As plantações de eucaliptos representam grande potencial econômico para a indústria de papel, celulose, entre outras, além de apresentar uma série de características positivas como alta produtividade, grande potencial de adaptação e ampla diversidade de espécies. Em consequência a tais vantagens, há décadas diversas pesquisas vem sendo realizadas com o intuito de monitorar e detectar diversas doenças que aferem este tipo de cultura. O monitoramento rápido das doenças em eucaliptos torna-se um requisito para evitar grandes perdas econômicas. Neste projeto de pesquisa utilizou-se imagens aéreas obtidas por VANTs (Veículos Aéreos Não-Tripulados) para detectar um tipo específico de estresse que afeta as plantações de eucaliptos: a Murcha de Ceratocyst is. Após rotular eucaliptos doentes e saudáveis e outras estruturas em imagens aéreas, técnicas de Aprendizado de Máquina Supervisionado foram desenvolvidas para generalizar o conhecimento e possibilitar uma rápida detecção através das imagens RGB e multiespectrais. Dentre as técnicas utilizadas, destacou-se a arquitetura de Redes Neurais Convolucional chamada de Custom- CNN, inspirada no modelo da tradicional arquitetura Lenet -5 agregando-se melhorias do estado-da-arte, como a camada convolucional 1x1. Na classificação do conjunto RGB, a Custom-CNN obteve o maior F-score, de 0,81, sendo que a técnica SVM-rbf obteve 0,67. No conjunto de dados com imagens multiespectrais, a Lenet -5 e a Custom-CNN at ingiram, respectivamente, 0,63 e 0,66 de F-score, enquanto o SVM-rbf obteve 0,46. Esta dissertação apresenta a metodologia utilizada para a classificação, elencando as principais características dos algoritmos utilizados, bem como os resultados experimentais obtidos. Há ainda uma aplicação do classificador Regressão Logística para o planejamento de trajetória com VANTs. / Eucalypt us plantations represent great economic potential for t he paper, pulp, among others, in addition to presenting a number of positive characteristics such as high productivity, great potential for adaptaion and wide diversity of species. In consequence of t hese advantages, there are several decades research has been conducted in order to monitor and detect various diseases that affect s this type of culture. The rapid monitoring of diseases in eucalyptus becomes a requirement to avoid major economic losses. In t his research project we used aerial images obtained by UAVs (Unmanned Aerial Vehicles) to detect an specific type of stress t hat a effect s eucalyptus plantations: the Ceratocyst is wilt . After labeling diseased eucalyptus, healthy eucalyptus and other structures in aerial images, Supervised Machine Learning techniques were developed to generalize knowledge and enable rapid detection through RGB and multispectral images. Among the techniques used, stood out t he Convolutional Neural Network architecture called Custom-CNN, that was inspired by the model of t raditional Lenet -5 architecture and with state-of-the-art improvements, such as t he 1x1 convolution layer. In t he classification of RGB dataset , the Custom-CNN obtained the highest F-score of 0.81, and SVM-RBF technique obtained 0.67. In t he dataset with multispectral images, Lenet -5 and Custom-CNN obtained, respectively, 0.63 and 0.66 of F-score, while SVM-rbf obtained 0.46. This paper presents the methodology used for classification, listing the main features of the algorithms and the experimental results. There is also an application of Logistic Regression classifier for path planning with UAVs.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2018-06-06T10:43:36Z No. of bitstreams: 2 Dissertação - Paulo Victor dos Santos - 2018.pdf: 13085792 bytes, checksum: 85f53ae260abe05ea15c17480a4cdebf (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-06-06T10:45:45Z (GMT) No. of bitstreams: 2 Dissertação - Paulo Victor dos Santos - 2018.pdf: 13085792 bytes, checksum: 85f53ae260abe05ea15c17480a4cdebf (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-06-06T10:45:45Z (GMT). No. of bitstreams: 2 Dissertação - Paulo Victor dos Santos - 2018.pdf: 13085792 bytes, checksum: 85f53ae260abe05ea15c17480a4cdebf (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-05-03 / The high cost of geo-referenced technologies capable of keeping up-to-date geographic information system data from a power distribution company, which has the basic urban map as the primary data layer, may result in non-purchase of this product, making information outdated or incomplete, generating in addition to losses, rework and confusion when there is a need to do verifications and validations that could be performed remotely. This proposal to