Дисертації з теми "Hyperspectral and multispectral data fusion"
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Vivone, Gemine. "Multispectral and hyperspectral pansharpening." Doctoral thesis, Universita degli studi di Salerno, 2014. http://hdl.handle.net/10556/1604.
Повний текст джерелаRemote sensing consists in measuring some characteristics of an object from a distance. A key example of remote sensing is the Earth observation from sensors mounted on satellites that is a crucial aspect of space programs. The first satellite used for Earth observation was Explorer VII. It has been followed by thousands of satellites, many of which are still working. Due to the availability of a large number of different sensors and the subsequent huge amount of data collected, the idea of obtaining improved products by means of fusion algorithms is becoming more intriguing. Data fusion is often exploited for indicating the process of integrating multiple data and knowledge related to the same real-world scene into a consistent, accurate, and useful representation. This term is very generic and it includes different levels of fusion. This dissertation is focused on the low level data fusion, which consists in combining several sources of raw data. In this field, one of the most relevant scientific application is surely the Pansharpening. Pansharpening refers to the fusion of a panchromatic image (a single band that covers the visible and near infrared spectrum) and a multispectral/hyperspectral image (tens/hundreds bands) acquired on the same area. [edited by author]
XII ciclo n.s.
Ahn, Byung Joon. "Design and development of a work-in-progress, low-cost Earth Observation multispectral satellite for use on the International Space Station." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587426345809705.
Повний текст джерелаJacq, Kévin. "Traitement d'images multispectrales et spatialisation des données pour la caractérisation de la matière organique des phases solides naturelles. High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core High-resolution grain size distribution of sediment core with 2 hyperspectral imaging Study of pansharpening methods applied to hyperspectral images of sediment cores." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAA024.
Повний текст джерелаThe evolution of the environment and climate are, currently, the focus of all attention. The impacts of the activities of present and past societies on the environment are in particular questioned in order to better anticipate the implications of our current activities on the future. Better describing past environments and their evolutions are possible thanks to the study of many natural recorders (sediments, speleothems, tree rings, corals). Thanks to them, it is possible to characterize biological-physical-chemical evolutions at di erent temporal resolutions and for di erent periods. The high resolution understood here as the su cient resolution for the study of the environment in connection with the evolution of societies constitutes the main lock of the study of these natural archives in particular because of the analytical capacity devices that can only rarely see ne inframillimetre structures. This work is built on the assumption that the use of hyperspectral sensors (VNIR, SWIR, LIF) coupled with relevant statistical methods should allow access to the spectral and therefore biological-physical-chemical contained in these natural archives at a spatial resolution of a few tens of micrometers and, therefore, to propose methods to reach the high temporal resolution (season). Besides, to obtain reliable estimates, several imaging sensors and linear spectroscopy (XRF, TRES) are used with their own characteristics (resolutions, spectral ranges, atomic/molecular interactions). These analytical methods are used for surface characterization of sediment cores. These micrometric spectral analyses are mapped to usual millimeter geochemical analyses. Optimizing the complementarity of all these data involves developing methods to overcome the di culty inherent in coupling data considered essentially dissimilar (resolutions, spatial shifts, spectral non-recovery). Thus, four methods were developed. The rst consists in combining hyperspectral and usual methods for the creation of quantitative predictive models. The second allows the spatial registration of di erent hyperspectral images at the lowest resolution. The third focuses on their merging with the highest of the resolutions. Finally, the last one focuses on deposits in sediments (laminae, oods, tephras) to add a temporal dimension to our studies. Through all this information and methods, multivariate predictive models were estimated for the study of organic matter, textural parameters and particle size distribution. The laminated and instantaneous deposits within the samples were characterized. These made it possible to estimate oods chronicles, as well as biological-physical-chemical variations at the season scale. Hyperspectral imaging coupled with data analysis methods are therefore powerful tools for the study of natural archives at ne temporal resolutions. The further development of the approaches proposed in this work will make it possible to study multiple archives to characterize evolutions at the scale of one or more watershed(s)
Benhalouche, Fatima Zohra. "Méthodes de démélange et de fusion des images multispectrales et hyperspectrales de télédétection spatiale." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30083/document.
Повний текст джерелаIn this thesis, we focused on two main problems of the spatial remote sensing of urban environments which are: "spectral unmixing" and "fusion". In the first part of the thesis, we are interested in the spectral unmixing of hyperspectral images of urban scenes. The developed methods are designed to unsupervisely extract the spectra of materials contained in an imaged scene. Most often, spectral unmixing methods (methods known as blind source separation) are based on the linear mixing model. However, when facing non-flat landscape, as in the case of urban areas, the linear mixing model is not valid any more, and must be replaced by a nonlinear mixing model. This nonlinear model can be reduced to a linear-quadratic/bilinear mixing model. The proposed spectral unmixing methods are based on matrix factorization with non-negativity constraint, and are designed for urban scenes. The proposed methods generally give better performance than the tested literature methods. The second part of this thesis is devoted to the implementation of methods that allow the fusion of multispectral and hyperspectral images, in order to improve the spatial resolution of the hyperspectral image. This fusion consists in combining the high spatial resolution of multispectral images and high spectral resolution of hyperspectral images. The implemented methods are designed for urban remote sensing data. These methods are based on linear-quadratic spectral unmixing techniques and use the non-negative matrix factorization. The obtained results show that the developed methods give good performance for hyperspectral and multispectral data fusion. They also show that these methods significantly outperform the tested literature approaches
Wahrman, Spencer A. "Time Series Analysis of Vegetation Change using Hyperspectral and Multispectral Data." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17473.
