Submitted by Rosangela Silva (rosangela.silva3@unioeste.br) on 2018-06-04T17:12:56Z No. of bitstreams: 2 Lucas Oldoni.pdf: 9472745 bytes, checksum: 1b2c1a8ee59169fa471b43d27a762f6e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-06-04T17:12:56Z (GMT). No. of bitstreams: 2 Lucas Oldoni.pdf: 9472745 bytes, checksum: 1b2c1a8ee59169fa471b43d27a762f6e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-02-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Studies related to the monitoring of agricultural production play a decisive and strategic role in the economic planning of the country, due to the importance of agribusiness, as well as food safety. Orbital remote sensing is an effective alternative to perform agricultural crop monitoring due to its low cost, large scale and speed of data collection. However, most of the sensors with high spatial resolution are of low temporal resolution, and the ones with higher temporal resolution have low spatial resolution. Therefore, for the monitoring of agricultural crops with a higher spatial solution, cloud covering can be a limiting factor. Such problems can be circumvented by using a fusion of images of several sensors with different spatial and temporal characteristics, thus creating new images, also called synthetic images. Thus, the objective of the work was the mapping of areas sown with soybean and corn using space-temporal fusion, such as Landsat 8 and MODIS images. In the first part of the research, agricultural crops were separated from other targets. The generated classification served as input to one of the classification algorithms, the Flexta Spatiotemporal Data Fusion (FSDAF), in the second part of the research. In addition to this algorithm, both the Spatial and Temporal Adaptive Reflection Fusion Model (STARFM) and the Advanced and Temporal Spatial Adaptive Reflection Fusion Model (ESTARFM) were employed to generate images for the 2016/2017 summer crops. Then, 5 rating scenarios were created. In the 1st and 2nd scenarios, only the images from the Landsat 8 with no occurrence of clouds were considered. For the 3rd, 4th, and 5th were carried out using images generated by STARFM, ESTARFM and FSDAF. In the third scenario, the metric images of images, Landsat 8 and images of fusion algorithms were used, 4th as a summary of statistical metrics, and in the 5th one as phenological metrics of the temporal profile of the Enhanced Vegetation Index (EVI). The scenario using the EVI phenological metrics from images generated by FSDAF and STARFM yielded better results, with global accuracy of 93.11 and 91.33%, respectively. These results are statistically better than those obtained using only existing Landsat 8 images. Thus, the use of phenological metrics obtained from synthetic images are important alternatives for mapping soybean and corn crops. / Estudos referentes ao acompanhamento da produção agrícola têm um peso determinante e estratégico no planejamento econômico do país, devido à importância do agronegócio, e também para segurança alimentar. O sensoriamento remoto orbital é uma alternativa eficaz para realizar o monitoramento das culturas agrícolas, devido ao baixo custo, grande escala de abrangência e rapidez na coleta de dados. Porém, geralmente os sensores com alta resolução espacial possuem baixa resolução temporal, e os com alta resolução temporal possuem baixa resolução espacial. Assim, para se realizar o acompanhamento de culturas agrícolas com uma resolução espacial mais alta, a cobertura por nuvens pode ser um fator limitante. Estes problemas podem ser contornados com a utilização de fusão de imagens de diversos sensores com características temporais e espaciais diferentes, criando, assim, novas imagens, também chamadas de imagens sintéticas. Deste modo, o objetivo do trabalho foi realizar o mapeamento de áreas semeadas com soja e milho utilizando fusão espaço-temporal de imagens Landsat 8 e MODIS. Na primeira parte do trabalho, foram separadas culturas agrícolas de outros alvos. A classificação gerada serviu de entrada em um dos algoritmos de classificação, o Flexible Spatiotemporal Data Fusion (FSDAF), na segunda parte do trabalho. Nessa parte, além deste algoritmo, também foram utilizados os algoritmos Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) e Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) para gerar imagens nas safras de verão 2016/2017. Então, foram criados 5 cenários de classificação. Nos 1º e 2º foram considerados a utilização apenas das imagens espectrais das imagens Landsat 8 livres de nuvens. As 3º, 4º e 5º foram realizadas com as imagens geradas pelo STARFM, ESTARFM e FSDAF. No 3º cenário foram utilizadas as métricas espectrais das imagens Landsat 8 e as imagens espectrais gerados pelos algoritmos de fusão, no 4º foram considerados as métricas estatísticas e no 5º as métricas fenológicas extraídas do perfil temporal do Enhanced Vegetation Index (EVI). Os cenários que utilizaram métricas fenológicas do EVI a partir de imagens geradas pelo FSDAF e STARFM obtiveram melhores resultados, com exatidão global de 93,11 e 91,33%, respectivamente, resultados estes estatisticamente melhores que os obtidos apenas com as imagens Landsat 8 existentes. Assim, a utilização de métricas fenológicas obtidas de imagens sintéticas são importantes alternativas para o mapeamento de soja e milho.