support energy distribution concessionaires will use Computational Vision (VC) and Digital Image Processing (PDI) methods, allowing a low cost and efficient maintenance in layers of buildings present in basic urban maps of distributors. This findings might result in possible savings, without any need of displacement in the field to observe a situation that can be evidenced remotely. / O alto custo de tecnologias georreferenciadas capazes de manter atualizados os dados do sistema de informação geográfico de uma concessionária de distribuição, que tenha o mapa urbano básico como a principal camada de dados, pode implicar na não aquisição deste produto, fazendo com que estas informações estejam desatualizadas ou incompletas, gerando além de perdas, retrabalho e confusão quando há a necessidade de se fazer verificações e validações que poderiam ser executadas remotamente. Esta proposta para apoio às concessionárias de distribuição de energia, utilizará métodos de Visão Computacional (VC) e Processamento Digital de Imagens (PDI), possibilitando uma manutenção de baixo custo e eficiente nas camadas de edificações presentes nos mapas urbanos básicos das distribuidoras. Os resultados podem gerar economias, sem a necessidade de deslocamento em campo para observação de uma situação que poderá ser evidenciada remotamente.

Moore besitzen weltweit eine große Bedeutung für den Natur- und Klimaschutz. Sie dienen als Lebensraum für eine Vielzahl an Pflanzen- und Tierarten sowie als Kohlenstoffsenken. Aufgrund intensiver land- und forstwirtschaftlicher Nutzung weist die Mehrheit der Moorgebiete jedoch hochgradige Schäden auf, durch welche sie in ihrer Funktionalität beeinträchtigt werden. Um die charakteristischen Biotopeigenschaften wiederherzustellen, laufen derzeit zahlreiche Moorschutzprogramme, unter anderem in der deutsch-tschechischen Grenzregion im Osterzgebirge. Damit die Auswirkungen der durchgeführten Schutz- und Renaturierungsmaßnahmen auf die Vegetationsstruktur verfolgt und kontrolliert werden können, erfolgt in dieser Region regelmäßig eine stereoskopische Luftbildinterpretation der Moorflächen. Derartige manuelle Auswertungen sind jedoch mit einem hohen Arbeitsaufwand verbunden, weswegen eine Automatisierung der Prozesse angestrebt wird. In der vorliegenden Arbeit wird ein Verfahren präsentiert, mit welchem die Vegetationsstrukturen der Moore bei Satzung teilautomatisch klassifiziert werden können. Unter Verwendung von digitalen Luftbildern und einem digitalen Geländemodell lassen sich verschiedene Gras-, Baum- und Bodenarten voneinander trennen und lokalisieren. Für die Unterscheidung der einzelnen Klassen werden sowohl pixel- als auch objektbasierte Merkmale in die Datenanalyse einbezogen. Aufnahmen der Satelliten WorldView-2 und Sentinel-2A wurden ebenfalls auf ihr Auswertepotential hin untersucht, allerdings ohne zufriedenstellende Ergebnisse. Die Automatisierung von Monitoring-Prozessen für Moorschutzgebiete ermöglicht eine Objektivierung des Analyseverfahrens und stellt eine zeit- und kostengünstige Alternative zur stereoskopischen Bildinterpretation dar. / Moorlands are of worldwide importance for nature and climate protection. They serve as a habitat for a variety of plant and animal species, as well as carbon sinks. Most of the moorlands show significant damage from intense agricultural and silvicultural use, affecting the functionality of many. Currently, several moorland protection programs are running to restore the habitats’ characteristic features, such as that in the Czech-German border region in The Eastern Ore Mountains. Using stereoscopic image interpretation, the moorlands in this region are regularly monitored to observe the influence of executed protection and renaturation measures on the local vegetation structures. However, such manual evaluations require high labor costs. Therefore, the automation of this process is sought. The master thesis at hand presents a procedure enabling the semi-automatic classification of vegetation structures in the moorlands nearby Satzung, Germany. Different grass, tree and soil types can be distinguished and localized using digital aerial imagery and a digital terrain model. For the distinction between different object classes, pixel- and object-based features are taken into consideration. Satellite images acquired by WorldView-2 and Sentinel-2A were also tested for their classification suitability, but without satisfactory results. The automation of monitoring processes for protected moorlands facilitates the externalization of the data analysis and represents a time- and cost-efficient alternative to stereoscopic image interpretations.

