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Lignocellulosic plants are the most abundant source of terrestrial biomass and are one of the potential sources of renewable energy that can replace the use of fossil fuels. For a country such as Sweden, where the forest industry accounts for 10% of the total export, there would be large economical benefits associated with increased biomass yield. The availability of research on wood development conducted in conifer tree species, which represent the majority of the forestry in Sweden, is limited and the majority of research has been conducted in model angiosperm species such as Arabidopsis thaliana. However, the large evolutionary distance between angiosperms and gymnosperms limits the possibility to identify orthologous genes and regulatory pathways by comparing sequence similarity alone. At such large evolutionary distances, the identification of gene similarity is, in most cases, not sufficient and additional information is required for functional annotation. In this thesis, two high-spatial resolution datasets profiling wood development were processed; one from the angiosperm tree Populus tremula and the other from the conifer species Picea abies. These datasets were each published together with a web resource including tools for the exploration of gene expression, co-expression and functional enrichment of gene sets. One developed resource allows interactive, comparative co-expression analysis between species to identify conserved and diverged co-expression modules. These tools make it possible to identifying conserved regulatory modules that can focus downstream research and provide biologists with a resource to identify regulatory genes for targeted trait improvement. / Lignocellulosa är den vanligast förekommande källan till markburen biomassa och är en av de förnybara energikällor som potentiellt kan ersätta användningen av fossila bränslen. För ett land som Sverige, där skogsindustrin som står för 10 \% av den totala exporten, skulle därför en ökad produktion av biomassa kunna ge stora ekonomiska fördelar. Forskningen på barrträd, som utgör majoriteten av svensk skog är begränsad och den huvudsakliga forskningen som har bedrivits på växter, har skett i modell organismer tillhörande gruppen gömfröiga växter som till exempel i Arabidopsis thaliana. Det evolutionära avståndet mellan gömfröiga (blommor och träd) och nakenfröiga (gran och tall) begränsar dock möjligheten att identifiera regulatoriska system mellan dessa grupper. Vid sådana stora evolutionära avstånd krävs det mer än att bara identifiera en gen i en modellorganism utan ytterligare information krävs som till exempel genuttrycksdata. I denna avhandling har två högupplösta experiment som profilerar vedens utveckling undersökts; ett från gömfröiga träd Populus tremula och det andra från nakenföriga träd (barrträd) Picea abies. Datat som behandlats har publicerats tillsammans med webbsidor med flera olika verktyg för att bland annat visa genuttryck, se korrelationer av genuttryck och test för anrikning av funktionella gener i en grupp. En resurs som utvecklats tillåter interaktiva jämförelser av korrelationer mellan arter för att kunna identifiera moduler (grupper av gener) som bevaras eller skilts åt mellan arter över tid. Identifieringen av sådana bevarade moduler kan hjälpa att fokusera framtida forskning samt ge biologer en möjlighet att identifiera regulatoriska gener för en riktad förbättring av egenskaper hos träd.

Analysis of forest canopy density based on textural features of high resolution imagery and airborne laser scanning data Abstract The objective of this thesis is to assess the forest canopy density in the Šumava Mountains, Czech Republic. The spruce forests in this area have been suffering from the bark beetle outbreak for almost 20 years resulting in a mixture of dead and young trees, mature forest stands and peat bogs. The canopy density was evaluated using a very high spatial resolution panchromatic imagery and low point density LiDAR, combined with an object oriented approach. The classification based on three GLCM texture measures (contrast, entropy and correlation), which were derived from the image objects, resulted in a kappa index of accuracy of 0.45. Adding the information from the LiDAR data, the accuracy of the classification improved up to 0.95.

