O objetivo desta Tese é a proposição de uma metodologia alternativa para planejamento de transportes que contempla as ferramentas disponíveis na ciência do sensoriamento remoto. A perspectiva adotada analisa aspectos do planejamento de transportes urbanos, tendo como embasamento os dados e informações advindos das imagens de satélite com alto poder de resolução espacial. A metodologia usa a abordagem baseada em objetos para classificar imagens de satélite de sensoriamento remoto. Através do processo de classificação, identificam-se feições urbanas úteis para o planejamento de transporte, em especial áreas de concentração de polos geradores de viagens do município de João Pessoa no estado da Paraíba, Brasil. A proposta é que com base nesses dados, e outros provenientes de uma pesquisa de campo (pesquisa domiciliar origem/destino), é possível caracterizar o uso do solo e a correspondente demanda por transportes. O estudo se justifica por propor uma alternativa mais ágil e menos onerosa, em comparação aos métodos tradicionais de construção e atualização da base de dados para análises de transportes. Ao identificar as regiões da cidade com as maiores quantidades de viagens geradas, os resultados obtidos auxiliam nas ações de planejamento do sistema de transportes, visando alcançar o equilíbrio entre oferta e demanda de transporte com o uso do solo urbano. / The objective of this Thesis is to propose an alternative method of transportation planning that considers the tools available in the science of remote sensing. The perspective adopted examines aspects of urban transportation planning, having as basis the data and information coming from satellite images with high spatial resolution. The methodology uses the object-based approach to classify remote sensing satellite imagery. Through the classification process, urban features useful for transportation planning are identified, mainly areas of concentration of trip generation in the city of João Pessoa, state of Paraíba, Brazil. The proposal is that, based on these data, and others from a field research (origin/destination home-interview survey), it is possible to characterize the land use and the corresponding demand for transport. The study is justified because it proposes a more agile and less costly alternative, compared to traditional methods of building and updating the database for transport analysis. By identifying areas of the city with the largest amounts of trips generated, the results support planning actions on the transportation system, in order to achieve a balance between transport supply and demand with urban land use.

Em países em desenvolvimento, doenças infecciosas se constituem ainda um grave problema de saúde pública. Muitas vezes, essas doenças estão altamente relacionadas a condições urbanas que podem ser encontradas em áreas mais pobres. Nesses casos, o sensoriamento remoto (SR) pode ser utilizado como uma poderosa ferramenta de estudo. Novos produtos de SR se encontram disponíveis no mercado, permitindo o desenvolvimento de análises espaciais cada vez mais profundas e precisas. No entanto, a complexidade que envolve a epidemiologia de doenças, a irregularidade de ocupações urbanas e a heterogeneidade das imagens de alta resolução espacial têm restringido o desenvolvimento de estudos nesse campo científico. O desafio de identificar elementos e tipologias urbanas em imagens de sensoriamento remoto relacionadas à ocorrência da leptospirose justifica-se pela crença de que ferramentas de SR podem ser mais amplamente utilizadas no monitoramento de carências urbanísticas e, consequentemente, na gestão de ações e investimentos públicos. A metodologia contempla uma revisão bibliográfica sistemática, com base na qual foram criados modelos de transmissão da leptospirose e investigadas tipologias urbanas presentes na área de estudo. As variáveis baseadas em dados de SR que formam os indicadores dos modelos e que caracterizam as tipologias foram usadas para definir objetos e atributos, alvos das investigações em imagens de alta resolução espacial. Os procedimentos de SR adotados baseiam-se na segmentação multi-nível, classificação baseada em objeto, e utilizam ortofotografias aéreas, imagem QuickBird e base cartográfica do eixo viário do Subúrbio Ferroviário de Salvador. Para o cálculo das variáveis utilizou-se produtos do processamento da imagem QuickBird. Procedimentos de geoprocessamento foram realizados em sistema de informações geográficas. Por fim, realizaram-se as primeiras análises epidemiológicas que investigam a relação da leptospirose com os elementos e tipologias urbanas identificados por meio de SR, cujos resultados apontam maior influência do percentual de pavimentação das vias, sua largura e qualidade da edificação na possibilidade de ocorrência da leptospirose no Subúrbio. Possíveis fontes de viés são discutidas ao lado de propostas de continuação da pesquisa. Apesar dos problemas e limitações identificados no processo, o estudo mostra que a metodologia desenvolvida baseada em SR se constitui uma poderosa ferramenta de análise do espaço intra-urbano, uma vez que permite a identificação de elementos e tipologias relacionados a situações de risco, apoiando assim, o direcionamento de investimentos públicos que venham refletir na melhoria das condições de saúde da população. / In developing countries, infectious diseases are still a serious public health problem. These diseases are often and highly related to urban conditions found in poor areas, in these cases, remote sensing (RS) can be used as a powerful tool. New RS products are now available allowing the development of more complex and precise spatial analysis. On the other hand, the complexity of epidemiological studies, the lack of regularity of precarious urban settlements and the heterogeneity of high spatial resolution images have been restricting the development of studies in this areas. The challenge of identifying urban elements and typologies related to the leptospirosis using RS products is pursued due the belief that RS can be more used among professionals and researchers in the task of monitoring the urban environment, and directing public investments and actions. The methodology presented consists in a broad literature review, which was used to support leptospirosis transmission risk models and to find urban typologies at the study area. Variables based on RS were identified in the disease models and in the typologies characterization. This models and typologies also defined targets to look for in the high spatial resolution images. RS procedures were based on multi-level segmentation, object-based classification, aerial photography, QuickBird satellite images and street axis vector data of the Railroad Suburb of Salvador. In order to obtain the variable\'s values, results of QuickBird image processing were added to a geographic database and processed using vector and raster over layering techniques. At last, epidemiological analysis were initiated aiming to find its relationship with the urban elements and typologies identified using RS. The results points paved streets, streets wideness and house quality as the RS variables that have more influence on the leptospirosis transmission chance. The dissertation also presents research restrains, potentials, possible sources of bias and future studies proposals. It concludes that the RS based methodology presented is a powerful tool for urban analysis, due to its capabilities for identifying urban targets related to risky situations, and, therefore, for helping direct public investments to improve life conditions an unprivileged city areas.

