L'increment de bases de dades que cada vegada contenen imatges més difícils i amb un nombre més elevat de categories, està forçant el desenvolupament de tècniques de representació d'imatges que siguin discriminatives quan es vol treballar amb múltiples classes i d'algorismes que siguin eficients en l'aprenentatge i classificació. Aquesta tesi explora el problema de classificar les imatges segons l'objecte que contenen quan es disposa d'un gran nombre de categories. Primerament s'investiga com un sistema híbrid format per un model generatiu i un model discriminatiu pot beneficiar la tasca de classificació d'imatges on el nivell d'anotació humà sigui mínim. Per aquesta tasca introduïm un nou vocabulari utilitzant una representació densa de descriptors color-SIFT, i desprès s'investiga com els diferents paràmetres afecten la classificació final. Tot seguit es proposa un mètode par tal d'incorporar informació espacial amb el sistema híbrid, mostrant que la informació de context es de gran ajuda per la classificació d'imatges. Desprès introduïm un nou descriptor de forma que representa la imatge segons la seva forma local i la seva forma espacial, tot junt amb un kernel que incorpora aquesta informació espacial en forma piramidal. La forma es representada per un vector compacte obtenint un descriptor molt adequat per ésser utilitzat amb algorismes d'aprenentatge amb kernels. Els experiments realitzats postren que aquesta informació de forma te uns resultats semblants (i a vegades millors) als descriptors basats en aparença. També s'investiga com diferents característiques es poden combinar per ésser utilitzades en la classificació d'imatges i es mostra com el descriptor de forma proposat juntament amb un descriptor d'aparença millora substancialment la classificació. Finalment es descriu un algoritme que detecta les regions d'interès automàticament durant l'entrenament i la classificació. Això proporciona un mètode per inhibir el fons de la imatge i afegeix invariança a la posició dels objectes dins les imatges. S'ensenya que la forma i l'aparença sobre aquesta regió d'interès i utilitzant els classificadors random forests millora la classificació i el temps computacional. Es comparen els postres resultats amb resultats de la literatura utilitzant les mateixes bases de dades que els autors Aixa com els mateixos protocols d'aprenentatge i classificació. Es veu com totes les innovacions introduïdes incrementen la classificació final de les imatges. / The release of challenging data sets with ever increasing numbers of object categories isforcing the development of image representations that can cope with multiple classes andof algorithms that are efficient in training and testing. This thesis explores the problem ofclassifying images by the object they contain in the case of a large number of categories. We first investigate weather the hybrid combination of a latent generative model with a discriminative classifier is beneficial for the task of weakly supervised image classification.We introduce a novel vocabulary using dense color SIFT descriptors, and then investigate classification performances by optimizing different parameters. A new way to incorporate spatial information within the hybrid system is also proposed showing that contextual information provides a strong support for image classification. We then introduce a new shape descriptor that represents local image shape and its spatial layout, together with a spatial pyramid kernel. Shape is represented as a compactvector descriptor suitable for use in standard learning algorithms with kernels. Experimentalresults show that shape information has similar classification performances and sometimes outperforms those methods using only appearance information. We also investigate how different cues of image information can be used together. Wewill see that shape and appearance kernels may be combined and that additional informationcues increase classification performance. Finally we provide an algorithm to automatically select the regions of interest in training. This provides a method of inhibiting background clutter and adding invariance to the object instance's position. We show that shape and appearance representation over the regions of interest together with a random forest classifier which automatically selects the best cues increases on performance and speed. We compare our classification performance to that of previous methods using the authors'own datasets and testing protocols. We will see that the set of innovations introduced here lead for an impressive increase on performance.