Autonomous systems require a lot of information about the environment in which they operate in order to perform different high-level tasks. The information is made available through various sources, such as remote and on-board sensors, databases, GIS, the Internet, etc. The sensory input especially is incrementally available to the systems and can be represented as streams. High-level tasks often require some sort of reasoning over the input data, however raw streaming input is often not suitable for the higher level representations needed for reasoning. DyKnow is a stream processing framework that provides functionalities to represent knowledge needed for reasoning from streaming inputs. DyKnow has been used within a platform for task planning and execution monitoring for UAVs. The execution monitoring is performed using formula progression with monitor rules specified as temporal logic formulas. In this thesis we present an analysis for providing spatio-temporal functionalities to the formula progressor and we extend the formula progression with spatial reasoning in RCC-8. The result implementation is capable of evaluating spatio-temporal logic formulas using progression over streaming data. In addition, a ROS implementation of the formula progressor is presented as a part of a spatio-temporal stream reasoning architecture in ROS. / Collaborative Unmanned Aircraft Systems (CUAS)

Cette thèse résume une démarche intégrée visant à évaluer l’exposition humaine et ses variations spatio-temporelles en cas de tsunami dans une zone urbaine littorale en Indonésie, ainsi que la capacité d’évacuation vers des refuges. Ce travail de recherche systématise des méthodes permettant d’estimer la quantité de population présente heure par heure durant n’importe quel jour de la semaine et de l’année, à une échelle très fine, dans une zone urbaine. Il se fonde pour cela sur une hypothèse de rythme de vie contrôlant les activités et donc la distribution de la population. L’heure d’arrivée d’un tsunami étant imprévisible, ces informations sont très importantes pour améliorer les programmes de réduction du risque. Cette démarche permet ainsi de dégager des scénarios types de distribution de la population, utilisés pour ensuite évaluer la capacité d’évacuation de ces populations. Le modèle de simulation dynamique issu de cette recherche permet de mesurer l’accessibilité des zones selon certains scénarios, et de proposer des améliorations pour une meilleure préparation de la protection des civils. / This dissertation summarizes an integrated approach whose aim is to assess the human exposure and its spatial and temporal variations in the event of a tsunami in a costal urban zone of Indonesia, as well as the capacity to join evacuation shelters for populations under threat. This research systematizes methods to estimate the amount of people present hour by hour during any day of the week and the year, at a very fine scale, in an urban area. It uses a hypothesis about a common rhythm of life which controls the activities and therefore the distribution of the Padang inhabitants. Considering that time of a tsunami occurrence is impossible to estimate, this information is very important to improve risk reduction programs. This approach allows in particular identifying various types of scenarios for the distribution of the population that can then be used to evaluate the evacuation capacity of these populations. A dynamic simulation model resulting from this research allows for the measurement of the accessibility of shelters following these scenarios. The analysis of the results suggests improvements for a better preparation on the part of authorities to protect civilians.