Ce travail de thèse propose une nouvelle approche ponctuelle pour l'analyse de texture dans l'imagerie de télédétection à très haute résolution (THR). Cette approche ne prend en compte que des points caractéristiques, et non pas tous les pixels dans l'image, pour représenter et caractériser la texture. Avec l'augmentation de la résolution spatiale des capteurs satellitaires, les images THR ne vérifient que faiblement l'hypothèse de stationnarité. Une telle approche devient donc pertinente étant donné que seuls l'interaction et les caractéristiques des points-clés sont exploitées. De plus, puisque notre approche ne considère pas tous les pixels dans l'image comme le font la plupart des méthodes denses de la littérature, elle est plus à-même de traiter des images de grande taille acquises par des capteurs THR. Dans ce travail, la méthode ponctuelle est appliquée en utilisant des pixels de maxima locaux et minima locaux (en intensité) extraits à partir de l'image. Elle est intégrée dans plusieurs chaînes de traitement en se fondant sur différentes techniques existantes telles la théorie des graphes, la notion de covariance, la mesure de distance géométrique, etc. En conséquence, de nombreuses applications basées sur la texture sont abordées en utilisant des données de télédétection (images optiques et radar), telles l'indexation d'images, la segmentation, la classification et la détection de changement, etc. En effectuant des expériences dédiées à chaque application thématique, la pertinence et l'efficacité du cadre méthodologique proposé sont confirmées et validées. / This thesis work proposes a novel pointwise approach for texture analysis in the scope of very high resolution (VHR) remote sensing imagery. This approach takes into consideration only characteristic pixels, not all pixels of the image, to represent and characterize textural features. Due to the fact that increasing the spatial resolution of satellite sensors leads to the lack of stationarity hypothesis in the acquired images, such an approach becomes relevant since only the interaction and characteristics of keypoints are exploited. Moreover, as this technique does not need to consider all pixels inside the image like classical dense approaches, it is more capable to deal with large-size image data offered by VHR remote sensing acquisition systems. In this work, our pointwise strategy is performed by exploiting the local maximum and local minimum pixels (in terms of intensity) extracted from the image. It is integrated into several texture analysis frameworks with the help of different techniques and methods such as the graph theory, the covariance-based approach, the geometric distance measurement, etc. As a result, a variety of texture-based applications using remote sensing data (both VHR optical and radar images) are tackled such as image retrieval, segmentation, classification, and change detection, etc. By performing dedicated experiments to each thematic application, the effectiveness and relevance of the proposed approach are confirmed and validated.