Повний текст джерелаGrand Lake, Colorado has experienced a severe mountain pine beetle outbreak over the past twenty years. The aim of this study was to map lodgepole pine mortality and health decline due to mountain pine beetle. Multispectral data spanning a five-year period from 2006 to 2011 were used to assess the progression from live, green trees to dead, gray-brown trees. IKONOS data from 2011 were corrected to reflectance and validated against an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset, also collected during 2011. These data were used along with additional reflectance-corrected multispectral datasets (IKONOS from 2007 and QuickBird from 2006 and 2009) to create vegetation classification maps using both library spectra and regions of interest. Two sets of classification maps were produced using Mixture-Tuned Matched Filtering. The results were assessed visually and mathematically. Through visual inspection of the classification maps, increasing lodgepole pine mortality over time was observed. The results were quantified using confusion matrices comparing the classification results of the AVIRIS classified data and the IKONOS and QuickBird classified data. The comparison showed that change could be seen over time, but due to the short time period of the data the change was not as significant as expected.
Hall, William D. "Exploration of Data Fusion between Polarimetric Radar and Multispectral Image Data." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17375.
Повний текст джерелаTypically, analysis of remote sensing data is limited to one sensor at a time which usually contains data from the same general portion of the electromagnetic spectrum. SAR and visible near infrared data of Monterey, CA, were analyzed and fused with the goal of achieving improved land classification results. A common SAR decomposition, the Pauli decomposition was performed and inspected. The SAR Pauli decomposition and the multispectral reflectance data were fused at the pixel level, then analyzed using multispectral classification techniques. The results were compared to the multispectral classifications using the SAR decomposition results for a basis of interpreting the changes. The combined dataset resulted in little to no quantitative improvement in land cover classification capability, however inspection of the classification maps indicated an improved classification ability with the combined data. The most noticeable increases in classification accuracy occurred in spatial regions where the land features were parallel to the SAR flight line. This dependence on orientation makes this fusion process more ideal for datasets with more consistent features throughout the scene.
PISCINI, ALESSANDRO. "Neural-Network approach to multispectral and hyperspectral data analysis for volcanic monitoring." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2015. http://hdl.handle.net/2108/214160.
Повний текст джерелаAdams, Andrew J. "Multispectral persistent surveillance /." Online version of thesis, 2008. http://hdl.handle.net/1850/7070.
Повний текст джерелаJahan, Farah. "Fusion of Hyperspectral and LiDAR Data for Land Cover Classification." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386555.
Повний текст джерелаThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Sharma, Rajeev. "Using multispectral and hyperspectral satellite data for early detection of mountain pine beetle damage." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31064.
Повний текст джерелаForestry, Faculty of
Graduate
Schneider, Sven. "A probablistic framework for classification and fusion of remotely sensed hyperspectral data." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9407.
Повний текст джерелаMutlu, Muge. "Mapping surface fuels using LIDAR and multispectral data fusion for fire behavior modeling." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1118.
Повний текст джерелаJordan, Johannes [Verfasser], Joachim [Akademischer Betreuer] Hornegger, and Joachim [Gutachter] Hornegger. "Interactive Analysis of Multispectral and Hyperspectral Image Data / Johannes Jordan ; Gutachter: Joachim Hornegger ; Betreuer: Joachim Hornegger." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2017. http://d-nb.info/1156780985/34.
Повний текст джерелаChen, Hang. "Optical Encryption Techniques for Color Image and Hyperspectral Data." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0374.
Повний текст джерелаOptical information security is one of the most important research directions in information science and technology, especially in the field of copyright protection, confidential information transmission/storage and military remote sensing. Since double random phase encoding technology (DRPE) was proposed, optical image encryption technology has become the main topic of optical information security and it has been developed and studied deeply. Optical encryption techniques offer the possibility of high-speed parallel processing of two dimension image data and hiding information in many different dimensions. In this context, much significant research and investigation on optical image encryption have been presented based on DRPE or further optical operation, such as digital holography, Fresnel transform, gyrator transform. Simultaneously, the encrypted image has been extended from single gray image to double image, color image and multi-image. However, the hyperspectral image, as a significant element in military and commercial remote sensing, has not been deeply researched in optical encryption area until now. This work extends the optical encryption technology from color image to hyperspectral image. For better comprehension of hyperspectral image encryption, this work begins with the introduction and analysis of the characteristics of hyperspectral cube. Subsequently, several kinds of encryption schemes for color image, including symmetric and asymmetric cryptosystem, are presented individually. Furthermore, the optical encryption algorithms for hyperspectral cube are designed for securing both the spatial and spectral information simultaneously. Some numerical simulations are given to validate the performance of the proposed encryption schemes. The corresponding attack experiment results demonstrate the capability and robustness of the approaches designed in this work. The research in this dissertation provides reference for the further practicality of hyperspectral image encryption
Polat, Songül. "Combined use of 3D and hyperspectral data for environmental applications." Thesis, Lyon, 2021. http://www.theses.fr/2021LYSES049.
Повний текст джерелаEver-increasing demands for solutions that describe our environment and the resources it contains, require technologies that support efficient and comprehensive description, leading to a better content-understanding. Optical technologies, the combination of these technologies and effective processing are crucial in this context. The focus of this thesis lies on 3D scanning and hyperspectral technologies. Rapid developments in hyperspectral imaging are opening up new possibilities for better understanding the physical aspects of materials and scenes in a wide range of applications due to their high spatial and spectral resolutions, while 3D technologies help to understand scenes in a more detailed way by using geometrical, topological and depth information. The investigations of this thesis aim at the combined use of 3D and hyperspectral data and demonstrates the potential and added value of a combined approach by means of different applications. Special focus is given to the identification and extraction of features in both domains and the use of these features to detect objects of interest. More specifically, we propose different approaches to combine 3D and hyperspectral data depending on the HSI/3D technologies used and show how each sensor could compensate the weaknesses of the other. Furthermore, a new shape and rule-based method for the analysis of spectral signatures was developed and presented. The strengths and weaknesses compared to existing approach-es are discussed and the outperformance compared to SVM methods are demonstrated on the basis of practical findings from the field of cultural heritage and waste management.Additionally, a newly developed analytical method based on 3D and hyperspectral characteristics is presented. The evaluation of this methodology is based on a practical exam-ple from the field of WEEE and focuses on the separation of materials like plastics, PCBs and electronic components on PCBs. The results obtained confirms that an improvement of classification results could be achieved compared to previously proposed methods.The claim of the individual methods and processes developed in this thesis is general validity and simple transferability to any field of application
Aval, Josselin. "Automatic mapping of urban tree species based on multi-source remotely sensed data." Thesis, Toulouse, ISAE, 2018. http://www.theses.fr/2018ESAE0021/document.