Les activités anthropiques dans le bassin méditerranéen sont en forte évolution. Dans les zones agricoles, cette croissance entraîne des évolutions considérables de l'occupation du sol. Cette activité agricole exerce un impact majeur sur le fonctionnement hydrologique des paysages qui n'est identifiable qu'à une échelle bien plus large, sur plusieurs dizaines de km². Cette thèse se concentre sur la classification de l'occupation du sol sur une large étendue à partir d'une image monodate à haute résolution spatiale (SPOT6/7).Dans ce contexte, les données d'apprentissage sont collectées par des enquêtes terrain, par conséquent, elles sont très limitées. Les méthodes d'apprentissage supervisées sont généralement utilisées, en supposant que la distribution des classes est stable sur toute l'image. Cependant, en pratique, on constate une distorsion des distributions des classes (apparition de nouvelles classes, disparition de classes). Ce problème, intitulé "datashift", se produit souvent sur des larges étendues. Ainsi le modèle construit sur les données d'apprentissage initiales s'avère sous optimal pour la classification de l'image entière. Pour atténuer ce problème, les techniques d'apprentissage actif définissent un ensemble d'apprentissage efficace, en l'adaptant itérativement par l'ajout des données non labellisées les plus informatives. Ces techniques permettent d'améliorer le modèle de classification tout en conservant un petit ensemble d'apprentissage initial. L'échantillonnage se base généralement sur deux métriques : l'incertitude et la diversité.Dans cette thèse, nous montrons l'apport des techniques d'apprentissage actif pour la cartographie de l'occupation du sol en milieu agricole, en proposant un échantillonnage adapté par parcelle.L'apport des méthodes d'apprentissage actif est validé par rapport à une sélection aléatoire des parcelles. Une métrique de diversité basée sur l'algorithme Meanshift a été proposée.Dans un deuxième temps, nous avons traité le sous-problème du "datashift" qui est l'apparition de nouvelles classes. Nous avons proposé de nouvelles métriques de diversité basées sur l'algorithme Meanshift et les Fuzzy k-means ainsi qu'une nouvelle stratégie de sélection des données adaptées à la détection de nouvelles classes.Dans la dernière partie, nous nous sommes intéressés aux contraintes spatiales induites par les observations sur terrain et nous avons proposé une stratégie de labellisation par points de vue qui permet de diminuer largement les coûts humains d'observations terrain tout en gardant de bonnes précisions de classification ainsi que la découverte des nouvelles classes.Les méthodes proposées ont été testées et validées avec une image multispectrale SPOT6 à 6m de résolution sur le bassin versant de Lebna, Cap-Bon, Tunisie. / Anthropogenic activities in the Mediterranean are in strong evolution. In agricultural areas, this growth leads to considerable changes in land cover. This agricultural activity has a major impact on the hydrological functioning of the landscapes which can be only identified on a wide scale, over several tens of km². This thesis focuses on the land cover classification on wide area from a high spatial resolution monodate image (SPOT6/7).In this context, the learning data are collected by field surveys, therefore they are very limited. Supervised learning methods are generally used, assuming that the class distribution is stable over all the image. However, in practice, there is a class distributions distortion (new classes appear, classes disappear). This problem, called "datashift", always occurs over wide areas. Thus, the model constructed on the initial learning data is sub-optimal for the classification of the entire image. To lessen this problem, active learning techniques define an effective learning set, by iteratively adapting it by adding the most informative unlabeled data. These techniques improve the classification model while retaining a small initial learning set. Sampling is generally based on two metrics: uncertainty and diversity.In this thesis, we show the contribution of active learning techniques for the land cover mapping in agricultural environment, proposing a suitable sampling per parcel.The active learning methods contribution is validated respectively to a random selection of parcels. A diversity metric based on the Meanshift algorithm has been proposed.Secondly, we treated the sub-problem of the "datashift" which is the appearance of new classes. We proposed new metrics of diversity based on the Meanshift algorithm and Fuzzy k-means as well as a new data selection strategy adapted to the detection of new classes.Finally we were interested in the spatial constraints induced by the field observations and we proposed a strategy of labeling by stand points which make it possible to greatly reduce the human costs for field observations while maintaining good classification precisions as well as the discovery of new classes.The proposed methodologies were tested and validated on a multispectral SPOT6 image with 6m resolution on the Lebna watershed, Cap-Bon, Tunisia.

