos descritores. PG é uma técnica de aprendizado de máquina baseada na teoria da evolução. As principais contribuições deste trabalho são: estudo comparativo de técnicas de vetorização de imagens; adaptação do modelo de recuperação de imagens por conteúdo proposto recentemente para realização de realimentação de relevância usando regiões de imagem; adaptação do modelo de realimentação de relevância para o reconhecimento de regiões em ISRs; implementação parcial de um sistema de reconhecimento semi-automático e vetorização de regiões em ISRs; proposta de metodologia de validação do sistema desenvolvido.
Abstract: The use of remote sensing images as a source of information in agrobusiness applications is very common. In these applications, it is fundamental to know how the space occupation is. However, the identification and recognition of crop regions in remote sensing images are not trivial tasks yet. Although there are automatic methods proposed to that, users prefer sometimes to identify regions manually. That happens because these methods are usually developed to solve specific problems, or, when they have a general purpose, they do not yield satisfying results. This work presents a semi-automatic method to vectorize regions from remote sensing images using relevance feedback based on genetic programming (GP). Relevance feedback is a technique used in content-based image retrieval (CBIR). Its objective is to agregate user preferences to the search process. The proposed solution consists in using image descriptors to encode texture and spectral features from the images, applying relevance feedback based on GP to combine these features with information obtained from the users interactions and, finally, segment the image. Finally, segmented image (raster) is converted into a vector representation. The main contributions of this work are: comparative study of image vectorization techniques; extension of a recently proposed relevance feedback approach for dealing with image regions; extension of the relevance feedback model for region recognition in remote sensing images; parcial implementation of the semi-automatic and vectorization system of remote sensing images regions; proposal a validation methodology.
Mestrado
Mestre em Ciência da Computação
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De, Franchis Carlo. "Earth Observation and Stereo Vision." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLN002/document.
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Cette thèse étudie les problèmes posés par l’estimation automatique de modèles numériques d’élévation de la surface terrestre à partir de photographies prises par des satellites. Ce travail a bénéficié d’une collaboration avec le CNES (Centre National d’Etudes Spatiales) sur le développement d’outils de vision stéréoscopique pour Pléiades, le premier satellite d’observation de la Terre capable de produire des paires ou triplets d’images quasi-simultanées. Le premier chapitre de la thèse décrit un modèle simplifié de caméra pushbroom destiné aux satellites d’observation de la Terre, et aborde le problème de la correction des données de calibration en faisant intervenir des mesures externes. Ce chapitre propose un nouvel algorithme pour affiner les paramètres d’orientation du satellite à partir d’un jeu de points de contrôle. Il est utilisable pour tous les satellites munis de caméras pushbroom. Dans le but d’appliquer aux images satellitaires les nombreux algorithmes de mise en correspondance stéréoscopique développés en traitement d’images et en vision par ordinateur, le deuxième chapitre explore l’adaptation de la théorie de la rectification épipolaire aux images prises par des caméras pushbroom. La rectification épipolaire est utilisée habituellement pour réduire la complexité du problème de mise en correspondance stéréoscopique, et permet d’appliquer les algorithmes les plus récents à des images satellitaires. Le chapitre suivant étudie les effets des erreurs de calibration géométrique sur la rectification et propose une méthode pour éliminer leur impact sur la mise en correspondance. Le quatrième chapitre décrit et analyse en détails une implémentation de l’algorithme Semi-Global Matching (SGM), classé actuellement parmi les meilleurs algorithmes de mise en correspondance stéréoscopique. En se fondant sur une réinterprétation récente de SGM, ce chapitre en propose une variante qui permet de réduire d’un facteur cinq son écart en énergie par rapport aux algorithmes de référence pour la minimisation de champs aléatoires de Markov. En assemblant les blocs algorithmiques décrits dans les chapitres précédents, le cinquième chapitre décrit S2P, une chaîne stéréoscopique complète qui produit des modèles numériques d’élévation à partir d’images satellitaires. Un modèle d’évolution de paysage est présenté dans le sixième chapitre comme exemple d’application. Le modèle est utilisé pour simuler numériquement la structure fine du réseau hydrographique sur des modèles numériques d’élévation obtenus à partir d’images prises par Pléiades. Le code source de la chaîne S2P2 est distribué en tant que logiciel open source. Afin d’assurer la reproductibilité des résultats obtenus, les