Повний текст джерелаWith the expansion of urban areas, air pollution and heat island effect are increasing, leading to state of health issues for the inhabitants and global climate changes. In this context, urban trees are a valuable resource for both improving air quality and promoting freshness islands. On the other hand, canopies are subject to specific conditions in the urban environment, causing the spread of diseases and life expectancy decreases among the trees. This thesis explores the potential of remote sensing for the automatic urban tree mapping, from the detection of the individual tree crowns to their species estimation, an essential preliminary task for designing the future green cities, and for an effective vegetation monitoring. Based on airborne hyperspectral, panchromatic and Digital Surface Model data, the first objective of this thesis consists in taking advantage of several data sources for improving the existing urban tree maps, by testing different fusion strategies (feature and decision level fusion). The nature of the results led us to optimize the complementarity of the sources. In particular, the second objective is to investigate deeply the richness of the hyperspectral data, by developing an ensemble classifiers approach based on vegetation indices, where the classifiers are species specific. Finally, the first part highlighted to interest of discriminating the street trees from the other structures of urban trees. In a Marked Point Process framework, the third objective is to detect trees in urban alignment. Through the first objective, this thesis demonstrates that the hyperspectral data are the main driver of the species prediction accuracy. The decision level fusion strategy is the most appropriate one for improving the performance in comparison the hyperspectral data alone, but slight improvements are obtained (a few percent) due to the low complementarity of textural and structural features in addition to the spectral ones. The ensemble classifiers approach developed in the second part allows the tree species to be classified from ground-based references, with significant improvements in comparison to a standard feature level classification approach. Each extracted species classifier reflects the discriminative spectral attributes of the species and can be related to the expertise of botanists. Finally, the street trees can be mapped thanks to the proposed MPP interaction term which models their contextual features (alignment and similar heights). Many improvements have to be explored such as the more accurate tree crown delineation, and several perspectives are conceivable after this thesis, among which the state of health monitoring of the urban trees
Lee, Mark. "Benthic mapping of coastal waters using data fusion of hyperspectral imagery and airborne laser bathymetry." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0000730.
Повний текст джерелаJacob, Alexander. "Radar and Optical Data Fusion for Object Based Urban Land Cover Mapping." Thesis, KTH, Geoinformatik och Geodesi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-45978.
Повний текст джерелаDragon 2 Project
Filiberti, Daniel Paul. "Combined Spatial-Spectral Processing of Multisource Data Using Thematic Content." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1066%5F1%5Fm.pdf&type=application/pdf.
Повний текст джерелаLachaize, Marie. "Fusion de données : approche evidentielle pour le tri des déchets." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS113.
Повний текст джерелаAutomatic waste sorting is a complex matterbecause of the diversity of the objects and of the presentmaterials. It requires input from various andheterogeneous data. This PhD work deals with the datafusion problem derived from an acquisition devicecomposed of three sensors, including an hyperspectralsensor in the NIR field. We first studied the benefit ofusing the belief function theory framework (BFT)throughout the fusion approach, using in particularconflict measures to drive the process. We first studiedthe BFT in the multiclass classification problem createdby hyperspectral data. We used the Error CorrectingOutput Codes (ECOC) framework which consists inseparating the multiclass problem into several binaryones, simpler to solve. The questions of the idealdecomposition of the multiclass problem (coding) and ofthe answer combination coming from the binaryclassifiers (decoding) are still open-ended questions. Thebelief function framework allows us to propose adecoding step modelling each binary classifier as anindividual source of information, thanks to the possibilityof handling compound hypotheses. Besides, the BFTprovides indices to detect non reliable decisions whichallow for an auto-evaluation of the method performedwithout using any ground truth. In a second part dealingwith the data fusion,we propose an evidential version ofan object-based approach composed with a segmentationmodule and a classification module in order to tackle theproblems of the differences in scale, resolutions orregistrations of the sensors. The objective is then toestimate a relevant spatial support corresponding to theobjects while labelling them in terms of material. Weproposed an interactive approach with cooperationbetween the two modules in a cross-validation kind ofway. This way, the reliability of the labelling isevaluated at the segment level, while the classificationinformation acts on the initial segments in order toevolve towards an object level segmentation: consensusamong the classification information within a segment orbetween adjacent regions allow the spatial support toprogressively reach object level
Tusa, jumbo Eduardo Alejandro. "Apport de la fusion LiDAR - hyperspectral pour la caractérisation géométrique et radiométrique des arbres." Thesis, Université Grenoble Alpes, 2020. https://tel.archives-ouvertes.fr/tel-03212453.