Dentre as técnicas diagnósticas por imagem, em Medicina Nuclear, têm-se destacado a Tomografia por Emissão de Pósitrons (PET). O PET fornece imagens funcionais da região ou órgão de interesse, possibilitanto o diagnóstico de várias doenças e, também, um mapeamento da evolução ou regressão das mesmas. Os atuais sistemas PET apresentam blocos de cristais arranjados em geometria circular, mas a busca por novos arranjos, em geometrias diferenciadas, é pertinente, pois pode possibilitar um melhor desempenho destes tomógrafos. O objetivo deste estudo é avaliar o desempenho de um sistema PET constituído por blocos de cristais em arranjo elipsoidal. Para tal estudo, fez-se uso do GATE, ambiente de simulação para tomografia por emissão que apresentou resultados satisfatórios comparados à aparelhos utilizados na rotina clínica. Desta forma, elaborou-se dois sistemas PET, o primeiro com 46 blocos de cristais arranjados em geometria circular, no qual, os detectores opostos foram separados a uma distância de 816,4 mm na direção transaxial. No segundo caso, utilizou-se 36 blocos de cristais, arranjados em geometria elíptica, onde os detectores foram separados a uma distância de 500 mm na direção vertical e 816,4 mm na direção horizontal. Uma vez realizadas as simulações, fez-se os testes de validação para Sensibilidade, Fração de Espalhamento, NEC (Noise Equivalente Count Rate) e Resolução Espacial, para então, avaliar e comparar o desempenho do sistema PET para ambas as geometrias propostas. Estes testes foram feitos de acordo com as especificações da norma NEMA NU 2-2007. Para o teste de sensibilidade, com o phantom localizado no centro do FOV transaxial do tomógrafo, obteve-se um ganho no sistema PET, com geometria elíptica, de 28,7% em relação ao sistema convencional, de geometria circular. Para o phantom deslocado à 10 cm do centro do FOV transaxial, a sensibilidade do sistema PET elíptico foi 26,2% superior em relação ao circular. O tempo de aquisição para cada simulação neste teste foi de 400 s. Para os testes de Fração de Espalhamento e NEC foram realizadas 33 simulações, para cada geometria, variando o tempo de aquisição e a atividade. A fração de espalhamento na geometria elíptica foi de 35,5% e na circular 34,6%. Na geometria circular, a curva NEC atinge o pico máximo em 259,3 kcps a uma concentração de atividade de 34,1 kBq ml-1 , já para geometria elíptica, o pico máximo é atingido em 239,1 kcps a uma concentração de atividade de 24,8 kBq ml-1 . As medidas foram adquiridas em três posições transaxiais (x,y) no centro do FOV e, posteriormente, deslocadas em ¼ do centro do FOV, totalizando 6 medidas para cada geometria. Para cada uma destas 6 medidas, encontrou-se a FWHM (largura a meia altura) e a FWTM (largura a 10% do máximo) nos 3 eixos de coordenadas (x,y,z), assim, para cada geometria proposta obteve-se 18 valores de FWHM e 18 valores de FWTM. A geometria elíptica apresentou melhoras na resolução espacial em FWHM em 4 dos 18 valores de resolução. Já para FWTM, a geometria elíptica apresentou melhora em 8 dos 18 valores de resolução. Em relação a FWHM, onde houve melhora na resolução espacial, teve-se um ganho médio de 18,7%. Já as perdas, em resolução espacial foi em média 43,26%. Para FWTM, a melhora em resolução espacial foi em média 12,09%, mas a perda de resolução, neste caso, foi em média 45,59%. Pode-se concluir que a geometria proposta apresenta algumas vantagens em relação a geometria convencional. Na geometria elíptica há uma redução de 10 blocos de cristais em relação a geometria circular, o que possivelmente gera uma redução no custo de fabricação do tomógrafo. Em relação a sensibilidade, há uma melhora significativa para nova geometria, bem como uma redução da concentração da atividade na qual o tomógrafo alcança seu melhor desempenho, embora ocorra uma ligeira diminuição na taxa de contagens no sistema elíptico. Essa redução na concentração de atividade pode ocasionar uma redução no tempo de exame e utilização de menos material radioativo, reduzindo o risco ao paciente e o custo do exame, bem como possibilitando a realização de um maior número de exames num mesmo intervalo de tempo. A fração de espalhamento do sistema elíptico é ligeiramente maior em relação ao sistema circular. A principal perda do sistema PET elíptico está na resolução espacial, onde não houve melhoras para maior parte das medidas realizadas, necessitando de alguns ajustes que pode se dar, entre outras coisas, no arranjo dos blocos de cristais. Acredita-se que ajustes na angulação dos detectores e na formação das coincidências seja possível melhorias nas resoluções, e NEC. / Among the diagnostic imaging techniques, in Nuclear Medicine, have been prominent in Positron