algorithmes implémentés dans S2P sont en cours de publication dans le journal IPOL, accompagnés de descriptions et d’analyses détaillées, de codes sources documentés et de démonstrateurs en ligneThis thesis deals with the problem of computing accurate digital elevationmodels of the Earth's surface from optical images taken by pushbroomobservation satellites. It takes advantage of the collaboration of thedefendant with CNES (the French Space Agency) on the development ofstereo vision tools for Pléiades, the first Earth observation satelliteproducing quasi simultaneous stereo pairs or triplets with small baseline.The first chapter describes a simple pushbroom camera model for observationsatellites orbiting around the Earth and addresses the correction of theacquisition geometry by involving extrinsic information. This chapter proposesa new algorithm to refine the orientation parameters from a set of groundcontrol points, applicable to all pushbroom satellites.With the goal of testing for satellite imaging the thriving exploration ofstereo matching by the computer vision community, the second chapter exploresthe adaptation of the theory of epipolar resampling to pushbroom images.Epipolar resampling is traditionally used in stereo to reduce the matchingcomputational cost, and permits to test for satellite imaging the mostcompetitive computer vision algorithms. The third chapter discusses the effectsof geometric calibration inaccuracies and proposes a method to cancel itsimpact on stereo matching.The fourth chapter analyzes and describes a detailed implementation of theSemi-Global Matching (SGM) algorithm, which is currently among the top-rankedstereo vision algorithms. Based on a recently proposed interpretation of SGM asa min-sum Belief Propagation algorithm, a variant is proposed that allows toreduce by a factor five the energy gap of SGM with respect to referencealgorithms for Markov Random Fields with truncated smoothness terms.By wrapping together the algorithmic blocks described in the previous chapters,the fifth chapter describes S2P, a complete stereo pipeline for producingdigital elevation models from satellite images. As an application, a landscapeevolution model is presented in the sixth chapter. The model is used tosimulate numerically the fine structure of the river networks on digitalelevation models obtained from Pléiades Earth observation images.The source code of the S2P stereo pipeline is distributed as open source. Toensure reproducibility, the algorithms implemented in each step of the S2Ppipeline are submitted to the IPOL journal, with detailed descriptions of thealgorithms, documented source codes and online demonstrations for each block ofthe pipeline
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Patruno, Jolanda. "Polarimetric RADARSAT-2 and ALOS PALSAR multi-frequency analysis over the archaeological site of Gebel Barkal (Sudan)." Phd thesis, Université Rennes 1, 2014. http://tel.archives-ouvertes.fr/tel-01061287.
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Aim of PhD research is to exploit SAR Polarimetry technique for the identification of surface and subsurface archaeological features in the site of Gebel Barkal (Sudan), inscribed in the UNESCO World Heritage List since 2003. Sand penetration capability of both C-band and L-band sensors are discussed analysing archived ALOS PALSAR and RADARSAT-2 specifically acquired (2012-2013) images. Moreover, the research activity illustrates the potential of integrating SAR polarimetric and optical satellite data in a dedicated GIS project, realised in collaboration with the Universities of Turin and Venice (Italy). The monitoring of ancient sites by means of remotely acquired polarimetric SAR data represents a benefit for the archaeological research, where detected anomalies can address archaeological excavations or ground truth verification, as shown in the PhD dissertation, and where threatening factors affect the integrity of a cultural site.
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Dore, Nicole. "Polarimetric multi-incidence angle analysis over the archaeological site of Samarra by means of RADARSAT-2 and ALOS PALSAR satellites datasets." Phd thesis, Université Rennes 1, 2014. http://tel.archives-ouvertes.fr/tel-01060848.
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This work has as goal to study the microwaves behavior over the archaeological structures still visible in the historical city of Samarra, the capital of the ancient Abbasid Caliphate located in Iraq. Three areas were taken into account for the Ph.D. research: an octagonal city, three racecourses stadiums and the city of al-Mutawakkiliyya. Threats to which the site is exposed and its historical importance let the city to be inscribed in the list of UNESCO sites in danger (2007). This gave a reason more to investigate this area by means of SAR RADARSAT-2 and ALOS PALSAR satellites. SAR potentiality, in fact, is well known, in particular for those areas of the World where surveys in situ are not allowed because of political instability (as in the case of Samarra) and because of the possibility of acquiring with any cloud cover conditions and in any kind of illumination (day/night).