De nombreuses applications de la vision par ordinateur requièrent la détection, la localisation et le suivi de régions ou d’objets d’intérêt dans une image ou une séquence d’images. De nombreux modèles d’attention visuelle, inspirés de la vision humaine, qui détectent de manière automatique les régions d’intérêt dans une image ou une vidéo, ont récemment été développés et utilisés avec succès dans différentes applications. Néanmoins, la plupart des approches existantes sont limitées à l’analyse de scènes statiques et très peu de méthodes exploitent la nature temporelle des séquences d’images.L'objectif principal de ce travail de thèse est donc l'étude de modèles d'attention visuelle pour l'analyse de scènes dynamiques complexes. Une carte de saliance est habituellement obtenue par la fusion d'une carte statitque (saliance spatiale dans une image) d'une part, et d'une carte dynamique (salience temporelle entre une série d'image) d'autre part. Dans notre travail, nous modélisons les changements dynamiques par un opérateur de texture LBP-TOP (Local Binary Patterns) et nous utilisons l'information couleur pour l'aspect spatial.Les deux cartes de saliances sont calculées en utilisant une formulation discriminante inspirée du système visuel humain, et fuionnées de manière appropriée en une carte de saliance spatio-temporelle.De nombreuses expériences avec des bases de données publiques, montrent que notre approche obteint des résulats meilleurs ou comparables avec les approches de la littérature. / Visual saliency is an important research topic in the field of computer vision due to its numerouspossible applications. It helps to focus on regions of interest instead of processingthe whole image or video data. Detecting visual saliency in still images has been widelyaddressed in literature with several formulations. However, visual saliency detection invideos has attracted little attention, and is a more challenging task due to additional temporalinformation. Indeed, a video contains strong spatio-temporal correlation betweenthe regions of consecutive frames, and, furthermore, motion of foreground objects dramaticallychanges the importance of the objects in a scene. The main objective of thethesis is to develop a spatio-temporal saliency method that works well for complex dynamicscenes.A spatio-temporal saliency map is usually obtained by the fusion of a static saliency mapand a dynamic saliency map. In our work, we model the dynamic textures in a dynamicscene with Local Binary Patterns (LBP-TOP) to compute the dynamic saliency map, andwe use color features to compute the static saliency map. Both saliency maps are computedusing a bio-inspired mechanism of Human Visual System (HVS) with a discriminantformulation known as center surround saliency, and are fused in a proper way.The proposed models have been extensively evaluated with diverse publicly availabledatasets which contain several videos of dynamic scenes. The evaluation is performed intwo parts. First, the method in locating interesting foreground objects in complex scene.Secondly, we evaluate our model on the task of predicting human observers fixations.The proposed method is also compared against state-of-the art methods, and the resultsshow that the proposed approach achieves competitive results.In this thesis we also evaluate the performance of different fusion techniques, because fusionplays a critical role in the accuracy of the spatio-temporal saliency map. We evaluatethe performances of different fusion techniques on a large and diverse complex datasetand the results show that a fusion method must be selected depending on the characteristics,in terms of color and motion contrasts, of a sequence. Overall, fusion techniqueswhich take the best of each saliency map (static and dynamic) in the final spatio-temporalmap achieve best results.

This thesis focuses on two topics, computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data. The first topic is motivated by the prohibitive computational burden in fitting Gaussian process models to large and irregularly spaced spatial datasets. Various approximation methods have been introduced to reduce the computational cost, but many rely on unrealistic assumptions about the process and retaining statistical efficiency remains an issue. We propose a new scheme to approximate the maximum likelihood estimator and the kriging predictor when the exact computation is infeasible. The proposed method provides different types of hierarchical low-rank approximations that are both computationally and statistically efficient. We explore the improvement of the approximation theoretically and investigate the performance by simulations. For real applications, we analyze a soil moisture dataset with 2 million measurements with the hierarchical low-rank approximation and apply the proposed fast kriging to fill gaps for satellite images. The second topic is motivated by rank-based outlier detection methods for functional data. Compared to magnitude outliers, it is more challenging to detect shape outliers as they are often masked among samples. We develop a new notion of functional data depth by taking the integration of a univariate depth function. Having a form of the integrated depth, it shares many desirable features. Furthermore, the novel formation leads to a useful decomposition for detecting both shape and magnitude outliers. Our simulation studies show the proposed outlier detection procedure outperforms competitors in various outlier models. We also illustrate our methodology using real datasets of curves, images, and video frames. Finally, we introduce the functional data ranking technique to spatio-temporal statistics for visualizing and assessing covariance properties, such as separability and full symmetry. We formulate test functions as functions of temporal lags for each pair of spatial locations and develop a rank-based testing procedure induced by functional data depth for assessing these properties. The method is illustrated using simulated data from widely used spatio-temporal covariance models, as well as real datasets from weather stations and climate model outputs.