Sundarban Reserved Forest in Bangladesh is playing a significant role in local and national economy and is providing protection to the coastline as well as to the indigenous people. During the past decades and also in recent time this forest was heavily disturbed by human intervention in many aspects. As a consequence the resources of the forest are fragmenting, shrinking and declining, which in turn leads to an increasing failure of satisfying increasing demands both at local and national levels. Therefore accurate and continuously updated spatial information is needed for optimising forest management and environmental planning on both levels to support the fulfilment of urgent needs of sustainability of the forest. Considering the specific topography and the poor accessibility of the forest versus the task of collecting information, remote sensing is an attractive, if not the only means of obtaining sound full-coverage spatial information on forest cover of Sundarban. This research used medium resolution Landsat ETM data of November 2000 and Landsat TM data of January 1989 to assess and monitor the forest for 1. Identification of the operational tools for mapping and monitoring the forest as well as on the examination of the reliability of the application of multitemporal satellite remote sensing data for building spatial databases on forest cover in Sundarban. 2. Based on the existing management plan of the forest as well as the spectral properties of Landsat ETM imagery a level III classification system was developed. 3. This classification strategy was tested by applying several methods to achieve the classification result with the highest accuracy and thus to build the most reliable methodology for mapping forest cover in Sundarban. 4. Forest cover change was assessed for the period of eleven years. Significant changes have been observed due to illegal removal of trees from the forest although a governmental moratorium on banning timber extraction exists since 1989. 5. Development of an operational monitoring scheme by means of multitemporal satellite imagery analysis, which will allow concerned authorities to set up sustainable and appropriate monitoring of the Sundarban Reserved Forest. / Das Schutzgebiet des Sundarban Mangrovenwaldes in Bangladesh spielt eine entscheidende Rolle in Hinsicht auf nationale und lokale sozio-ökonomische und sozio-ökologische Aspekte. Das Waldgebiet stabilisiert nicht nur die Küstenlinie, sondern schützt auch die Bevölkerung vor den Einflüssen von Flutkatastrophen. Durch menschlichen Einfluss wurde die Region während der letzten Jahrzehnte mehr und mehr unmittelbar gestört. Der Rückgang des Ertrags an Ressourcen aus dem Wald führte zu wachsender Unzufriedenheit in der von diesen Nutzungs-möglichkeiten abhängigen Bevölkerung. Um eine Optimierung des Waldmanagements durchführen zu können, werden kontinuierliche und genaue raumbezogene Daten benötigt. Betrachtet man die spezifische Topographie und die schlechte Zugänglichkeit der Waldgebiete, so bietet die Fernerkundung eine attraktive Möglichkeit, raumbezogene Informationen für die großen Flächen des Sundurban Mangrovenwaldes zu erfassen. Zur Analyse und Überwachung der Waldgebiete wurden zwei Satellitenbild-Datensätze mit mittlerer Auflösung verwendet, und zwar Landsat ETM Daten aus dem Jahre 2000 (November) sowie Landsat TM Daten aus dem Jahre 1989 (Januar). Die zentralen Aktivitäten im Rahmen der Bearbeitung der Dissertation beziehen sich auf 1. die Identifikation der notwendigen Werkzeuge für eine erfolgreiche Kartierung und Überwachung der Waldgebiete sowie Untersuchung der Zuverlässigkeit multi-temporaler Fernerkundungsdaten für den Aufbau einer Datenbasis für die Kartierung von Waldbedeckungsarten im Untersuchungsgebiet des Sunderban Mangroven-waldes, 2. die Entwicklung eines Klassifikationssystems nach dem USGS-Schlüssel (Auflösungsebene III) auf Grundlage des existierenden Managementplanes und der spektralen Qualität der Landsat ETM Satellitenbilddaten, 3. den Test der Klassifikationsstrategie durch Adaption unterschiedlicher Methoden und Optimierung in bezug auf Erzielung eines Ergebnisses in maximal erreichbarer Genauigkeit als Ausgangspunkt für den Aufbau einer Methodologie zum Monitoring des Sunderban Mangrovenwaldes, 4. die Extraktion der Veränderungen der Waldbedeckung über ein Zeitintervall von 11 Jahren mit weitreichenden Erkenntnissen zur Dynamik der Degradations-effekte, die hauptsächlich durch illegales Fällen trotz Verbot durch ein Regierungs-memorandum seit 1989 beschleunigt wird, 5. die Entwicklung einer operationellen Monitoring-Struktur mit Hilfe von multi-temporaler Satellitenbildanalyse für ein nachhaltiges und angepasstes raumbezo-genes Management des Sunderban-Mangrovenwaldes.