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Romero, Adriana. "Assisting the training of deep neural networks with applications to computer vision." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/316577.
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Deep learning has recently been enjoying an increasing popularity due to its success in solving challenging tasks. In particular, deep learning has proven to be effective in a large variety of computer vision tasks, such as image classification, object recognition and image parsing. Contrary to previous research, which required engineered feature representations, designed by experts, in order to succeed, deep learning attempts to learn representation hierarchies automatically from data. More recently, the trend has been to go deeper with representation hierarchies. Learning (very) deep representation hierarchies is a challenging task, which involves the optimization of highly non- convex functions. Therefore, the search for algorithms to ease the learning of (very) deep representation hierarchies from data is extensive and ongoing. In this thesis, we tackle the challenging problem of easing the learning of (very) deep representation hierarchies. We present a hyper-parameter free, off-the-shelf, simple and fast unsupervised algorithm to discover hidden structure from the input data by enforcing a very strong form of sparsity. We study the applicability and potential of the algorithm to learn representations of varying depth in a handful of applications and domains, highlighting the ability of the algorithm to provide discriminative feature representations that are able to achieve top performance. Yet, while emphasizing the great value of unsupervised learning methods when labeled data is scarce, the recent industrial success of deep learning has revolved around supervised learning. Supervised learning is currently the focus of many recent research advances, which have shown to excel at many computer vision tasks. Top performing systems often involve very large and deep models, which are not well suited for applications with time or memory limitations. More in line with the current trends, we engage in making top performing models more efficient, by designing very deep and thin models. Since training such very deep models still appears to be a challenging task, we introduce a novel algorithm that guides the training of very thin and deep models by hinting their intermediate representations. Very deep and thin models trained by the proposed algorithm end up extracting feature representations that are comparable or even better performing than the ones extracted by large state-of-the-art models, while compellingly reducing the time and memory consumption of the model.
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Kurtz, Camille. "Une approche collaborative segmentation - classification pour l'analyse descendante d'images multirésolutions." Phd thesis, Université de Strasbourg, 2012. http://tel.archives-ouvertes.fr/tel-00735217.
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Depuis la fin des années 1990, les images optiques à très hautes résolutions spatiales issues de capteurs satellitaires sont de plus en plus accessibles par une vaste communauté d'utilisateurs. En particulier, différents systèmes satellitaires sont maintenant disponibles et produisent une quantité de données importante, utilisable pour l'observation de la Terre. En raison de cet important volume de données,les méthodes analytiques manuelles deviennent inadaptées pour un traitement efficace de ces données. Il devient donc crucial d'automatiser ces méthodes par des procédés informatiques, capables de traiter cette quantité de données hétérogènes.Dans le cadre de cette thèse, nos recherches se sont focalisées sur le développement de nouvelles approches basées régions (i.e., segmentation et classification) permettant l'extraction de plusieurs niveaux de connaissance et d'information à partir d'ensembles d'images à différentes résolutions spatiales. De telles images offrent en effet des vues différentes de la scène étudiée, ce qui peut permettre de faciliter l'extraction des objets d'intérêt. Ces derniers étant structurés sous la forme de hiérarchies d'objets complexes, nos travaux se sont naturellement tournés (1) vers l'utilisation d'approches de segmentation hiérarchique fournissant des ensembles de partitions de la scène à différents niveaux de détail et (2) vers l'intégration de connaissances de haut-niveau dans les processus de fouille de données. De manière plus générale, nous nous sommes intéressés à élaborer un outil informatique reposant sur une stratégie d'analyse descendante,similaire à celle d'un utilisateur, qui consiste à interpréter la scène en considérant, en premier lieu, les grandes zones composant les territoires (à partir des images aux résolutions les plus grossières) puis à affiner récursivement le niveau d'interprétation pour en extraire des zones plus spécialisées (à partir des images aux résolutions les plus fines).L'ensemble de ces travaux a été implanté dans une bibliothèque logicielle et validé dans le contexte de l'analyse d'environnements urbains à partir d'ensembles d'images multi résolutions.
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(9187466), Bharath Kumar Comandur Jagannathan Raghunathan. "Semantic Labeling of Large Geographic Areas Using Multi-Date and Multi-View Satellite Images and Noisy OpenStreetMap Labels." Thesis, 2020.