Повний текст джерелаMountain forests provide environmental ecosystem services (EES) to communities: supplying of recreational landscapes, protection against natural hazards, supporting biodiversity conservation, among others. The preservation of these EES through space and time requires a good characterization of the resources. Especially in mountains, stands are very heterogeneous and timber harvesting is economically possible thanks to trees of higher value. This is why we want to be able to map each tree and estimate its characteristics, including quality, which is related to its shape and growth conditions. Field inventories are not able to provide a wall to wall cover of detailed tree-level information on a large scale. On the other hand, remote sensing tools seem to be a promising technology because of the time efficient and the affordable costs for studying forest areas. LiDAR data provide detailed information from the vertical distribution and location of the trees, but it is limited for mapping species. Hyperspectral data are associated to absorption features in the canopy reflectance spectrum, but is not effective for characterizing tree geometry. Hyperspectral and LiDAR systems provide independent and complementary data that are relevant for the assessment of biophysical and biochemical attributes of forest areas. This PhD thesis deals with the fusion of LiDAR and hyperspectral data to characterize individual forest trees. The leading idea is to improve methods to derive forest information at tree-level by extracting geometric and radiometric features. The contributions of this research work relies on: i) an updated review of data fusion methods of LiDAR and hyperspectral data for forest monitoring, ii) an improved 3D segmentation algorithm for delineating individual tree crowns based on Adaptive Mean Shift (AMS3D) and an ellipsoid crown shape model, iii) a criterion for feature selection based on random forests score, $5$-fold cross validation and a cumulative error function for forest tree species classification. The two main methods used to derive forest information at tree level are tested with remote sensing data acquired in the French Alps
Hunger, Sebastian, Pierre Karrasch, and Christine Wessollek. "Evaluating the potential of image fusion of multispectral and radar remote sensing data for the assessment of water body structure." SPIE, 2016. https://tud.qucosa.de/id/qucosa%3A34859.
Повний текст джерелаWaheed, Tahir. "Artificial intelligence analysis of hyperspectral remote sensing data for management of water, weed, and nitrogen stresses in corn fields." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86060.
Повний текст джерелаA three factor split-split-plot experiment, with four randomized blocks as replicates, was established during the growing seasons of 2003 and 2004. Corn (Zea mays L.) hybrid DKC42-22 was grown because this hybrid is a good performer on light soils in Quebec. There were twelve 12 x 12m plots in a block (one replication per treatment per block) and the total number of plots was 48. Water stress was the main factor in the experiment. A drip irrigation system was laid out and each block was split into irrigated and non-irrigated halves. The second main factor of the experiment was weeds with two levels i.e. full weed control and no weed control. Weed treatments were assigned randomly by further splitting the irrigated and non-irrigated sub-blocks into two halves. Each of the weed treatments was furthermore split into three equal sub-sub-plots for nitrogen treatments (third factor of the experiment). Nitrogen was applied at three levels i.e. 50, 150 and 250 kg N ha-1 (Quebec norm is between 120-160 kg N ha-1).
The hyperspectral data were recorded (spectral resolution = 1 nm) mid-day (between 1000 and 1400 hours) with a FieldSpec FR spectroradiometer over a spectral range of 400-2500 run at three growth stages namely: early growth, tasseling and full maturity, in each of the growing season.
There are two major original contributions in this thesis: First is the development of a hyperspectral data analysis procedure for separating visible (400-700 nm), near-infrared (700-1300 nm) and mid-infrared (1300-2500 nm) regions of the spectrum for use in discriminant analysis procedure. In addition, of all the spectral band-widths analyzed, seven waveband-aggregates were identified using STEPDISC procedure, which were the most effective for classifying combined water, weed, and nitrogen stress. The second contribution is the successful classification of hyperspectral observations acquired over an agricultural field, using three innovative artificial intelligence approaches; support vector machines (SVM), genetic algorithms (GA) and decision tree (DT) algorithms. These AI approaches were used to evaluate a combined effect of water, weed and nitrogen stresses in corn and of all the three AI approaches used, SVM produced the best results (overall accuracy ranging from 88% to 100%).
The general conclusion is that the conventional statistical and artificial intelligence techniques used in this study are all useful for quickly mapping combined affects of irrigation, weed and nitrogen stresses (with overall accuracies ranging from 76% to 100%). These approaches have strong potential and are of great benefit to those investigating the in-season impact of irrigation, weed and nitrogen management for corn crop production and other environment related challenges.
Rocha, de Oliveira Rodrigo. "Development and implementation of strategies for process data fusion, modelling and control." Doctoral thesis, Universitat de Barcelona, 2022. http://hdl.handle.net/10803/673296.
Повний текст джерелаAmb l'arribada de la Indústria 4.0 i la creixent disponibilitat de sensors i sistemes d'adquisició de dades, els processos de fabricació moderns generen quantitats ingents de dades de procés a una escala mai vista. Durant les últimes dècades, el desenvolupament continuat de metodologies d'anàlisi de processos basades en la interpretació directa de la mesura ha confirmat la importància de l'anàlisi multivariant de dades en aquest camp. Tot i així, caldrà desenvolupar noves aproximacions inspirades en metodologies existents o encara per descobrir per afrontar els nous reptes que planteja la revolució digital en l'anàlisi de processos. Aquesta tesi s'ha centrat en el desenvolupament i aplicació d'eines quimiomètriques lligades a la tecnologia analítica de processos (PAT) per al seguiment, modelització i control de processos per lots. Tota la metodologia proposada ha estat provada en processos reals de diversa naturalesa monitorats amb sensors de diferents tipologies. Les eines quimiomètriques desenvolupades en aquesta tesi estan pensades per ser utilitzades en dos contextos diferents: a) el seguiment, modelització i control de processos mitjançant sondes espectroscòpiques i sensors de procés, i b) el seguiment de processos mitjançant imatges hiperespectrals. En el context del monitoratge de processos mitjançant sondes espectroscòpiques i sensors de procés, s'han dissenyat diferents metodologies per gestionar la informació procedent de dades de procés per lots sincronitzats i no sincronitzats. Per a dades de lots sincronitzats, s'han dissenyat noves estratègies