Emission Tomography (PET). PET provides functional images of the region or organ of interest, allowing diagnostic of various diseases and also a mapping of the development or regression of the same. Current PET systems feature blocks arranged in circular geometry crystals, but the search for new arrangements, in different geometries, is relevant because it can provide a better performance of these scanners. The objective of this study is to evaluate the performance of a PET system consisting of blocks of crystals ellipsoidal arrangement. For this study, we used the GATE simulation environment for emission tomography that presented satisfactory results compared to the devices used in the clinical routine. This way we prepared two PET systems, the first one containing 46 blocks of crystals arranged in a circular geometry, where the opposite detectors were separated by a distance of 816.4 mm in the transaxial direction. In the second case, we used 36 blocks of crystals arranged in an elliptical geometry, where the detectors are separated a distance of 500 mm in the vertical direction and 816.4 mm in the horizontal direction. After the simulations were performed, the validation tests for sensitivity, scatter fraction, NEC (Noise Equivalent Count Rate) and Spatial Resolution were made, to finally evaluate and compare the performance of the PET system for both proposed geometries. The tests were made accordingly to specified NEMA NU 2-2007 standard. For the sensitivity test, with the phantom located in the center of FOV transaxial of the tomography, we obtained a gain in PET system with elliptical geometry of 28.7% compared to the conventional system, the circular geometry. For the phantom shifted to 10 cm from the center of the transaxial FOV, the sensitivity of PET elliptical system was 26.2% higher than in the circular. The acquisition time for each simulation in this test was 400 s. For Scatter Fraction test and NEC, were made 33 simulations for each geometry, varying the time of acquisition and activity. The scatter fraction in the elliptical geometry was 35,5% and 34,6% in the circular geometry. In the circular geometry, NEC curve reaches its peak in 259.3 kcps at an activity concentration of 34.1 kBq ml-1, for the elliptical geometry, the maximum peak is reached at 239.1 kcps at an activity concentration of 24.8 kBq ml-1. For the spatial resolution test we used a point source containing 18F with 4 MBq activity and acquisition time of 200 s. The measurements were acquired in three transaxials positions (x,y) in the center of FOV and, shifted in ¼ of the center of the FOV, summarizing 6 measurements for each geometry. For each one of these 6 measurements, we found the FWHM (Full width at half-maximum amplitude) and the FWTM (Full width at tenth-maximum amplitude) in the three axis (x,y,z), in this way, for each proposed geometry, we obtained 18 values of FWHM and 18 values of FWTM. The elliptical geometry showed improvement in the spatial resolution in FWHM in 4 of the 18 resolution values. For the FWTM, the elliptical geometry showed improvement in 8 of 18 resolution values. Regarding the FWHM, where there was an improvement in spatial resolution, we obtained the average gain of 18,7%. For the losses, in spatial resolution was an average of 43,26%. For the FWTM, the improvement in spatial resolution was an average percentage of 12,09%, while the loss of resolution had the average percentage of 45,59%. It can be concluded that the proposed geometry showed some advantages regarding the traditional geometry. In the elliptical geometry there is one reduction of 10 blocks of crystals in relation to the circular geometry, which possibly can cut fabrication costs of the tomography. Regarding to sensibility, there is a significant improvement for the new geometry, as well as one reduction of the activity concentration in which the tomography reaches the best performance, although the occurrence of a slight fall in the counts in the elliptical system. This reduction in the activity concentration can help to reduce the test time and the use less radioactive material, reducing the risk for the patients and the test costs, as well as improving the number of tests in the same period of time. The scattering fraction in the elliptical system is slight higher to the circular system. The major loss in the elliptical PET system is the spatial resolution, where there was not improvement for the great fraction of the obtained measurements, requiring some modifications that can be made, in special, in the crystal blocks arrangement. It is believed that some modifications to the angle of the detectors and in the formation of the coincidences can improve the resolutions and NEC.