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This dissertation addresses the problem of how to design a convolutional neural network (CNN) for giving semantic labels to the points on the ground given the satellite image coverage over the area and, for the ground truth, given the noisy labels in OpenStreetMap (OSM). This problem is made challenging by the fact that -- (1) Most of the images are likely to have been recorded from off-nadir viewpoints for the area of interest on the ground; (2) The user-supplied labels in OSM are frequently inaccurate and, not uncommonly, entirely missing; and (3) The size of the area covered on the ground must be large enough to possess any engineering utility. As this dissertation demonstrates, solving this problem requires that we first construct a DSM (Digital Surface Model) from a stereo fusion of the available images, and subsequently use the DSM to map the individual pixels in the satellite images to points on the ground. That creates an association between the pixels in the images and the noisy labels in OSM. The CNN-based solution we present yields a 4-8% improvement in the per-class segmentation IoU (Intersection over Union) scores compared to the traditional approaches that use the views independently of one another. The system we present is end-to-end automated, which facilitates comparing the classifiers trained directly on true orthophotos vis-`a-vis first training them on the off-nadir images and subsequently translating the predicted labels to geographical coordinates. This work also presents, for arguably the first time, an in-depth discussion of large-area image alignment and DSM construction using tens of true multi-date and multi-view WorldView-3 satellite images on a distributed OpenStack cloud computing platform.
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Uttam, Kumar *. "Algorithms For Geospatial Analysis Using Multi-Resolution Remote Sensing Data." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2280.
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Geospatial analysis involves application of statistical methods, algorithms and information retrieval techniques to geospatial data. It incorporates time into spatial databases and facilitates investigation of land cover (LC) dynamics through data, model, and analytics. LC dynamics induced by human and natural processes play a major role in global as well as regional scale patterns, which in turn influence weather and climate. Hence, understanding LC dynamics at the local / regional as well as at global levels is essential to evolve appropriate management strategies to mitigate the impacts of LC changes. This can be captured through the multi-resolution remote sensing (RS) data. However, with the advancements in sensor technologies, suitable algorithms and techniques are required for optimal integration of information from multi-resolution sensors which are cost effective while overcoming the possible data and methodological constraints. In this work, several per-pixel traditional and advanced classification techniques have been evaluated with the multi-resolution data along with the role of ancillary geographical data on the performance of classifiers.
Techniques for linear and non-linear un-mixing, endmember variability and determination of spatial distribution of class components within a pixel have been applied and validated on multi-resolution data. Endmember estimation method is proposed and its performance is compared with manual, semi-automatic and fully automatic methods of endmember extraction. A novel technique - Hybrid Bayesian Classifier is developed for per pixel classification where the class prior probabilities are determined by un-mixing a low spatial-high spectral resolution multi-spectral data while posterior probabilities are determined from the training data obtained from ground, that are assigned to every pixel in a high spatial-low spectral resolution multi-spectral data in Bayesian classification. These techniques have been validated with multi-resolution data for various landscapes with varying altitudes. As a case study, spatial metrics and cellular automata based models applied for rapidly urbanising landscape with moderate altitude has been carried out.
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"Acquisition and modeling of 3D irregular objects." Chinese University of Hong Kong, 1994. http://library.cuhk.edu.hk/record=b5888184.
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by Sai-bun Wong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.
Includes bibliographical references (leaves 127-131).