per al control estadístic multivariant de processos (MSPC, Multivariate Statistical Process Control) offline i online. Els models MSPC offline, destinats a controlar lots complets, es van construir a partir d'informació associada a variables originals de sensors o d'informació espectral comprimida, procedent de resultats de models d'anàlisi exploratòria i de resolució multivariant. Les metodologies de control de processos online es van basar en l'ús de models locals de MSPC construïts explorant l'efecte de diferents dissenys de finestres de temps de procés sobre la capacitat de discriminar observacions seguint condicions normals d’operació (NOC, Normal Operation Conditions) d’observacions amb un comportament anòmal. Per a les dades de lots no sincronitzats, es va proposar una nova metodologia MSPC online exempta de l’etapa de sincronització per fer un seguiment de l'evolució i el control del procés basada en l’ús d'una trajectòria global del procés per lots, que serveix per a la construcció de models locals de MSPC. Una millora clara dels resultats associada a tots els escenaris de models MSPC està vinculada a l'ús de noves estratègies de fusió de dades de nivell intermedi (mid-level data fusion). La nova contribució d'aquesta tesi és l'extensió de la idea de fusió de dades a la incorporació tant de respostes de sensors diversos com de resultats de models multivariants obtinguts de respostes d’un mateix sensor, però relacionats amb diferents tasques de modelització. Aquests resultats de models multivariants, que aporten informació molt més específica que els scores de PCA, per exemple, permeten una tria més acurada de la informació que s’introdueix en els models MSPC i faciliten una millor interpretació de les causes de comportaments anòmals en el procés. Les solucions quimiomètriques proposades per al seguiment de processos mitjançant imatges hiperespectrals (HSI, Hyperspectral Images) es van orientar principalment a aprofitar la informació espacial de la mesura per a l'avaluació qualitativa i quantitativa de l'heterogeneïtat en els processos de mescla. La descripció qualitativa de l'heterogeneïtat està vinculada al resultat de l’anàlisi de resolució multivariant de les dades HSI, que proporciona mapes de distribució de components purs que ofereixen una bona representació visual de la uniformitat en la distribució espacial dels diferents materials en la mescla estudiada. La caracterització quantitativa de l'heterogeneïtat s'obté de l'anàlisi variogràfica dels mapes de distribució i està basada en dos índexs: l'índex d'heterogeneïtat global (GHI, Global Heterogeneity Index), relacionat amb la dispersió dels valors de concentració dels píxels individuals, i l'índex d'uniformitat distribucional (DUI, Distributional Uniformity Index), que descriu l'heterogeneïtat distribucional, normalment ignorada en plantejaments tradicionals, que expressa el grau d’uniformitat en la distribució espacial dels diferents materials que formen una mescla. S'ha demostrat que aquests índexs són una eina PAT potent per caracteritzar l'heterogeneïtat dels processos de mescla seguits amb mesures discretes o en temps real mitjançant imatgeria hiperespectral d’infraroig proper (NIR-HSI). Per al seguiment de processos en temps real basat en imatges, s'ha adaptat una extensió d'aquesta metodologia, anomenada SWiVIA (Sliding Window Variographic Image Analysis – Anàlisi variogràfica d’imatges basada en finestres mòbils), per a l'avaluació en temps real de l'heterogeneïtat en el seguiment continu de processos. La versatilitat de la metodologia SWiVIA permet l'avaluació de l'heterogeneïtat amb la resolució temporal i l'escala espacial d'escrutini desitjada segons les característiques del procés de mescla estudiat.
Giordano, Sébastien. "Démélange d'images radar polarimétrique par séparation thématique de sources." Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1176/document.
Повний текст джерелаLand cover is a layer of information of significant interest for land management issues. In this context, combining remote sensing observations of different types is expected to produce more reliable results on land cover classification. The objective of this work is to explore the use of polarimetric radar images in association with co-registered higher resolution optical images. Extracting information from a polarimetric representation consists in decomposing it with target decomposition algorithms. Understanding these mechanisms is challenging as they are mixed inside the radar cell resolution but it is the key to producing a reliable land cover classification. The problem while using these target decomposition algorithms is that average physical parameters are obtained. As a result, each land cover type of a mixed pixel might not be well described by the average polarimetric parameters. The effect is all the more important as speckle affecting radar observations requires a local estimation of the polarimetric matrices. In this context, we chose to assess whether optical images can improve the understanding of radar images at the observation scale so as to retrieve more information. Spatial and spectral unmixing methods, traditionally designed for optical image fusion, were found to be an interesting framework. As a consequence, the idea of unmixing physical radar scattering mechanisms with the optical images is proposed. The original method developed is the decomposition of the polarimetric information, based on land cover type. This thematic decomposition is performed before applying usual target decomposition algorithms. A linear mixing model for radar images and an unmixing algorithm are proposed in this document. Having pointed out that the linear unmixing model is able to split off polarimetric information on a land cover type basis, the information contained in the unmixed matrices is evaluated. The assesment is carried out with generated simulated data and polarimetric radar images from the Radarsat-2 satellite. For this experiment, textit {Bare soil} and textit {Forested area} were considered for land cover types. It was found that despite speckle the reconstructed radar information after the unmixing is statically relevant with the observations. Moreover, the unmixing algorithm is capable of assimilating information from optical images. The question whether the unmixed radar images contain relevant thematic information is more challenging. Results on real and simulated data show that this capacity depends on the types of land cover considered and their respective proportions. Future work will be carried out to make the estimation step more robust to speckle and to test this unmixing algorithm on longer wavelength radar images. In this case, this method could be used to have a better estimation of vegetation biomass in the context of open forested areas
Elatawneh, Alata [Verfasser], Thomas F. [Akademischer Betreuer] Knoke, and Xiaoxiang [Akademischer Betreuer] Zhu. "Investigations into the potentials of hyperspectral and multi-seasonal / multispectral satellites data for forest parameter determination / Alata Elatawneh. Gutachter: Xiaoxiang Zhu ; Thomas F. Knoke. Betreuer: Thomas F. Knoke." München : Universitätsbibliothek der TU München, 2015. http://d-nb.info/1074999517/34.