With the availability of inexpensive hardware and software, digital imaging has become an important medium of communication in our daily lives. A huge amount of digital images are being collected and become available through the internet and stored in various fields such as personal image collections, medical imaging, digital arts etc. Therefore, it is important to make sure that images are stored, searched and accessed in an efficient manner. The use of bag of visual words (BOW) model for modelling images based on local invariant features computed at interest point locations has become a standard choice for many computer vision tasks. Based on this promising model, this thesis investigates three main problems: natural scene classification, annotation and retrieval. Given an image, the task is to design a system that can determine to which class that image belongs to (classification), what semantic concepts it contain (annotation) and what images are most similar to (retrieval). This thesis contributes to scene classification by proposing a weighting approach, named keypoints density-based weighting method (KDW), to control the fusion of colour information and bag of visual words on spatial pyramid layout in a unified framework. Different configurations of BOW, integrated visual vocabularies and multiple image descriptors are investigated and analyzed. The proposed approaches are extensively evaluated over three well-known scene classification datasets with 6, 8 and 15 scene categories using 10-fold cross validation. The second contribution in this thesis, the scene annotation task, is to explore whether the integrated visual vocabularies generated for scene classification can be used to model the local semantic information of natural scenes. In this direction, image annotation is considered as a classification problem where images are partitioned into 10x10 fixed grid and each block, represented by BOW and different image descriptors, is classified into one of predefined semantic classes. An image is then represented by counting the percentage of every semantic concept detected in the image. Experimental results on 6 scene categories demonstrate the effectiveness of the proposed approach. Finally, this thesis further explores, with an extensive experimental work, the use of different configurations of the BOW for natural scene retrieval.

Image representation is in the heart of many computer vision algorithms. Different computer vision tasks (e.g. classification, detection) require discriminative image representations to recognize visual categories. In a nutshell, the bag-of-visual-words image representation is the most successful approach for object and scene recognition. In this thesis, we mainly revolve around this model and search for discriminative image representations. In the first part, we present a novel approach to incorporate spatial information in the BoVW method. In this framework, we present a simple and efficient way to infuse spatial information by taking advantage of the orientation and length of the segments formed by pairs of similar descriptors. We introduce the notion of soft-similarity to compute intra and inter visual word spatial relationships. We show experimentally that, our method adds important discriminative information to the BoVW method and complementary to the state-of-the-art method. Next, we focus on color description in general. Differing from traditional approaches of invariant description to account for photometric changes, we propose discriminative color descriptor. We demonstrate that such a color description automatically learns a certain degree of photometric invariance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape descriptor, the proposed color descriptor obtain excellent results on four challenging data sets.Finally, we focus on the most accurate color representation i.e. multispectral reflectance which is an intrinsic property of a surface. Even with the modern era technological advancement, it is difficult to extract reflectance information without sophisticated instruments. To this end, we propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results show that the accuracy of the spectral reconstruction improves significantly over the spectral reconstruction based on a single illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops

Notwithstanding the recent successes of deep convolutional neural networks for classification tasks, they are sensitive to the selection of their hyperparameters, which impose an exponentially large search space on modern convolutional models. Traditional hyperparameter selection methods include manual, grid, or random search, but these require expert knowledge or are computationally burdensome. Divergently, Bayesian optimization and evolutionary inspired techniques have surfaced as viable alternatives to the hyperparameter problem. Thus, an alternative hybrid approach that combines the advantages of these techniques is proposed. Specifically, the search space is partitioned into discrete-architectural, and continuous and categorical hyperparameter subspaces, which are respectively traversed by a stochastic genetic search, followed by a genetic-Bayesian search. Simulations on a prominent image classification task reveal that the proposed method results in an overall classification accuracy improvement of 0.87% over unoptimized baselines, and a greater than 97% reduction in computational costs compared to a commonly employed brute force approach. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