Abstract --- p.v
Acknowledgment --- p.vii
Chapter 1 --- Introduction --- p.1-8
Chapter 1.1 --- Overview --- p.2
Chapter 1.2 --- Survey --- p.4
Chapter 1.3 --- Objectives --- p.6
Chapter 1.4 --- Thesis Organization --- p.7
Chapter 2 --- Range Sensing --- p.9-30
Chapter 2.1 --- Alternative Approaches to Range Sensing --- p.9
Chapter 2.1.1 --- Size Constancy --- p.9
Chapter 2.1.2 --- Defocusing --- p.11
Chapter 2.1.3 --- Deconvolution --- p.14
Chapter 2.1.4 --- Binolcular Vision --- p.18
Chapter 2.1.5 --- Active Triangulation --- p.20
Chapter 2.1.6 --- Time-of-Flight --- p.22
Chapter 2.2 --- Transmitter and Detector in Active Sensing --- p.26
Chapter 2.2.1 --- Acoustics --- p.26
Chapter 2.2.2 --- Optics --- p.28
Chapter 2.2.3 --- Microwave --- p.29
Chapter 2.3 --- Conclusion --- p.29
Chapter 3 --- Scanning Mirror --- p.31-47
Chapter 3.1 --- Scanning Mechanisms --- p.31
Chapter 3.2 --- Advantages of Scanning Mirror --- p.32
Chapter 3.3 --- Feedback of Scanning Mirror --- p.33
Chapter 3.4 --- Scanning Mirror Controller --- p.35
Chapter 3.5 --- Point-to-Point Scanning --- p.39
Chapter 3.6 --- Line Scanning --- p.39
Chapter 3.7 --- Specifications and Measurements --- p.41
Chapter 4 --- The Rangefinder with Reflectance Sensing --- p.48-58
Chapter 4.1 --- Ambient Noises --- p.49
Chapter 4.2 --- Occlusion/Shadow --- p.49
Chapter 4.3 --- Accuracy and Precision --- p.50
Chapter 4.4 --- Optics --- p.53
Chapter 4.5 --- Range/Reflectance Crosstalk --- p.56
Chapter 4.6 --- Summary --- p.58
Chapter 5 --- Computer Generation of Range Map --- p.59-75
Chapter 5.1 --- Homogenous Transformation --- p.61
Chapter 5.2 --- From Global to Viewer Coordinate --- p.63
Chapter 5.3 --- Z-buffering --- p.55
Chapter 5.4 --- Generation of Range Map --- p.66
Chapter 5.5 --- Experimental Results --- p.68
Chapter 6 --- Characterization of Range Map --- p.76-90
Chapter 6.1 --- Mean and Gaussian Curvature --- p.76
Chapter 6.2 --- Methods of Curvature Generation --- p.78
Chapter 6.2.1 --- Convolution --- p.78
Chapter 6.2.2 --- Local Surface Patching --- p.81
Chapter 6.3 --- Feature Extraction --- p.84
Chapter 6.4 --- Conclusion --- p.85
Chapter 7 --- Merging Multiple Characteristic Views --- p.91-119
Chapter 7.1 --- Rigid Body Model --- p.91
Chapter 7.2 --- Sub-rigid Body Model --- p.94
Chapter 7.3 --- Probabilistic Relaxation Matching --- p.95
Chapter 7.4 --- Merging the Sub-rigid Body Model --- p.99
Chapter 7.5 --- Illustration --- p.101
Chapter 7.6 --- Merging Multiple Characteristic Views --- p.104
Chapter 7.7 --- Mislocation of Feature Extraction --- p.105
Chapter 7.7.1 --- The Transform Matrix for Perfect Matching --- p.106
Chapter 7.7.2 --- Introducing The Errors in Feature Set --- p.108
Chapter 7.8 --- Summary --- p.113
Chapter 8 --- Conclusion --- p.120-126
References --- p.127-131
Appendix A - Projection of Object --- p.A1-A2
Appendix B - Performance Analysis on Rangefinder System --- p.B1-B16
Appendix C - Matching of Two Characteristic views --- p.C1-C3
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Fauvel, Mathieu. "Spectral and Spatial Methods for the Classification of Urban Remote Sensing Data." Phd thesis, 2007. http://tel.archives-ouvertes.fr/tel-00258717.
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Lors de ces travaux, nous nous sommes intéressés au problème de la classification supervisée d'images satellitaires dezones urbaines. Les données traitées sont des images optiques à très hautes résolutions spatiales: données panchromatiques à très haute résolution spatiale (IKONOS, QUICKBIRD, simulations PLEIADES) et des images hyperspectrales (DAIS, ROSIS).
Deux stratégies ont été proposées.
La première stratégie consiste en une phase d'extraction de caractéristiques spatiales et spectrales suivie d'une phase de classification. Ces caractéristiques sont extraites par filtrages morphologiques : ouvertures et fermetures géodésiques et filtrages surfaciques auto-complémentaires. La classification est réalisée avec les machines à vecteurs supports (SVM)
non linéaires. Nous proposons la définition d'un noyau spatio-spectral utilisant de manière conjointe l'information spatiale
et l'information spectrale extraites lors de la première phase.\\
La seconde stratégie consiste en une phase de fusion de données pre- ou post-classification. Lors de la fusion postclassification,
divers classifieurs sont appliqués, éventuellement sur plusieurs données issues d'une même scène (image panchromat
ique, image multi-spectrale). Pour chaque pixel, l'appartenance à chaque classe est estimée à l'aide des classifieurs. Un schém
a de fusion adaptatif permettant d'utiliser l'information sur la fiabilité locale de chaque classifieur, mais aussi l'information globale disponible a priori sur les performances de chaque algorithme pour les différentes classes, est proposé
.