Повний текст джерелаBenmoussat, Mohammed Seghir. "Hyperspectral imagery algorithms for the processing of multimodal data : application for metal surface inspection in an industrial context by means of multispectral imagery, infrared thermography and stripe projection techniques." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4347/document.
Повний текст джерелаThe work presented in this thesis deals with the quality control and inspection of industrial metallic surfaces. The purpose is the generalization and application of hyperspectral imagery methods for multimodal data such as multi-channel optical images and multi-temporal thermographic images. In the first application, data cubes are built from multi-component images to detect surface defects within flat metallic parts. The best performances are obtained with multi-wavelength illuminations in the visible and near infrared ranges, and detection using spectral angle mapper with mean spectrum as a reference. The second application turns on the use of thermography imaging for the inspection of nuclear metal components to detect surface and subsurface defects. A 1D approach is proposed based on using the kurtosis to select 1 principal component (PC) from the first PCs obtained after reducing the original data cube with the principal component analysis (PCA) algorithm. The proposed PCA-1PC method gives good performances with non-noisy and homogeneous data, and SVD with anomaly detection algorithms gives the most consistent results and is quite robust to perturbations such as inhomogeneous background. Finally, an approach based on fringe analysis and structured light techniques in case of deflectometric recordings is presented for the inspection of free-form metal surfaces. After determining the parameters describing the sinusoidal stripe patterns, the proposed approach consists in projecting a list of phase-shifted patterns and calculating the corresponding phase-images. Defect location is based on detecting and analyzing the stripes within the phase-images
Berger, Christian [Verfasser], Sören Mathias [Gutachter] Hese, Christiane [Gutachter] Schmullius, and Hannes [Gutachter] Taubenböck. "Fusion of high spatial resolution multispectral & object height data for urban environmental monitoring : methods & applications / Christian Berger ; Gutachter: Sören Mathias Hese, Christiane Schmullius, Hannes Taubenböck." Jena : Friedrich-Schiller-Universität Jena, 2017. http://d-nb.info/1177597705/34.
Повний текст джерелаGrohnfeldt, Claas Hendrik [Verfasser], Xiaoxiang [Akademischer Betreuer] [Gutachter] Zhu, Richard [Gutachter] Bamler, and Naoto [Gutachter] Yokoya. "Multi-sensor Data Fusion for Multi- and Hyperspectral Resolution Enhancement Based on Sparse Representations / Claas Hendrik Grohnfeldt ; Gutachter: Richard Bamler, Xiaoxiang Zhu, Naoto Yokoya ; Betreuer: Xiaoxiang Zhu." München : Universitätsbibliothek der TU München, 2017. http://d-nb.info/1140835181/34.
Повний текст джерелаBacher, Raphael. "Méthodes pour l'analyse des champs profonds extragalactiques MUSE : démélange et fusion de données hyperspectrales ;détection de sources étendues par inférence à grande échelle." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAT067/document.
Повний текст джерелаThis work takes place in the context of the study of hyperspectral deep fields produced by the European 3D spectrograph MUSE. These fields allow to explore the young remote Universe and to study the physical and chemical properties of the first galactical and extra-galactical structures.The first part of the thesis deals with the estimation of a spectral signature for each galaxy. As MUSE is a terrestrial instrument, the atmospheric turbulences strongly degrades the spatial resolution power of the instrument thus generating spectral mixing of multiple sources. To remove this issue, data fusion approaches, based on a linear mixing model and complementary data from the Hubble Space Telescope are proposed, allowing the spectral separation of the sources.The second goal of this thesis is to detect the Circum-Galactic Medium (CGM). This CGM, which is formed of clouds of gas surrounding some galaxies, is characterized by a spatially extended faint spectral signature. To detect this kind of signal, an hypothesis testing approach is proposed, based on a max-test strategy on a dictionary. The test statistics is learned on the data. This method is then extended to better take into account the spatial structure of the targets, thus improving the detection power, while still ensuring global error control.All these developments are integrated in the software library of the MUSE consortium in order to be used by the astrophysical community.Moreover, these works can easily be extended beyond MUSE data to other application fields that need faint extended source detection and source separation methods
Burian, František. "Tvorba multispektrálních map v mobilní robotice." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-233689.
Повний текст джерелаMartins, George Deroco [UNESP]. "Inferência dos níveis de infecção por Nematoides na cultura cafeeira a partir de dados de sensoriamento remoto adquiridos em multiescala." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/148760.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Os nematoides são importantes fitoparasitas que se constituem em um problema sério para o cultivo do café no Brasil. Como a ocorrência de nematoides no sistema radicular do cafeeiro causa desequilíbrios nutricionais na planta que provocam variações na resposta espectral da folha e define uma configuração espacial característica às áreas infectadas, o objetivo desta pesquisa avaliar o potencial de dados de sensoriamento remoto adquiridos em multiescala para discriminar e mapear o café sadio, em estágio inicial de infecção e severamente infectado. A pesquisa foi desenvolvida em três áreas experimentais, localizadas no sul do estado de Minas Gerais, nas quais foi certificada a ocorrência de nematoides e realizadas medições de variáveis biofísicas e dados hiperespectrais na folha e sobre o dossel da planta. Os dados hiperespectrais também foram utilizados em simulação de bandas dos sensores do RapidEye e OLI/Landsat 8 para identificar as faixas espectrais mais sensíveis para a discriminação de patógenos em plantas de café. Nenhum dos parâmetros biofísicos avaliados discriminou eficientemente as folhas de plantas sadias e infectadas, mas a simulação de bandas indicou que os intervalos espectrais do vermelho, vermelho limítrofe e infravermelho próximos do RapidEye foram complementares para a discriminação de plantas de café sadio e dos dois níveis de infecção. Essas bandas, mais uma imagem NDVI, foram utilizadas na classificação das áreas infectadas por nematoides, a qual definiu a distribuição espacial de café sadio e dos dois níveis de infecção, com uma acurácia global de 78% e coeficiente kappa de 0,71. A classificação não supervisionada da imagem multiespectral OLI/Landsat 8 também definiu as três condições, porém com baixa confiabilidade (coeficiente kappa igual a 0,41). Por outro lado, uma inferência espacial quantitativa da concentração de nematoides/cm³ no solo, a partir de um modelo empírico baseado na imagem RapidEye, apresentou um erro consideravelmente alto (21,89%).