La cartographie, l'inventaire et le suivi de glissements de terrain sont indispensables pour l'évaluation de l'aléa glissements de terrain et la gestion des catastrophes. La disponibilité croissante des satellites THR, des drones et des appareils photo numériques grand public offre un grand potentiel pour soutenir ces tâches à l'échelle régionale et locale en complément detechniques établies telles que l'instrumentation in-situ, radar, et les acquisitions par scanner laser. Un manque d'outils de traitement d'image pour l’extraction efficace d’informations pertinentes à partir de différents types d'imagerie optique complique encore l'exploitation des données optiques et entrave la mise en oeuvre de services opérationnels. Cette thèse est consacrée à l'élaboration et l'application de techniques de traitement d'image pour la cartographie, la caractérisation et la surveillance des glissements de terrain en exploitant des données d'imagerie optique. Un état de l'art approfondi des techniques de télédétection innovantes pour la surveillance des glissements de terrain est proposé et démontre le potentiel et les limites des techniques et propose des critères pour le choix des capteurs disponibles (plateformes et méthodes d'analyse d'images) selon le processus observé et les besoins des utilisateurs. Pour la cartographie rapide des glissements de terrain lors de catastrophes majeures, une méthode qui combine segmentation d'image et apprentissage supervisé est développée pour l'analyse des images satellitaires THR à travers plusieurs exemples en Chine, au Brésil, à Haïti, en Italie et en France. Pour l'analyse de glissements de terrain à l'échelle locale, la recherche a élaboré des chaînes de traitement d'images pour la détection de fissures à partir de séries temporelles d'images de drones comme possible géo-indicateurs de l'activité des glissements, la mesure des champs de déplacements horizontaux à partir d'images satellitaires THR utilisant en utilisant des méthodes stéréophotogrammétrie et par corrélation d’image, et les mesures 3D à partir de photographies terrestres basées sur des méthodes de photogrammétrie multi-images. / Landslide inventory mapping and monitoring are indispensable for hazard assessment and disaster management. The enhanced availability of VHR satellites, UAVs and consumer grade digital cameras offers a great potential to support those tasks at regional and local scales, and to complement established techniques such as in situ instrumentation, radar, andlaser scanning. A lack of image processing tools for the efficient extraction process-relevant information from different types of optical imagery still complicates the exploitation of optical data and hinders the implementation of operational services. This doctoral thesis is dedicated to the development and application of image processing techniques for the mapping,characterization and monitoring of landslides with optical remote sensing data. A comprehensive review of innovative remote sensing techniques for landslide monitoring shows the potential and limitations of available techniques and guides the selection of the most appropriate combination of sensors – platforms – image analysis methods according to the observed process and end-user needs. For the efficient detection of landslides after major triggering events at the regional scale, a method for rapid mapping combining image segmentation, feature extraction, supervised learning is developed. For detailed landslide investigations at the local scale, this study elaborates image processing chains for detection of surface fissures in time-series of UAV images as geo-indicators of landslide activity, the measurement of horizontal surface displacements from VHR satellite images using stereo-photogrammetric and image correlation methods, and 3D measurements from terrestrial photographs based on multi-view open-source photogrammetry.

Le mélanome est la forme la plus grave de cancer de la peau. Cette thèse a contribué au développement de deux approches différentes pour le diagnostic assisté par ordinateur du mélanome : approche analytique et approche comparative.L'approche analytique imite le comportement du dermatologue en détectant les caractéristiques de malignité sur la base de méthodes analytiques populaires dans une première étape, et en combinant ces caractéristiques dans une deuxième étape. Nous avons étudié l’impacte d’un système du diagnostic automatique utilisant des images dermoscopique de lésions cutanées pigmentées sur le diagnostic de dermatologues. L'approche comparative, appelé concept du Vilain Petit Canard (VPC), suppose que les naevus chez le même patient ont tendance à partager certaines caractéristiques morphologiques ainsi que les dermatologues identifient quelques groupes de similarité. VPC est le naevus qui ne rentre dans aucune de ces groupes, susceptibles d'être mélanome. / Melanoma is the most serious type of skin cancer. This thesis focused on the development of two different approaches for computer-aided diagnosis of melanoma: analytical approach and comparative approach. The analytical approach mimics the dermatologist’s behavior by first detecting malignancy features based on popular analytical methods, and in a second step, by combining these features. We investigated to what extent the melanoma diagnosis can be impacted by an automatic system using dermoscopic images of pigmented skin lesions. The comparative approach, called Ugly Duckling (UD) concept, assumes that nevi in the same patient tend to share some morphological features so that dermatologists identify a few similarity clusters. UD is the nevus that does not fit into any of those clusters, likely to be suspicious. The goal was to model the ability of dermatologists to build consistent clusters of pigmented skin lesions in patients.