As plantações de eucaliptos representam grande potencial econômico para a indústria de papel, celulose, entre outras, além de apresentar uma série de características positivas como alta produtividade, grande potencial de adaptação e ampla diversidade de espécies. Em consequência a tais vantagens, há décadas diversas pesquisas vem sendo realizadas com o intuito de monitorar e detectar diversas doenças que aferem este tipo de cultura. O monitoramento rápido das doenças em eucaliptos torna-se um requisito para evitar grandes perdas econômicas. Neste projeto de pesquisa utilizou-se imagens aéreas obtidas por VANTs (Veículos Aéreos Não-Tripulados) para detectar um tipo específico de estresse que afeta as plantações de eucaliptos: a Murcha de Ceratocyst is. Após rotular eucaliptos doentes e saudáveis e outras estruturas em imagens aéreas, técnicas de Aprendizado de Máquina Supervisionado foram desenvolvidas para generalizar o conhecimento e possibilitar uma rápida detecção através das imagens RGB e multiespectrais. Dentre as técnicas utilizadas, destacou-se a arquitetura de Redes Neurais Convolucional chamada de Custom- CNN, inspirada no modelo da tradicional arquitetura Lenet -5 agregando-se melhorias do estado-da-arte, como a camada convolucional 1x1. Na classificação do conjunto RGB, a Custom-CNN obteve o maior F-score, de 0,81, sendo que a técnica SVM-rbf obteve 0,67. No conjunto de dados com imagens multiespectrais, a Lenet -5 e a Custom-CNN at ingiram, respectivamente, 0,63 e 0,66 de F-score, enquanto o SVM-rbf obteve 0,46. Esta dissertação apresenta a metodologia utilizada para a classificação, elencando as principais características dos algoritmos utilizados, bem como os resultados experimentais obtidos. Há ainda uma aplicação do classificador Regressão Logística para o planejamento de trajetória com VANTs. / Eucalypt us plantations represent great economic potential for t he paper, pulp, among others, in addition to presenting a number of positive characteristics such as high productivity, great potential for adaptaion and wide diversity of species. In consequence of t hese advantages, there are several decades research has been conducted in order to monitor and detect various diseases that affect s this type of culture. The rapid monitoring of diseases in eucalyptus becomes a requirement to avoid major economic losses. In t his research project we used aerial images obtained by UAVs (Unmanned Aerial Vehicles) to detect an specific type of stress t hat a effect s eucalyptus plantations: the Ceratocyst is wilt . After labeling diseased eucalyptus, healthy eucalyptus and other structures in aerial images, Supervised Machine Learning techniques were developed to generalize knowledge and enable rapid detection through RGB and multispectral images. Among the techniques used, stood out t he Convolutional Neural Network architecture called Custom-CNN, that was inspired by the model of t raditional Lenet -5 architecture and with state-of-the-art improvements, such as t he 1x1 convolution layer. In t he classification of RGB dataset , the Custom-CNN obtained the highest F-score of 0.81, and SVM-RBF technique obtained 0.67. In t he dataset with multispectral images, Lenet -5 and Custom-CNN obtained, respectively, 0.63 and 0.66 of F-score, while SVM-rbf obtained 0.46. This paper presents the methodology used for classification, listing the main features of the algorithms and the experimental results. There is also an application of Logistic Regression classifier for path planning with UAVs.

No description available.