Les Hydrocarbures Aromatiques polycycliques (HAP) sont des polluants organiques persistants, dont la faible disponibilité dans les sols historiquement contaminés limite leur biodégradation. La capacité des plantes à favoriser l’élimination de ces polluants par l’action des microorganismes associés à leur rhizosphère a été montrée (rhizodégradation). Toutefois les résultats sont variables, suggérant la variabilité spatio-temporelle des processus. Des études à différentes échelles de temps et d’espace, utilisant des microcosmes et des dispositifs in situ ont été menées pour préciser ces phénomènes. L’étude de la variabilité spatiale des HAP et de la diversité bactérienne a été menée au sein de deux rhizosphères contrastées (ray-grass et luzerne), à l’échelle centimétrique après 37 jours de culture sur un sol de friche industrielle. Les résultats ont montré une spatialisation de la teneur en sucre et du pH, de la diversité bactérienne et de l’abondance microbienne, spécifique de l’espèce végétale, mais sans structuration de la teneur en HAP. L’étude de la variabilité temporelle de ces processus a révélé la dissipation en 6 jours des polluants biodisponibles ainsi qu’un effet positif des plantes par rapport au sol nu sur la dissipation des HAP et sur l’expression des gènes de HAP-dioxygénase. Une étude à plus long terme (6 ans) et in situ a montré que le couvert végétal ralentit la dissipation des HAP et influencent fortement la diversité microbienne, tout comme certains paramètres édaphiques. L’ensemble de ces résultats montre l’importance de la biodisponibilité des HAP qui conditionne leur dissipation, et de la dynamique des communautés microbiennes dans la rhizosphère / Polycyclic Aromatic Hydrocarbons (PAH) are persistent organic pollutants in soil, whose degradation in aged-contaminated soil is limited by their low bioavailability. The ability of plants to promote pollutant dissipation through the action of rhizosphere microorganisms has been shown (rhizodegradation). However contrasted results were obtained suggesting spatio-temporal variability of processes. Different experiments, with different time and space scales, using microcosm and field trials were achieved to precise phenomena. Study of spatial variability of PAH and bacterial diversity were done in two-contrasted rhizospheres (ryegrass and alfalfa) at centimeter scale after 37 days of plant growth, on a wasteland aged contaminated soil Results showed spatial structuration of sugar content, pH, bacterial diversity and microbial density, depending on plant species, but no vertical gradient was observed for PAH concentration. Study of temporal variability of processes showed dissipation of bioavailable fraction of pollutant in just 6 days and in comparison with bare soil, a positive impact of plant was shown on PAH dissipation and on expression of PAH dioxygenase genes. A long-term study (over 6 years) in a field trial revealed that plant cover slowed down PAH dissipation and impacted bacterial and fungal diversity as edaphic parameters. All these results underlined the importance of PAH bioavailability for the dissipation process and of spatio-temporal dynamic of microbial community, in the rhizosphere

This work derives a nonlinear optical spatio-temporal instability. It is a perturbative analysis that begins from Maxwell’s equations and its constituent relations to derive a vectorial nonlinear wave equation. In fact, it is a new theoretical method that has been developed that builds on previous aspects of nonlinear optics in a more general way. The perturbation in the wave equation derived is coupled with its complex conjugate which has been taken for granted so far. Once decoupled it gives rise to a second-order equation and thus a true instability regime because the wavevector can become complex. The solution obtained for the perturbation that co-propagates with the driving laser is a generalization to modulation and filamentation instability, extending beyond the nonlinear Schrodinger and nonlinear transverse diffusion equations[1][2]. As a result of this new mechanism, new phenomena can be explored. For example, the Kerr Nonlinear Instability can lead to exponential growth, and hence amplification. This can occur even at wavelengths that are typically hard to operate at, such as into far infrared wave- lengths. This provides a mechanism for obtaining amplification in the far infrared from a small seed pulse without the need for population inversion. The analysis provides the basic framework that can be extended to many different avenues. This will be the subject of future work, as outlined in the conclusion of this thesis.