Les différents résultats sont fusionnés à l'aide d'opérateurs flous.
Les méthodes ont été validées sur des images réelles. Des
améliorations significatives sont obtenues par rapport aux méthodes publiées dans la litterature.
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Wiemker, Rafael. "The Color Constancy Problem in Multispectral Remote Sensing - On the Impact of Surface Orientation on Spectral Signatures." Phd thesis, 1997. http://tel.archives-ouvertes.fr/tel-00010773.
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"Monitoring Physiological Signals Using Camera." Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.41236.
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abstract: Monitoring vital physiological signals, such as heart rate, blood pressure and breathing pattern, are basic requirements in the diagnosis and management of various diseases. Traditionally, these signals are measured only in hospital and clinical settings. An important recent trend is the development of portable devices for tracking these physiological signals non-invasively by using optical methods. These portable devices, when combined with cell phones, tablets or other mobile devices, provide a new opportunity for everyone to monitor one’s vital signs out of clinic.
This thesis work develops camera-based systems and algorithms to monitor several physiological waveforms and parameters, without having to bring the sensors in contact with a subject. Based on skin color change, photoplethysmogram (PPG) waveform is recorded, from which heart rate and pulse transit time are obtained. Using a dual-wavelength illumination and triggered camera control system, blood oxygen saturation level is captured. By monitoring shoulder movement using differential imaging processing method, respiratory information is acquired, including breathing rate and breathing volume. Ballistocardiogram (BCG) is obtained based on facial feature detection and motion tracking. Blood pressure is further calculated from simultaneously recorded PPG and BCG, based on the time difference between these two waveforms.
The developed methods have been validated by comparisons against reference devices and through pilot studies. All of the aforementioned measurements are conducted without any physical contact between sensors and subjects. The work presented herein provides alternative solutions to track one’s health and wellness under normal living condition.Dissertation/Thesis
Doctoral Dissertation Electrical Engineering 2016
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LORENZI, Luca. "Development of an Innovative System for the Reconstruction of New Generation Satellite Images." Phd thesis, 2012. http://tel.archives-ouvertes.fr/tel-00816978.
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Les satellites de télédétection sont devenus incontournables pour la société civile. En effet, les images satellites ont été exploitées avec succès pour traiter plusieurs applications, notamment la surveillance de l'environnement et de la prévention des catastrophes naturelles. Dans les dernières années, l'augmentation de la disponibilité de très haute résolution spatiale (THR) d'images de télédétection abouti à de nouvelles applications potentiellement pertinentes liées au suivi d'utilisation des sols et à la gestion environnementale. Cependant, les capteurs optiques, en raison du fait qu'ils acquièrent directement la lumière réfléchie par le soleil, ils peuvent souffrir de la présence de nuages dans le ciel et / ou d'ombres sur la terre. Il s'agit du problème des données manquantes, qui induit un problème important et crucial, en particulier dans le cas des images THR, où l'augmentation des détails géométriques induit une grande perte d'informations. Dans cette thèse, de nouvelles méthodologies de détection et de reconstruction de la région contenant des données manquantes dans les images THR sont proposées et appliquées sur les zones contaminées par la présence de nuages et / ou d'ombres. En particulier, les contributions méthodologiques proposées comprennent: i) une stratégie multirésolution d'inpainting visant à reconstruire les images contaminées par des nuages ; ii) une nouvelle combinaison d'information radiométrique et des informations de position spatiale dans deux noyaux spécifiques pour effectuer une meilleure reconstitution des régions contaminés par les nuages en adoptant une régression par méthode a vecteurs supports (RMVS) ; iii) l'exploitation de la théorie de l'échantillonnage compressé avec trois stratégies différentes (orthogonal matching pursuit, basis pursuit et une solution d'échantillonnage compressé, basé sur un algorithme génétique) pour la reconstruction d'images contaminés par des nuages; iv) une chaîne de traitement complète qui utilise une méthode à vecteurs de supports (SVM) pour la classification et la détection des zones d'ombre, puis une régression linéaire pour la reconstruction de ces zones, et enfin v) plusieurs critères d'évaluation promptes à évaluer la performance de reconstruction des zones d'ombre. Toutes ces méthodes ont été spécialement développées pour fonctionner avec des images très haute résolution. Les résultats expérimentaux menés sur des données réelles sont présentés afin de montrer et de confirmer la validité de toutes les méthodes proposées. Ils suggèrent que, malgré la complexité des problèmes, il est possible de récupérer de façon acceptable les zones manquantes masquées par les nuages ou rendues erronées les ombres.