A agricultura de precisão tem agregado alto valor para os agricultores por causa das tecnologias que estão ligadas a ela. Sistemas que extraem informações de imagens digitais são extremamente utilizados para que o agricultor tome decisões a fim de aumentar sua produtividade. Uma das técnicas de realizar o monitoramento é a construção de um mosaico de imagens aéreas, onde são utilizadas aeronaves voando em baixa altitude. Esta técnica pode levar dezenas de horas para ser concluída, dependendo da configuração do computador que a executa. Com o intuito de reduzir o tempo nessa construção e tornar possível o embarque a essa aplicação, este trabalho apresenta uma maneira simplificada de construir o mosaico de imagens aéreas baseada na técnica de georreferenciamento direto, no qual utiliza a computação heterogênea para acelerar o desempenho. Essa abordagem é composta por apenas três técnicas que também compõem a abordagem clássica para a construção de mosaicos (warping, extração de características e combinação de características), além de inserir em seus cálculos os dados fornecidos pelos sensores GPS e IMU com a finalidade de direcionar e posicionar cada imagem pertencente ao conjunto que formará o mosaico. A plataforma de computação heterogênea utilizada neste trabalho é a NVIDIA Jetson TK1 escolhida pelo fato de disponibilizar de uma GPU que suporta a linguagem de programação CUDA. Utilizando esta abordagem, a falta de correção da perspectiva do conteúdo (geometria) da imagem gera um resultado inesperado, pois os dados fornecidos pela IMU, ao contrário do que se imagina, apenas servem para corrigir a posição das coordenadas do GPS registradas no momento de captura de cada imagem que compõem o mosaico. O tempo de execução da aplicação desenvolvida é satisfatório tornando possível a adoção desta abordagem. / Accuracy agriculture has added value to farmers thanks to the new technologies that are linked to it. Systems that extract information from digital images are very usefull to help farmers making decisions in order to increase their productivity. One of the techniques to perform this kind of monitoring is the construction of an aerial imagery mosaic where aircrafts flies in low altitude. This technique may take hours to be completed, depending on computer\'s configuration. With the purpose of reducing time in this construction, this thesis presents a simplified way to make aerial imagery mosaic based on direct georeferencing. This approach is composed by three techniques that also make up the classic approach to building mosaics (warping, extraction of characteristics and combination of characteristics), the difference is with this technique here presented is also possible to insert into the calculations the data provided by the GPS and IMU sensors with the purpose of directing and positioning each image to the belonging set to form the mosaic. The heterogeneous computing platform used in this work is the NVIDIA JetsonTK1, this platform was chosen because it offers a GPU that supports the language of CUDA programming. If the images\' geometry errors weren\'t rectfyed, using this approach, an unexpected result happens, because the data provided by IMU, contrary to what is imagined, only serve to correct the position of the GPS coordinates recorded at the moment of capture of each image that composes the mosaic. The developing time in this application is satisfactory making the adoption of this approch favorable.

Maps are important for both human and robot navigation. Given a route, drivingassistance systems consult maps to guide human drivers to their destinations. Similarly, topological maps of a road network provide a robotic vehicle with information about where it can drive and what driving behaviors it should use. By providing the necessary information about the driving environment, maps simplify both manual and autonomous driving. The majority of existing cartographic databases are built, using manual surveys and operator interactions, to primarily assist human navigation. Hence, the resolution of existing maps is insufficient for use in robotics applications. Also, the coverage of these maps fails to extend to places where robotics applications require detailed geometric information. To augment the resolution and coverage of existing maps, this thesis investigates computer vision algorithms to automatically build lane-level detailed maps of highways and parking lots by analyzing publicly available cartographic resources, such as orthoimagery. Our map-building methods recognize image patterns and objects that are tightly coupled with the structure of the underlying road network by 1) identifying, without human intervention, locally consistent image cues and 2) linking them based on the obtained local evidence and prior information about roadways. We demonstrate the accuracy of our bootstrapping approach in building lane-level detailed roadwaymaps through experiments. Due to expected abnormal events on highways such as roadwork, the geometry and traffic rules of highways that appear on maps can occasionally change. This thesis also addresses the problem of updating the resulting maps with temporary changes by analyzing perspective imagery acquired from a vision sensor installed on a vehicle. To robustly recognize highway work zones, our sign recognizer focuses on handling variations of signs’ colors and shapes. Sign recognition errors, which are inevitable, can cause our system to misread temporary highway changes. To handle potential errors, our method utilizes the temporal redundancy of sign occurrences and their corresponding classification decisions. We demonstrate the effectiveness and robustness of our approach highway workzone recognition through testing with video data recorded under various weather conditions. Two major results of this thesis work are 1) algorithms that analyze orthoimages to produce lane-level detailed maps of highways and parking lots and 2) on-vehicle computer vision algorithms that are able to recognize temporary changes on highways. Our maps can provide detailed information about a route, in advance, to either a human driver or a self-driving vehicle. While driving on highways, our roadway-assessing algorithms enable the vehicle to update the resulting maps with temporary changes to the route.