Le problème de la stéréovision à partir de caméras multiples calibrées capturant une scène fixe est étudié depuis plusieurs décennies. Les résultats présentés dans le benchmark de stéréovision proposé par Strecha et al., attestent de la qualité des reconstructions obtenues. En particulier, les travaux du laboratoire IMAGINE, mènent à des résultats visuellement impressionnant. Aussi, il devient intéressant de calibrer des scènes de plus en plus vastes, afin d'appliquer ces algorithmes de stéréovision de façon optimale. Trois objectifs essentiels apparaissent : – La précision de la calibration doit être améliorée. En effet comme pointé par Yasutaka Furukawa, même les benchmarks de stéréovision fournissent parfois des caméras bruitées à la précision imparfaite. Un des moyen d'améliorer les résultats de stéréovision est d'augmenter la précision de la calibration. – Il est important de pouvoir prendre en compte les cycles dans le graphe des caméras de façon globale. En effet la plupart des méthodes actuelles sont séquentielles, et dérivent. Ainsi ces méthodes ne garantissent pas, pour une très grande boucle, de retrouver cette configuration cyclique, mais peuvent plutôt retrouver une configuration des caméras en spirale. Comme on calibre des réseaux d'images, de plus en plus grand, ce point est donc crucial. – Pour calibrer des réseaux d'images très grands, il convient d'avoir des algorithmes rapides. Les méthodes de calibration que nous proposons dans la première partie, permettent de calibrer des réseaux avec une précision très proche de l'état de l'art. D'autre part elle permettent de gérer les contraintes de cyclicité par le biais d'optimisations linéaires sous contraintes linéaires. Ainsi ces méthodes permettent de prendre en compte les cycles et bénéficient de la rapidité de la programmation linéaire. Enfin, la recherche en stéréovision étant arrivée à maturité, il convient de s'intéresser à l'étape suivante, à savoir la reconstruction spatio-temporelle. La méthode du laboratoire IMAGINE représentant l'état de l'art en stéréovision, il est intéressant de développer cette méthode et de l'étendre à la reconstruction spatio-temporelle, c'est-à-dire la reconstruction d'une scène dynamique capturée par un dôme de caméras. Nous verrons cette méthode dans la seconde partie de ce manuscrit / The issue of retrieving a 3D shape from a static scene captured with multiple view point calibrated cameras has been deeply studied these last decades. Results presented in the stereovision benchmark made by Strecha et al., show the high quality of state of the art methods. Particularly, works from IMAGINE laboratory lead to impressive results. So, it becomes convenient to calibrate wider and wider scenes, in order to apply these stereovision algorithms to large scale scenes. Three main objectives appear : – The calibration accuracy should be improved. As stated by Yasutaka Furukawa, even stereovision benchmarks use noisy cameras. So one obvious way to improve stereovision, is to improve camera calibration. – It is crucial to take cycles into account in cameras graph in a global way. Most of nowadays methods are sequential and so present a drift. So these methods do not offer the guarantee to retrieve the loopy configuration for a loop made of a high number of images, but retrieve a spiral configuration. As we aim to calibrate wider and wider cameras networks, this point becomes quite crucial. – To calibrate wide cameras networks, having quick and linear algorithms can be necessary. Calibration methods we propose in the first part, allow to calibrate with an accuracy close to state of the art. Moreover, we take cyclicity constraints into account in a global way, with linear optimisations under linear constraints. So these methods allow to take cycle into account and benefit from quickness of linear programming. Finally, sterovision being a well studied topic, it is convenient to concentrate on the next step, that is, spatio-temporal reconstruction. The IMAGINE' stereovision method being the state of the art, it is interesting to extend this method to spatio-temporal reconstruction, that is, dynamique scene reconstruction captured from a dome of cameras