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MCQUAT, Gregory John. "Feature Extraction Workflows for Urban Mobile-Terrestrial LiDAR Data." Thesis, 2011. http://hdl.handle.net/1974/6530.
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Mobile Terrestrial LiDAR (MTL) is an active remote sensing technology that uses laser-based ranging and global positioning systems (GPS) to record 3D point location measurements on surfaces within and near transportation corridors, such as along a railroad track or a street. This thesis examines geovisualization for improving user-oriented workflows and also examines geographic object-based image analysis (GEOBIA) for the development of automated feature extraction. A LiDAR sensor-centric perspective during the data acquisition phase is used to organize data for the user and to transform the data into a 2D reference frame for object-oriented image analysis of MTL data.
Organizing the display of MTL data relative to the scanner presented new opportunities for visualization techniques and was an effective method for communicating space that was scanned, or not, in an urban scene. It offers new avenues for quality assessment of MTL survey of urban environments by explicitly displaying gaps in data coverage. A number of techniques for navigating and visualizing data from a sensor-perspective are examined.
A novel sensor-perspective transformation of MTL data from three to two dimensions enables analysis of MTL data in common GIS and image-processing environments. GEOBIA software (Definiens’ eCognition) is used to construct a procedural feature extraction workflow. The procedures are constructed with semantic classes, data processing rules and functions that drive geometric segmentation and feature recognition. Geometric regularities in urban scenes and knowledge about spatial and semantic relationships are incorporated into the rule set. The results are fluidly integrated back into a GIS environment.
Investigation of alternative approaches to handling MTL data such as those carried out in this thesis are essential if this technology is to see widespread use.Thesis (Master, Geography) -- Queen's University, 2011-05-24 13:10:15.198
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WANG, Bo. "Radar à synthèse d'ouverture polarimétrique pour la caractérisation de la surface de la mer et la détection de navire." Phd thesis, 2013. http://tel.archives-ouvertes.fr/tel-00979001.
Full textAbstract:
In our study, sea surface characteristics imaged by multi-polarization space-borne synthetic aperture radar (SAR) have been investigated. For the first time, a decomposition of different scattering mechanisms have been performed for ocean satellite SAR imagery to better understand the non-Bragg (Scalar) contribution to the total radar cross section (RCS) and Doppler measurements. Characteristics retrieval and target classification have been established, using polarimetry and Bayesian detection theories. There are generally three types of surface scattering mechanisms occurring when the sea surface is detected by microwave radar, i.e., Bragg, specular, and Rayleigh. Depolarized Bragg contribution corresponds to sea surface capillary wave, while the other two Scalar contributions correspond respectively to the crest of the longer wave before it breaks and foams formed by wave breaking. Different scattering mechanisms induce different polarimetric scattering coefficients and Doppler spectrum. It had been impossible to separate those scattering mechanisms with single polarization radar imageries. On pixel scale, we decomposed radar scattering matrices physically into Bragg and Scalar contributions. The decomposition is an iteration initiated with the radar incidence angle, and controlled by a local incidence angle which is function of co-polarization and cross-polarization. Based on these developments and testing, a strategy has been refined to analyze the signature of different features, to retrieve wind seas and sea swell parameters, as well as slick areas, ships, oil rigs, such polarized targets that may be buried in the Scalar contribution. With polarimetric scattering matrices estimated both for Bragg and Scalar contributions, a sea clutter model describing almost the real sea surface has been improved statistically. From this point, this improved model could be combined with Bayesian detectors to classify man-made metallic targets, such as ships, oil rigs, etc.