Nos centros urbanos, as precipitações sempre estiveram ligadas a problemas como inundações e propagação de doenças. Para solucioná-los, é comum a realização de obras hidráulicas nos sistemas de drenagem urbanos. Para tanto, deve-se conhecer as condições da bacia hidrográfica e as consequências que qualquer alteração no ambiente pode causar. Portanto, modelos hidrológicos são utilizados na previsão do comportamento das bacias frente a eventos de precipitação, aumentando a eficácia das obras e diminuindo os riscos associados a estas. Para o uso de modelos, são necessários diversos parâmetros hidrológicos referentes à bacia, tais como área de drenagem, comprimento e declividade dos talvegues, tipo de cobertura de solo etc. Com o avanço da tecnologia, a determinação destes torna-se cada vez mais precisa, bem como os modelos utilizados, trazendo o Sistema de Informações Geográficas (SIG) e o sensoriamento remoto como poderosas ferramentas de apoio a estudos hidrológicos. Neste trabalho, aplicou-se o processo de classificação automática supervisionada pelo método da Análise Orientada a Objeto a uma imagem de satélite de alta resolução da bacia hidrográfica do córrego do Gregório, para caracterizar sua cobertura de solo e determinar os parâmetros hidrológicos número de deflúvio (CN, pelo método do SCS), grau de vegetação (PP), área (A), comprimento (L) e declividades dos talvegues (S) das sub-bacias que compõem a bacia, para as quais os resultados obtidos foram bastante satisfatórios. Por fim, atualizou-se o modelo hidrológico EESC (1993), referente ao sistema de macrodrenagem de São Carlos, obtendo-se hidrogramas finais com diferenças, em relação ao modelo original, de até 33,96% para vazão de pico (Qp), 77,78% para tempo de pico (tp) e 29,86% para volume total de escoamento. / In urban centers, precipitation always been related to problems such as floods and spread of disease. To solve them, it is common to make hydraulic interventions in the urban drainage systems. For this, it is necessary to know the conditions of the watershed and the consequences that any change in the environment can cause. Therefore, hydrological models are used to predict the river behavior in opposite to precipitation events, increasing the efficiency of the hydraulic interventions and reducing the associated risks to these. For the use of models, it is necessary to have several hydrological parameters related to the basin such as drainage area, river length, slope of the thalweg, type of soil cover etc. Trough the technological advancement, the parameter determination becomes more accurate as well as the models, and the Geographic Information System (GIS) and remote sensing appear as powerful tools to support hydrological studies. In this study, we have applied the automatic supervised classification process by the Object-Oriented Analysis method to a high resolution satellite image of the córrego do Gregório watershed, to classify soil coverage and to determine the hydrological parameters curve-number (CN by the SCS method), vegetation degree (PP), area (A), length (L), and slope of thalwegs (S) of the sub-basins of the córrego do Gregório watershed, for which the results were quite satisfactory. Finally, a hydrological model for the São Carlos macrodrainage system called EESC model (1993) was updated with the new parameters, obtaining final hydrographs with differences from the original model up to 33.96% for peak discharge (Qp), 77.78% for peak time (tp) and 29.86% for total volume of runoff.