Nous assistons actuellement à une explosion de la quantité des données visuelles. Par exemple, plusieurs millions de photos sont partagées quotidiennement sur les réseaux sociaux. Les méthodes d'interprétation d'images vise à faciliter l'accès à ces données visuelles, d'une manière sémantiquement compréhensible. Dans ce manuscrit, nous définissons certains buts détaillés qui sont intéressants pour les taches d'interprétation d'images, telles que la classification ou la recherche d'images, que nous considérons dans les trois chapitres principaux. Tout d'abord, nous visons l'exploitation de la nature multimodale de nombreuses bases de données, pour lesquelles les documents sont composés d'images et de descriptions textuelles. Dans ce but, nous définissons des similarités entre le contenu visuel d'un document, et la description textuelle d'un autre document. Ces similarités sont calculées en deux étapes, tout d'abord nous trouvons les voisins visuellement similaires dans la base multimodale, puis nous utilisons les descriptions textuelles de ces voisins afin de définir une similarité avec la description textuelle de n'importe quel document. Ensuite, nous présentons une série de modèles structurés pour la classification d'images, qui encodent explicitement les interactions binaires entre les étiquettes (ou labels). Ces modèles sont plus expressifs que des prédicateurs d'étiquette indépendants, et aboutissent à des prédictions plus fiables, en particulier dans un scenario de prédiction interactive, où les utilisateurs fournissent les valeurs de certaines des étiquettes d'images. Un scenario interactif comme celui-ci offre un compromis intéressant entre la précision, et l'effort d'annotation manuelle requis. Nous explorons les modèles structurés pour la classification multi-étiquette d'images, pour la classification d'image basée sur les attributs, et pour l'optimisation de certaines mesures de rang spécifiques. Enfin, nous explorons les classifieurs par k plus proches voisins, et les classifieurs par plus proche moyenne, pour la classification d'images à grande échelle. Nous proposons des méthodes d'apprentissage de métrique efficaces pour améliorer les performances de classification, et appliquons ces méthodes à une base de plus d'un million d'images d'apprentissage, et d'un millier de classes. Comme les deux méthodes de classification permettent d'incorporer des classes non vues pendant l'apprentissage à un coût presque nul, nous avons également étudié leur performance pour la généralisation. Nous montrons que la classification par plus proche moyenne généralise à partir d'un millier de classes, sur dix mille classes à un coût négligeable, et les performances obtenus sont comparables à l'état de l'art. / We are currently experiencing an exceptional growth of visual data, for example, millions of photos are shared daily on social-networks. Image understanding methods aim to facilitate access to this visual data in a semantically meaningful manner. In this dissertation, we define several detailed goals which are of interest for the image understanding tasks of image classification and retrieval, which we address in three main chapters. First, we aim to exploit the multi-modal nature of many databases, wherein documents consists of images with a form of textual description. In order to do so we define similarities between the visual content of one document and the textual description of another document. These similarities are computed in two steps, first we find the visually similar neighbors in the multi-modal database, and then use the textual descriptions of these neighbors to define a similarity to the textual description of any document. Second, we introduce a series of structured image classification models, which explicitly encode pairwise label interactions. These models are more expressive than independent label predictors, and lead to more accurate predictions. Especially in an interactive prediction scenario where a user provides the value of some of the image labels. Such an interactive scenario offers an interesting trade-off between accuracy and manual labeling effort. We explore structured models for multi-label image classification, for attribute-based image classification, and for optimizing for specific ranking measures. Finally, we explore k-nearest neighbors and nearest-class mean classifiers for large-scale image classification. We propose efficient metric learning methods to improve classification performance, and use these methods to learn on a data set of more than one million training images from one thousand classes. Since both classification methods allow for the incorporation of classes not seen during training at near-zero cost, we study their generalization performances. We show that the nearest-class mean classification method can generalize from one thousand to ten thousand classes at negligible cost, and still perform competitively with the state-of-the-art.