Moore besitzen weltweit eine große Bedeutung für den Natur- und Klimaschutz. Sie dienen als Lebensraum für eine Vielzahl an Pflanzen- und Tierarten sowie als Kohlenstoffsenken. Aufgrund intensiver land- und forstwirtschaftlicher Nutzung weist die Mehrheit der Moorgebiete jedoch hochgradige Schäden auf, durch welche sie in ihrer Funktionalität beeinträchtigt werden. Um die charakteristischen Biotopeigenschaften wiederherzustellen, laufen derzeit zahlreiche Moorschutzprogramme, unter anderem in der deutsch-tschechischen Grenzregion im Osterzgebirge. Damit die Auswirkungen der durchgeführten Schutz- und Renaturierungsmaßnahmen auf die Vegetationsstruktur verfolgt und kontrolliert werden können, erfolgt in dieser Region regelmäßig eine stereoskopische Luftbildinterpretation der Moorflächen. Derartige manuelle Auswertungen sind jedoch mit einem hohen Arbeitsaufwand verbunden, weswegen eine Automatisierung der Prozesse angestrebt wird. In der vorliegenden Arbeit wird ein Verfahren präsentiert, mit welchem die Vegetationsstrukturen der Moore bei Satzung teilautomatisch klassifiziert werden können. Unter Verwendung von digitalen Luftbildern und einem digitalen Geländemodell lassen sich verschiedene Gras-, Baum- und Bodenarten voneinander trennen und lokalisieren. Für die Unterscheidung der einzelnen Klassen werden sowohl pixel- als auch objektbasierte Merkmale in die Datenanalyse einbezogen. Aufnahmen der Satelliten WorldView-2 und Sentinel-2A wurden ebenfalls auf ihr Auswertepotential hin untersucht, allerdings ohne zufriedenstellende Ergebnisse. Die Automatisierung von Monitoring-Prozessen für Moorschutzgebiete ermöglicht eine Objektivierung des Analyseverfahrens und stellt eine zeit- und kostengünstige Alternative zur stereoskopischen Bildinterpretation dar. / Moorlands are of worldwide importance for nature and climate protection. They serve as a habitat for a variety of plant and animal species, as well as carbon sinks. Most of the moorlands show significant damage from intense agricultural and silvicultural use, affecting the functionality of many. Currently, several moorland protection programs are running to restore the habitats’ characteristic features, such as that in the Czech-German border region in The Eastern Ore Mountains. Using stereoscopic image interpretation, the moorlands in this region are regularly monitored to observe the influence of executed protection and renaturation measures on the local vegetation structures. However, such manual evaluations require high labor costs. Therefore, the automation of this process is sought. The master thesis at hand presents a procedure enabling the semi-automatic classification of vegetation structures in the moorlands nearby Satzung, Germany. Different grass, tree and soil types can be distinguished and localized using digital aerial imagery and a digital terrain model. For the distinction between different object classes, pixel- and object-based features are taken into consideration. Satellite images acquired by WorldView-2 and Sentinel-2A were also tested for their classification suitability, but without satisfactory results. The automation of monitoring processes for protected moorlands facilitates the externalization of the data analysis and represents a time- and cost-efficient alternative to stereoscopic image interpretations.

No description available.