Submitted by Angela Maria de Oliveira (amolivei@uepg.br) on 2018-05-08T17:08:26Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Antonio David Viniski.pdf: 3962317 bytes, checksum: f5afcd11e4083b0ae065ae21490ac77f (MD5) / Made available in DSpace on 2018-05-08T17:08:26Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Antonio David Viniski.pdf: 3962317 bytes, checksum: f5afcd11e4083b0ae065ae21490ac77f (MD5) Previous issue date: 2018-03-16 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O sensoriamento remoto agrícola tem fornecido um volumoso conjunto de dados espaciais, os quais podem ser utilizados em diferentes segmentos, como na estimativa de produtividade de grãos. Dentre as tecnologias empregadas no SR, a utilização de aeronaves remotamente pilotadas (RPA) na agricultura vêm crescendo, sendo uma alternativa na obtenção de dados para a estimativa de produtividade. Porém, esses conjuntos de dados gerados demandam métodos e técnicas capazes de extrair informações úteis e relevantes dos mesmos. Algumas técnicas de geoestatística, como a krigagem, têm sido empregadas, mas a utilização da mineração de dados (MD), assim como da mineração de dados espaciais (MDE), podem ser alternativas viáveis para suprir essa demanda. Este trabalho teve como objetivo avaliar o uso de técnicas de MD e MDE na estimativa da produtividade de grãos de soja e trigo, utilizando dados de imagens obtidas por meio de RPA. A área de estudo localiza-se no município de Piraí do Sul, Paraná. Foi utilizada uma RPA de asa fixa para o acompanhamento das culturas de soja e trigo. No imageamento do trigo foram utilizadas duas câmeras, uma com a captura de imagens no espectro visível (RGB), e outra no infravermelho próximo (NIR), tendo sendo analisadas também as resoluções espaciais de 10 e 20 cm/pixel para cada câmera. Para a soja apenas a câmera RGB foi utilizada e as resoluções espaciais sobrevoadas foram 10, 20 e 26 cm/pixel. Os dados do atributo meta, a produtividade das culturas, foram obtidos por meio de colhedoras de precisão. Os atributos de predição, correspondendo aos valores das bandas espectrais e altitude do terreno, foram submetidos aos algoritmos de MD empregando as técnicas de regressão linear múltipla (RLM), redes neurais artificiais (RNA) e máquina de vetores de suporte para regressão (SVR). Para a MDE, foi utilizado o modelo aditivo generalizado (GAM). Para fins de comparação, os dados foram também analisados pelo método tradicional de krigagem. As técnicas foram testadas considerando duas abordagens principais: (i) utilizando apenas as bandas espectrais para estimativa e, (ii) utilizando as bandas espectrais e os valores de altitude do terreno. Para a MD clássica, os melhores resultados foram obtidos com a técnica SVR, utilizando o kernel Laplacian. Na MDE, o método GAM com a função de ajuste gaussiana apresentou os melhores resultados. Tanto para as técnicas clássicas de MD como para a MDE, a incorporação da altitude nos modelos de regressão possibilitou aumento considerável nos coeficientes de correlação e determinação, com consequente diminuição no erro (RMSE). Os valores de correlação obtidos com a MDE foram semelhantes aos obtidos com o método de krigagem, porém a MDE foi mais eficiente em avaliar o impacto dos atributos de predição (valores das bandas espectrais e altitude) na estimativa do atributo meta. Com isso, conclui-se que a MDE mostra-se viável de ser utilizada como ferramenta na geração de modelos para estimativa de produtividade de grãos com base em dados de imagens de RPA. / Agricultural remote sensing (RS) has provided a massive set of spatial data which can be used in different segments, such as in grain yield estimation. Among the technologies applied in RS, the use of remotely piloted aircraft (RPA) in agriculture is growing as an alternative to obtain data for estimating productivity. However, these generated data sets require methods and techniques capable of extracting useful and relevant information from them. Some geostatistics techniques have been applied, such as kriging, but the use of data mining (DM) as well as spatial data mining (SDM) can be viable alternatives to meet that demand. The goal of this work was to evaluate the use of DM and SDM techniques for estimating soybean and wheat grain yield using image data obtained by RPA. The study area is located in Piraí do Sul, Paraná State. A fixed wing RPA was used to monitor soybean and wheat crops. In wheat crop imaging two cameras were used, one to capture images in the visible spectrum (RGB), and the other one using the near infrared (NIR) spectrum. Also, it was analyzed the spatial resolutions of 10 and 20 cm / pixel for each camera. For soybean only the RGB camera