Nematodes are important phytoparasites that constitute a serious issue for coffee cultivation in Brazil. Because root infection by nematodes induces spectral variation in leaves and defines a unique spatial configuration in the cultivation field, the aim of this study is to evaluate the potential of remote sensing data acquired in multiscale to discriminate and map healthy, early infected and severely infected coffee plants. This study was carried out in three experimental areas, located in the in southern Minas Gerais State, in which the occurrence of nematodes was certified and biophysical and hyperspectral measurements of the leaves and on the canopy were made. Hyperspectral data were also used to simulate the bands of the RapidEye and OLI/Landsat 8 sensors to identify the most sensitive spectral ranges for pathogen discrimination in coffee plants. None of the biophysical parameters efficiently discriminated the leaves of healthy and infected plants, but the band simulations indicated that red, red edge and near infrared spectral ranges were complementary to the discrimination of healthy coffee plants and the two levels of infection. These bands, plus an (NDVI) image, were used for a multispectral classification of healthy and nematode-infected areas. The multispectral classification defined the spatial distribution of healthy, early infected and two levels of infection, with an overall accuracy of 78% and kappa coefficient of 0.71. The unsupervised classification of the multispectral image OLI/Landsat 8 also defined the three conditions, but with low reliability (kappa coefficient equal to 0.41). In contrast, a quantitative spatial inference of the soil nematode concentration/cm³, from an empirical model based on the RapidEye image, presented a considerably high error (21.89%).
gomez, cecile. "Potentiels des données de télédétection multisources pour la cartographie géologique : Application à la région de Rehoboth (Namibie)." Phd thesis, Université Claude Bernard - Lyon I, 2004. http://tel.archives-ouvertes.fr/tel-00008556.
Повний текст джерелаGomez, Cécile. "Potentiels des données de télédétection multisources pour la cartographie géologique : Application à la région de Rehoboth (Namibie)." Phd thesis, Université Claude Bernard - Lyon I, 2004. http://tel.archives-ouvertes.fr/tel-00665112.
Повний текст джерелаAhmad, Touseef. "Augmenting Hyperspectral Image Unmixing Models Using Spatial Correlation, Spectral Variability, And Sparsity." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6081.
Повний текст джерелаChen, Yang-Chi 1973. "Knowledge-based learning for classification of hyperspectral data." 2007. http://hdl.handle.net/2152/15971.
Повний текст джерелаMehta, Viraj Kirankumar. "Data fusion of multispectral remote sensing measurements using wavelet transform." 2003. http://www.lib.ncsu.edu/theses/available/etd-03282003-133133/unrestricted/etd.pdf.
Повний текст джерелаUnni, V. S. "Efficient and Convergent Algorithms for High-Fidelity Hyperspectral Image Fusion." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5916.
Повний текст джерелаShieh, Chia-Sheng, and 謝嘉聲. "A Study on the Data Fusion for SPOT Multispectral and anchromatic Imagery." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/50277785057887986523.
Повний текст джерела國立交通大學
土木工程研究所
82
The integration of differebt data sorce inGIS has received increasing emphasis as a result of new development in computer and GIS techgnology. Integration of various data sets to fully utilize complementary information hsa become an important component. In recent years, there has been a large increase in the amount of image data that are available to users for various purposes. The launch of the French SPOT satellite system has given the capability to a range of land use and land cover analyses. This thesis compares the results of diffenent methods used to integrate the information contents of the SPOT Panchromatic and Multispectral image data. Four integrating methods, namely, clour space transformation, principal component analysis, high pass filter and radiometric method are ivvestigated. To evaluate the result of different merging methods, six measures are used. They are visual inspection, correlation, root mean square error, difference of two images, entropy value and the histogram comparison.
Ferraz, Óscar Almeida. "Combining low-power with parallel processing for multispectral and hyperspectral image compression." Master's thesis, 2019. http://hdl.handle.net/10316/88005.
Повний текст джерелаO CCSDS 123 é um algoritmo de compressão de imagens hiperespectrais e multiespectrais composto por um preditor e um codificador. Normalmente, os sistemas que geram este tipo de imagens (satélites, drones, etc…) têm restrições energéticas. Este algoritmo é implementado, sobretudo em FPGAs devido ao seu baixo consumo energético. O mercado dos smartphones tem tornado os CPUs e GPUs em dispositivos energeticamente eficientes, colocando-os em posição de competir contra as FPGAs no campo de compressão de baixo consumo.O objetivo desta dissertação é, utilizando uma Jetson TX2, paralelizar o CCSDS-123. No preditor, quando a predição é intra-banda (P=0), é utilizado um único kernel. Quando se usa predição inter-banda (P>0), o preditor passa a ter dependências de dados dentro das bandas, tornando a paralelização menos eficiente e mais difícil de implementar. No codificador, que contém dependências de dados, são estudadas paralelizações utilizando vários dispositivos (CPU+GPU) nos dois codificadores contemplados nesta norma. Produzindo uma solução híbrida de computação heterogénea.As implementações são alvo de testes que compararam o tempo de execução paralela com os tempos execução em série de forma a identificar as melhores implementações. Ainda é feita uma análise energética medindo a potência utilizada pela placa ao longo do tempo de execução do algoritmo. No final, a taxa de débito e a eficiência energética são comparadas com o estado de arte.O uso de GPUs de baixo consumo traz um novo paradigma ao campo de compressão multiespectral e hiperespectral. Apesar de não tão eficientes como as FPGAs, GPUs conseguem altas taxas de débito.