This paper presents methods and results of county-level population estimation using Landsat Thematic Mapper (TM) images of Denton County and Collin County in Texas. Landsat TM images acquired in March 2000 were classified into residential and non-residential classes using maximum likelihood classification and knowledge-based classification methods. Accuracy assessment results from the classified image produced using knowledge-based classification and traditional supervised classification (maximum likelihood classification) methods suggest that knowledge-based classification is more effective than traditional supervised classification methods. Furthermore, using randomly selected samples of census block groups, ordinary least squares (OLS) and geographically weighted regression (GWR) models were created for total population estimation. The overall accuracy of the models is over 96% at the county level. The results also suggest that underestimation normally occurs in block groups with high population density, whereas overestimation occurs in block groups with low population density.

[pt] Classificação de imagens é o processo de extração de informação em imagens digitais para reconhecimento de padrões e objetos homogêneos, que em sensoriamento remoto propõe-se a encontrar padrões entre os pixels pertencentes a uma imagem digital e áreas da superfície terrestre, para uma análise posterior por um especialista. Nesta dissertação, utilizamos a metodologia de aprendizado de máquina support vector machines para o problema de classificação de imagens, devido a possibilidade de trabalhar com grande quantidades de características. Construímos classificadores para o problema, utilizando imagens distintas que contém as informações de espaços de cores RGB e HSB, dos valores altimétricos e do canal infravermelho de uma região. Os valores de relevo ou altimétricos contribuíram de forma excelente nos resultados, uma vez que esses valores são características fundamentais de uma região e os mesmos não tinham sido analisados em classificação de imagens de sensoriamento remoto. Destacamos o resultado final, do problema de classificação de imagens, para o problema de identificação de piscinas com vizinhança dois. Os resultados obtidos são 99 por cento de acurácia, 100 por cento de precisão, 93,75 por cento de recall, 96,77 por cento de F-Score e 96,18 por cento de índice Kappa. / [en] Image Classification is an information extraction process in digital images for pattern and homogeneous objects recognition. In remote sensing it aims to find patterns from digital images pixels, covering an area of earth surface, for subsequent analysis by a specialist. In this dissertation, to this images classification problem we employ Support Vector Machines, a machine learning methodology, due the possibility of working with large quantities of features. We built classifiers to the problem using different image information, such as RGB and HSB color spaces, altimetric values and infrared channel of a region. The altimetric values contributed to excellent results, since these values are fundamental characteristics of a region and they were not previously considered in remote sensing images classification. We highlight the final result, for the identifying swimming pools problem, when neighborhood is two. The results have 99 percent accuracy, 100 percent precision, 93.75 percent of recall, 96.77 percent F-Score and 96.18 percent of Kappa index.