was used and the overhead spatial resolutions were 10, 20 and 26 cm / pixel. The goal attribute data (crop yield), was obtained by precision harvester. The prediction attributes, corresponding to the values of spectral bands and terrain altitude, were submitted to DM algorithms using the multiple linear regression (MLR), artificial neural networks (ANN) and support vector regression (SVR) techniques. For SDM, the generalized additive model (GAM) was used. For comparison purposes, data were also analyzed by the traditional kriging method. The techniques were tested using two main approaches: (i) using only spectral bands for estimation and, (ii) using spectral bands and terrain altitude values. For classical DM, the best results were obtained with SVR technique, using the Laplacian kernel. The GAM method with the Gaussian fit function presented the best results for SDM. For both classical DM and SDM techniques, adding altitude in the regression models allowed a considerable increase in correlation and determination coefficients, with consequent decrease in error (RMSE). The correlation values obtained with SDM were similar to those obtained with kriging method, but SDM was more efficient in evaluating the impact of the prediction attributes (spectral bands and altitude) in the estimation of the goal attribute. Thus, it is concluded that SDM can be useful as a tool for estimating grain yield based on RPA image data.

Depuis une quinzaine d’années, l’émergence des données de télédétection à Très Haute Résolution Spatiale (THRS) et la démocratisation des Systèmes d’Information Géographique (SIG) aident à répondre aux nouveaux besoins croissants d’informations spatialisées. Le développement de nouvelles méthodes de cartographie offre une opportunité pour comprendre et anticiper les mutations des surfaces terrestres aux grandes échelles, jusqu’ici mal connues. En France, l’emploi de bases de données spatialisées sur l’occupation du sol à grande échelle (BD Ocsol GE) est devenu incontournable dans les opérations courantes de planification et de suivi des territoires. Pourtant, l’acquisition de ce type de bases de données spatialisées est encore un besoin difficile à satisfaire car les demandes portent sur des productions cartographiques sur-mesure, adaptées aux problématiques locales des territoires. Face à cette demande croissante, les prestataires réguliers de ce type de données cherchent à optimiser les procédés de fabrication avec des techniques récentes de traitements d’image. Cependant, la Photo-Interprétation Assistée par Ordinateur (PIAO) reste la méthode privilégiée des prestataires. En raison de sa grande souplesse, elle répond toujours au besoin de cartographie aux grandes échelles, malgré son coût important. La substitution de la PIAO par des méthodes de production entièrement automatisées est rarement envisagée. Toutefois, les développements récents en matière de segmentation d’images peuvent contribuer à l’optimisation de la pratique de la photo-interprétation. Cette thèse présente ainsi une série d’outils (ou modules) qui participent à l’élaboration d’une assistance à la digitalisation pour l’exercice de photo-interprétation d’une BD Ocsol GE. L’assistance se traduit par la réalisation d’un prédécoupage du paysage à partir d’une segmentation menée sur une image THRS. L’originalité des outils présentés est leur intégration dans un contexte de production fortement contraint. La construction des modules est conduite à travers trois prestations cartographiques à grande échelle commandités par des entités publiques. L’apport de ces outils d’automatisation est analysé à travers une analyse comparative entre deux procédures de cartographie : l’une basée sur une démarche de photo-interprétation entièrement manuelle et la seconde basée sur une photo-interprétation assistée en amont par une segmentation numérique. Les gains de productivité apportés par la segmentation sont, évalués à l’aide d’indices quantitatifs et qualitatifs, sur des configurations paysagères différentes. À des degrés divers, il apparaît que quelque soit le type de paysage cartographié, les gains liés à la cartographie assistée sont substantiels. Ces gains sont discutés, à la fois, d’un point de vue technique et d’un point de vue thématique dans une perspective commerciale. / Over the last fifteen years, the emergence of remote sensing data at Very High Spatial Resolution (VHRS) and the democratization of Geographic Information Systems (GIS) have helped to meet the new and growing needs for spatial information. The development of new mapping methods offers an opportunity to understand and anticipate land cover change at large scales, still poorly known. In France, spatial