As video capturing devices become more ubiquitous from surveillance cameras to smart phones, the demand of automated video analysis is increasing as never before. One obstacle in this process is to efficiently locate where a human operator’s attention should be, and another is to determine the specific types of activities or actions without ambiguity. It is the special interest of this dissertation to locate spatial and temporal regions of interest in videos and to develop a better action representation for video-based activity analysis. This dissertation follows the scheme of “locating then recognizing” activities of interest in videos, i.e., locations of potentially interesting activities are estimated before performing in-depth analysis. Theoretical properties of regions of interest in videos are first exploited, based on which a unifying framework is proposed to locate both spatial and temporal regions of interest with the same settings of parameters. The approach estimates the distribution of motion based on 3D structure tensors, and locates regions of interest according to persistent occurrences of low probability. Two contributions are further made to better represent the actions. The first is to construct a unifying model of spatio-temporal relationships between reusable mid-level actions which bridge low-level pixels and high-level activities. Dense trajectories are clustered to construct mid-level actionlets, and the temporal relationships between actionlets are modeled as Action Graphs based on Allen interval predicates. The second is an effort for a novel and efficient representation of action graphs based on a sparse coding framework. Action graphs are first represented using Laplacian matrices and then decomposed as a linear combination of primitive dictionary items following sparse coding scheme. The optimization is eventually formulated and solved as a determinant maximization problem, and 1-nearest neighbor is used for action classification. The experiments have shown better results than existing approaches for regions-of-interest detection and action recognition.

Many agricultural landscapes in India are irrigated with wastewater, and it is a common livelihood practice particularly in urban and peri-urban areas. Farmers around urban agglomerations continuously depend on the wastewater released from nearby urban centres. While providing opportunities with respect to water and nutrient supply, irrigating with wastewater has adverse environmental impacts, particularly on the local aquifer systems. Therefore, addressing the wastewater irrigation influence on local aquifer systems is crucial for sustainable groundwater management. The present research demonstrates the impacts of wastewater irrigation, seasonality and spatio-temporal variations in the groundwater quality and its geochemical evolution and mixing processes in different land use and crop settings. The doctoral research aims at understanding the aquifer heterogeneity, land use conditions, groundwater dynamics and contaminant fate and transport in the long-term wastewater irrigation system to develop sustainable and suitable groundwater management strategies. The selected study watershed is located on the banks of Musi River in a peri-urban catchment of the Musi River basin in India. Statistical techniques, land use change modelling and solute flow and transport modelling tools are employed to identify and quantify the linkages between contaminants, agricultural use and environmental variables, particularly those characterizing the groundwater qualities. The research results suggest that concentrations of the major ionic substances increase after the monsoon season, especially in wastewater irrigated areas and the major polluted groundwaters to come from the wastewater irrigated parts of the watershed. Clusters of chemical variables identified indicate that groundwater pollution is highly impacted by mineral interactions and long-term wastewater irrigation. The groundwater geochemistry of the watershed is largely controlled by long-term wastewater irrigation, local rainfall patterns and water-rock interactions. The detected land use changes in the watershed indicate that, as a consequence of urban pressures, agricultural landscapes are being converted into built-up areas and, at the same time, former barren land is converted to agricultural plots. The mapped land use data are used in modelling the aquifer conditions and to observe the groundwater dynamics in the peri-urban environment. The study results provide the basis for sustainable agriculture and groundwater development using the efficient scenarios identified for wastewater irrigation management. The resulting strategies for integrated management of water and waste will contribute to the water security and achieve the respective Sustainable Development Goals (SDGs 2, 3, 6, 11 and 15).