E-handeln ökar stadigt och mellan åren 2010 och 2014 var det en ökning på antalet konsumenter som handlar online från 28,9% till 34,2%. Otillräcklig information kring en produkts pris tvingar köpare att leta bland flera olika återförsäljare efter det bästa priset. Det finns olika sätt att ta fram informationen som krävs för att kunna jämföra priser. En metod för att kunna jämföra priser är automatiserad produktmatchning. Denna metod använder algoritmer för bildigenkänning där dess syfte är att detektera, lokalisera och känna igen objekt i bilder. Bildigenkänningsalgoritmer har ofta problem med att hitta objekt i bilder på grund av yttre faktorer såsom belysning, synvinklar och om bilden innehåller mycket onödig information. Tidigare har algoritmer såsom ANN (artificial neural network), random forest classifier och support vector machine används men senare undersökningar har visat att CNN (convolutional neural network) är bättre på att hitta viktiga egenskaper hos objekt som gör dem mindre känsliga mot dessa yttre faktorer. Två exempel på alternativa CNN-arkitekturer som vuxit fram är EfficientNet och ResNet som båda har visat bra resultat i tidigare forskning men det finns inte mycket forskning som hjälper en välja vilken CNN-arkitektur som leder till ett så bra resultat som möjligt. Vår frågeställning är därför: Vilken av EfficientNet- och ResNetarkitekturerna ger det högsta resultatet på produktmatchning med utvärderingsmåtten f1-score, precision och recall? Resultatet av studien visar att EfficientNet är den över lag bästa arkitekturen för produktmatchning på studiens datamängd. Resultatet visar också att ResNet var bättre än EfficientNet på att föreslå rätt matchningar av bilderna. De matchningarna ResNet gör stämmer mer än de matchningar EfficientNet föreslår då Resnet fick ett högre recall än vad EfficientNet fick.  EfficientNet uppnår dock en bättre recall som visar att EfficientNet är bättre än ResNet på att hitta fler eller alla korrekta matchningar bland sina potentiella matchningar. Men skillnaden i recall är större mellan modellerna vilket göra att EfficientNet får en högre f1-score och är över lag bättre än ResNet, men vad som är viktigast kan diskuteras. Är det viktigt att de föreslagna matchningarna är korrekta eller att man hittar alla korrekta matchningar. Är det viktigaste att de föreslagna matchningarna är korrekta har ResNet ett övertag men är det viktigare att hitta alla korrekta matchningar har EfficientNet ett övertag. Resultatet beror därför på vad som anses vara viktigast för att avgöra vilken av arkitekturerna som ger bäst resultat. / E-commerce is steadily increasing and between the years 2010 and 2014, there was an increase in the number of consumers shopping online from 28,9% to 34,2%. Insufficient information about the price of a product forces buyers to search among several different retailers for the best price. There are different ways to produce the information required to be able to compare prices. One method to compare prices is automated product matching. This method uses image recognition algorithms where its purpose is to detect, locate and recognize objects in images. Image recognition algorithms often have problems finding objects in images due to external factors such as brightness, viewing angles and if the image contains a lot of unnecessary information. In the past, algorithms such as ANN, random forest classifier and support vector machine have been used, but recent studies have shown that CNN is better at finding important properties of objects that make them less sensitive to these external factors. Two examples of alternative CNN architectures that have emerged are EfficientNet and ResNet, both of which have shown good results in previous studies, but there is not a lot of research that helps one choose which CNN architecture that leads to the best possible result. Our question is therefore: Which of the EfficientNet and ResNet architectures gives the highest result on product matching with the evaluation measures f1-score, precision, and recall? The results of the study show that EfficientNet is the overall best architecture for product matching on the dataset. The results also show that ResNet was better than EfficientNet in proposing the right matches for the images. The matches ResNet makes are more accurate than the matches EfficientNet suggests when Resnet received a higher precision than EfficientNet. However, EfficientNet achieves a better recall that shows that EfficientNet is better than ResNet at finding more or all correct matches among its potential matches. The difference in recall is greater than the difference in precision between the models, which means that EfficientNet gets a higher f1-score and is generally better than ResNet, but what is most important can be discussed. Is it important that the suggested matches are correct or that you find all the correct matches? If the most important thing is that the proposed matches are correct, ResNet has an advantage, but if it is more important to find all correct matches, EfficientNet has an advantage. The result therefore depends on what is considered to be most important in determining which of the architectures gives the best results

Indiana University-Purdue University Indianapolis (IUPUI) / Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove) redundant and insignificant elements that either are irrelevant or do not affect performance of the network upon disposition. Cardinality, a new dimension to the existing spatial dimensions, and class-balanced focal loss function, a weighting factor inversely proportional to the number of samples, has been incorporated in order to relieve the harsh effects of pruning, into the design of CondenseNeXt’s algorithm. Furthermore, extensive analyses of this novel CNN architecture was performed on three benchmarking image datasets: CIFAR-10, CIFAR-100 and ImageNet by deploying the trained weight on to an ARM-based embedded computing platform: NXP BlueBox 2.0, for real-time image classification. The outputs are observed in real-time in RTMaps Remote Studio’s console to verify the correctness of classes being predicted. CondenseNeXt achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error), and up to 59.98% reduction in forward FLOPs compared to CondenseNet. CondenseNeXt can also achieve a final trained model size of 2.9 MB, however at the cost of 2.26% in accuracy loss. Thus, performing image classification on ARM-Based computing platforms without requiring a CUDA enabled GPU support, with outstanding efficiency.

The aim of this thesis is to explore clustering algorithms of machine unsupervised learning, which can be used for image database classification by similarity. For chosen clustering algorithms is written up a theoretical basis. For better classification of used database this thesis deals with different methods of image preprocessing. With these methods the features from image are extracted. Next the thesis solves of implementation of preprocessing methods and practical application of clustering algorithms. In practical part is programmed aplication in Python programming language, which classifies the database of images into classes by similarity. The thesis tests all of used methods and at the end of the thesis is processed searches of results.