The CCSDS 123 is a hyperspectral and multispectral image compression algorithm composed of a predictor and an encoder. Usually, the systems that generate these types of images (satellites, drones, etc.) have energy restrictions. Hence, FPGAs show themselves as efficient devices to implement the CCSDS 123 due to its low energy consumption. The smartphone market has turned CPUs and GPUs into energy-efficient systems, making them potential competitors against FPGAs implementation dominance in the field of low-energy compression.The objective of this dissertation is, using a low-power GPU (Jetson TX2), to parallelize the CCSDS 123. Intra-band prediction (P=0) uses a single kernel. When using inter-band prediction (P>0), the predictor has data dependencies within bands, making parallelization less efficient and more challenging to implement. Hybrid parallelizations (CPU+GPU) are studied for the two encoders designed for this standard, producing a heterogeneous computing system.The implementations are subject to tests that compare the parallel execution times with the serial execution times in order to identify the best implementations. An energy analysis is performed, measuring the power used by the board over the algorithm's running time. In the end, the throughput rate and energy efficiency are compared with the state-of-the-art.The use of low-power graphics processing units (GPUs) brings a new paradigm to the field of multispectral and hyperspectral compression. Even though, not as the efficiency as FPGAs, GPUs deliver high throughput rates.
Arienzo, Alberto. "Multi-sensor Model-based Data Fusion for Remote Sensing Applications." Doctoral thesis, 2022. http://hdl.handle.net/2158/1272763.
Повний текст джерела"Discriminating wetland vegetation species in an African savanna using hyperspectral data." Thesis, 2010. http://hdl.handle.net/10413/2140.
Повний текст джерелаThesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.
Kandare, Kaja. "Fusion of airborne laser scanning and hyperspectral data for predicting forest characteristics at different spatial scales." Doctoral thesis, 2017. http://hdl.handle.net/10449/44160.
Повний текст джерелаCheng, Jing-Yi, and 陳靖怡. "A Band Selection approach of Simulated Annealing Feature Uniformity for the Data Fusion of Hyperspectral and SAR Imageries." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/uv3k28.
Повний текст джерела國立臺北科技大學
電機工程系研究所
96
With the recent advances of state-of-the-art sensors, data initially developed in a few multispectral bands today can be now collected from several hundred hyperspectral and even thousands of ultraspectral bands. While images are continuously being acquired and archived, existing methodologies have proved inadequate for analyzing such large volumes of data. As a result, a vital demand exists for new concepts and methods to deal with high-dimensional datasets. In this paper,we fuse hyperspectral imaging and synthetic aperture radar imaging. We use Simulated annealing feature uniformity band selection (SAFU) from hyperspectral imaging feature extraction. Previously, scholars have put forward the “simulated annealing band selection” (SABS) . In this paper, we propose a novel feature extraction method, called simulated annealing feature uniformity (SAFU) band selection approach to improve the computational and the precise performances of the “clustered eigenspace / feature scale uniformity transformation” (CE/FSUT) of SABS method for clustering the CE features. It takes advantage of the special characteristics of SA to concentrate the CE feature sets of different classes into the most common feature subspaces. A distance measure based on SAFU is then applied to decompose the similarity for land cover classification purposes. Compared with the CE/FSUT method, the SAFU can group the CE feature sets of each different class in the same orders and can unify the feature scales of each different CE feature set at the same time. It can simultaneously group highly correlated bands of each different class into the same CE feature sets with higher effectiveness but lower computational loads. To demonstrate the advantages of the proposed method, we compared several different configurations categorized by the parameters of constructing SA annealing schedule. The performance of the propose method is evaluated by fusing MODIS/ASTER airborne simulator (MASTER) images and the Airborne Synthetic Aperture Radar (AIRSAR) images. Compared with conventional feature extraction techniques, SAFU evinced improved discriminatory properties, crucial to subsequent PBF classification. It made use of the potentially significant separability embedded in high-dimensional datasets to select a unique set of the most important feature bands. The experimental results showed that the proposed SAFU approach is effective and can be used as an alternative to the existing feature extraction method for the data fusion of hyperspectral data sets.
Liu, Jin-Nan, and 劉進男. "A Parallel Simulated Annealing Approach to Band Selection and Feature Extraction for the Data Fusion of Hyperspectral and SAR Imageries." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/kkg444.
Повний текст джерела國立臺北科技大學
電機工程系研究所
96
Satellite remote sensing images can interpret all kinds of large-scale terrain to understand the different topography of the distribution, such as sugarcane, oceans, paddy fields, reservoirs, and so on. Recent advances of satellite sensors technologies continue to update the development resulting in the increase of large spectral information available. The noises contained in these huge information can’t avoid the curse of dimensionality., As a result, how to efficiently select the right and effective spectral information has become important. This paper presents an alternative promising concept, known as the parallel simulated annealing band selection (PSABS), which adopts a novel parallel approach to the data fusion of remote sensing images of the same scene collected from multiple sources. The applications can be divided into three parts: 1.) a parallel simulated annealing (PSA), 2.) a clustered eigenspace / feature scale uniformity transformation (CE/FSUT), and a parallel positive Boolean function (PPBF). PSA and CE/FSUT are used to select the high-dimensional fused datasets, and cluster the highly related information to a set of modular subspaces. Finally, a PPBF classifer is then applied to these selected band modules to execute the classification. The effectiveness of the proposed PSABS is evaluated by MODIS/ASTER airborne simulator (MASTER) hyperspectral and SAR images for hyperspectral band selection. The experimental results demonstrated that PSABS can significantly improve the computational loads and provide a more reliable quality of solution compared to the traditional methods.
(8713962), James Ulcickas. "LIGHT AND CHEMISTRY AT THE INTERFACE OF THEORY AND EXPERIMENT." Thesis, 2020.
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