databases about land cover and land use at large scale have become an essential part of current planning and monitoring of territories. However, the acquisition of this type of database is still a difficult need to satisfy because the demands concern tailor-made cartographic productions, adapted to the local problems of the territories. Faced with this growing demand, regular service providers of this type of data seek to optimize manufacturing processes with recent image-processing techniques. However, photo interpretation remains the favoured method of providers. Due to its great flexibility, it still meets the need for mapping at large scale, despite its high cost. Using fully automated production methods to substitute for photo interpretation is rarely considered. Nevertheless, recent developments in image segmentation can contribute to the optimization of photo-interpretation practice. This thesis presents a series of tools that participate in the development of digitalization assistance for the photo-interpretation exercise. The assistance results in the realization of a pre-cutting of the landscape from a segmentation carried out on a VHRS image. Tools development is carried out through three large-scale cartographic services, each with different production instructions, and commissioned by public entities. The contribution of these automation tools is analysed through a comparative analysis between two mapping procedures: manual photo interpretation versus digitally assisted segmentation. The productivity gains brought by segmentation are evaluated using quantitative and qualitative indices on different landscape configurations. To varying degrees, it appears that whatever type of landscape is mapped, the gains associated with assisted mapping are substantial. These gains are discussed both technically and thematically from a commercial perspective.

<p>The major advantage of position sensitive radiation detector systems lies in their ability to non invasively map the regional distribution of the emitted radiation in real-time. Three of such detector systems were studied in this thesis, gamma-cameras, positron cameras and CMOS image sensors. A number of physical factors associated to these detectors degrade the qualitative and quantitative properties of the obtained images. These blurring factors could be divided into two groups. The first group consists of the general degrading factors inherent to the physical interaction processes of radiation with matter, such as scatter and attenuation processes which are common to all three detectors The second group consists of specific factors inherent to the particular radiation detection properties of the used detector which have to be separately studied for each detector system. Therefore, the aim of this thesis was devoted to the development of computational methods to enable quantitative molecular imaging in PET, SPET and in vivo patient dosimetry with CMOS image sensors.</p><p>The first task was to develop a novel quantitative dual isotope method for simultaneous assessments of regional lung ventilation and perfusion using a SPET technique. This method included correction routines for photon scattering, non uniform attenuation at two different photon energies (140 and 392 keV) and organ outline. This quantitative method was validated both with phantom experiments and physiological studies on healthy subjects.</p><p>The second task was to develop and clinically apply a quantitative method for tumour to background activity uptake measurements using planar mammo-scintigraphy, with partial volume compensation.</p><p>The third stage was to produce several computational models to assess the spatial resolution limitations in PET from the positron range, the annihilation photon non-collineairy and the photon depth of interaction.</p><p>Finally, a quantitative image processing method for a CMOS image sensor for applications in ion beam therapy dosimetry was developed.</p><p>From the obtained phantom and physiological results it was concluded that the methodologies developed for the simultaneous measurement of the lung ventilation and perfusion and for the quantification of the tumour malignancy grade in breast carcinoma were both accurate. Further, the obtained models for the influence that the positron range in various human tissues, and the photon emission non-collinearity and depth of interaction have on PET image spatial resolution, could be used both to optimise future PET camera designs and spatial resolution recovery algorithms. Finally, it was shown that the proton fluence rate in a proton therapy beam could be monitored and visualised by using a simple and inexpensive CMOS image sensor.</p>