La construction d'algorithmes classifiant des images à grande échelle est devenue une t^ache essentielle du fait de la difficulté d'effectuer des recherches dans les immenses collections de données visuelles non-etiquetées présentes sur Internet. L'objetif est de classifier des images en fonction de leur contenu pour simplifier la gestion de telles bases de données. La classification d'images à grande échelle est un problème complexe, de par l'importance de la taille des ensembles de données, tant en nombre d'images qu'en nombre de classes. Certaines de ces classes sont dites "fine-grained" (sémantiquement proches les unes des autres) et peuvent même ne contenir aucun représentant étiqueté. Dans cette thèse, nous utilisons des représentations à l'état de l'art d'images et nous concentrons sur des méthodes d'apprentissage efficaces. Nos contributions sont (1) un banc d'essai d'algorithmes d'apprentissage pour la classification à grande échelle et (2) un nouvel algorithme basé sur l'incorporation d'étiquettes pour apprendre sur des données peu abondantes. En premier lieu, nous introduisons un banc d'essai d'algorithmes d'apprentissage pour la classification à grande échelle, dans un cadre entièrement supervisé. Il compare plusieurs fonctions objectifs pour apprendre des classifieurs linéaires, tels que "un contre tous", "multiclasse", "classement", "classement avec pondération" par descente de gradient stochastique. Ce banc d'essai se conclut en un ensemble de recommandations pour la classification à grande échelle. Avec une simple repondération des données, la stratégie "un contre tous" donne des performances meilleures que toutes les autres. Par ailleurs, en apprentissage en ligne, un pas d'apprentissage assez petit s'avère suffisant pour obtenir des résultats au niveau de l'état de l'art. Enfin, l'arrêt prématuré de la descente de gradient stochastique introduit une régularisation qui améliore la vitesse d'entraînement ainsi que la capacité de régularisation. Deuxièmement, face à des milliers de classes, il est parfois difficile de rassembler suffisamment de données d'entraînement pour chacune des classes. En particulier, certaines classes peuvent être entièrement dénuées d'exemples. En conséquence, nous proposons un nouvel algorithme adapté à ce scénario d'apprentissage dit "zero-shot". Notre algorithme utilise des données parallèles, comme les attributs, pour incorporer les classes dans un espace euclidien. Nous introduisons par ailleurs une fonction pour mesurer la compatibilité entre image et étiquette. Les paramètres de cette fonction sont appris en utilisant un objectif de type "ranking". Notre algorithme dépasse l'état de l'art pour l'apprentissage "zero-shot", et fait preuve d'une grande flexibilité en permettant d'incorporer d'autres sources d'information parallèle, comme des hiérarchies. Il permet en outre une transition sans heurt du cas "zero-shot" au cas où peu d'exemples sont disponibles. / Building algorithms that classify images on a large scale is an essential task due to the difficulty in searching massive amount of unlabeled visual data available on the Internet. We aim at classifying images based on their content to simplify the manageability of such large-scale collections. Large-scale image classification is a difficult problem as datasets are large with respect to both the number of images and the number of classes. Some of these classes are fine grained and they may not contain any labeled representatives. In this thesis, we use state-of-the-art image representations and focus on efficient learning methods. Our contributions are (1) a benchmark of learning algorithms for large scale image classification, and (2) a novel learning algorithm based on label embedding for learning with scarce training data. Firstly, we propose a benchmark of learning algorithms for large scale image classification in the fully supervised setting. It compares several objective functions for learning linear classifiers such as one-vs-rest, multiclass, ranking and weighted average ranking using the stochastic gradient descent optimization. The output of this benchmark is a set of recommendations for large-scale learning. We experimentally show that, online learning is well suited for large-scale image classification. With simple data rebalancing, One-vs-Rest performs better than all other methods. Moreover, in online learning, using a small enough step size with respect to the learning rate is sufficient for state-of-the-art performance. Finally, regularization through early stopping results in fast training and a good generalization performance. Secondly, when dealing with thousands of classes, it is difficult to collect sufficient labeled training data for each class. For some classes we might not even have a single training example. We propose a novel algorithm for this zero-shot learning scenario. Our algorithm uses side information, such as attributes to embed classes in a Euclidean space. We also introduce a function to measure the compatibility between an image and a label. The parameters of this function are learned using a ranking objective. Our algorithm outperforms the state-of-the-art for zero-shot learning. It is flexible and can accommodate other sources of side information such as hierarchies. It also allows for a smooth transition from zero-shot to few-shots learning.

[pt] Informações precisas sobre a distribuição da população são reconhecidamente importantes. A fonte de informação mais completa sobre a população é o censo, cujos os dados são disponibilizados de forma agregada em setores censitários. Esses setores são unidades operacionais de tamanho e formas irregulares, que dificulta a análise espacial dos dados associados. Assim, a mudança de setores censitários para um conjunto de células regulares com estimativas adequadas facilitaria a análise. Uma metodologia a ser utilizada para essa mudança poderia ser baseada na classificação de imagens de sensoriamento remoto para a identificação de domicílios, que é a base das pesquisas envolvendo a população. A detecção de áreas edificadas é uma tarefa complexa devido a grande variabilidade de características de construção e de imagens. Os métodos usuais são complexos e muito dependentes de especialistas. Os processos automáticos dependem de grandes bases de imagens para treinamento e são sensíveis à variação de qualidade de imagens e características das construções e de ambiente. Nesta tese propomos a utilização de um método automatizado para detecção de edificações em imagens Google Earth que mostrou bons resultados utilizando um conjunto de imagens relativamente pequeno e com grande variabilidade, superando as limitações dos processos existentes. Este resultado foi obtido com uma aplicação prática. Foi construído um conjunto de imagens com anotação de áreas construídas para 12 regiões do Brasil. Estas imagens, além de diferentes na qualidade, apresentam grande variabilidade nas características das edificações e no ambiente geográfico. Uma prova de conceito será feita na utilização da classificação de área construída nos métodos dasimétrico para a estimação de população em gride. Ela mostrou um resultado promissor quando comparado com o método usual, possibilitando a melhoria da qualidade das estimativas. / [en] The importance of precise information about the population distribution is widely acknowledged. The census is considered the most reliable and complete source of this information, and its data are delivered in an aggregated form in sectors. These sectors are operational units with irregular shapes, which hinder the spatial analysis of the data. Thus, the transformation of sectors onto a regular grid would facilitate such analysis. A methodology to achieve this transformation could be based on remote sensing image classification to identify building where the population lives. The building detection is considered a complex task since there is a great variability of building characteristics and on the images quality themselves. The majority of methods are complex and very specialist dependent. The automatic methods require a large annotated dataset for training and they are sensitive to the image quality, to the building characteristics, and to the environment. In this thesis, we propose an automatic method for building detection based on a deep learning architecture that uses a relative small dataset with a large variability. The proposed method shows good results when compared to the state of the art. An annotated dataset has been built that covers 12 cities distributed in different regions of Brazil. Such images not only have different qualities, but also shows a large variability on the building characteristics and geographic environments. A very important application of this method is the use of the building area classification in the dasimetric methods for the population estimation into grid. The concept proof in this application showed a promising result when compared to the usual method allowing the improvement of the quality of the estimates.

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.