Aplicação de técnicas de geoprocessamento para a avaliação da expansão urbana no municípo de Duque de Caxias (RJ) como subsídio ao monitoramento de dutos: trecho da faixa de dutos Rio de Janeiro - Belo Horizonte

Different forms of human pressure may occur in the pipeline ranges, due to the large extensions and various configurations of land use, which can pass through the pipelines. Due to the dynamics of these pressures, it is necessary to monitor temporal changes of land use and cover the surface. Under this theme, appears as extremely important to use products and techniques of remote sensing, as they allow the identification of objects of the land surface that may compromise the security and monitoring of the pipeline, and allows the extraction of information conditions on land use at different periods of time. Based on the above, this paper aims to examine in a temporal approach, the process of urban expansion in the municipality of Duque de Caxias, located on the outskirts of the metropolitan area of the state of Rio de Janeiro, as well as settlement patterns characteristic of areas that the changes occurred in the period 1987 to 2010. We used the technique of visual analysis to perform the change detection and the technique of image classification, aimed at monitoring human pressure over a stretch of track pipeline Rio de Janeiro - Belo Horizonte, located in the state of Rio de Janeiro. The stages of work involved the characterization of the study area, urban sprawl and the existing settlement patterns, through the analysis of bibliographic data. The processing of Landsat 5 images and the application of the technique of change detection were performed in three scenes for the years 1987, 1998 and 2010, while the classification process was performed on the image RapidEye for the year 2010. Can be noted an increase in urban area of approximately 22.38% and the change of land cover from natural to built. This growth is concentrated outside to the area of direct influence of the duct, occurring in the area of indirect influence of the enterprise. Regarding the settlement patterns of growth areas, it was observed that these are predominantly

Classificação de áreas de favelas a partir de imagens IKONOS: Viabilidade de uso de uma abordagem orientada a objeto

The growth of large cities is usually accelerated and disorganized, which causes social, economical and infrastructural conflicts and frequently, occupation in illegal areas. For a better administration of these areas, the public manager needs information about their location. This information can be obtained through land utilization and land cover maps, where orbital images of remote sensing are used as one of the most traditional sources of data. In this context, the present work tested the applicability of the object-based classification to categorize two slum areas, taking into account the structure of the streets, size of the huts, distance between the houses, among other parameters. These area combinations of physical aspects were analyzed using the image IKONOS II and the software eCognition. Slum areas tend to be, to the contrary of the planned areas, disarranged, with narrow streets, small houses built with a variety of materials and without definition of blocks. The results of land cover classification for slum areas are encouraging because they are accurate and little ambiguous in the classification process. Thus, it would allow its utilization by urban managers.

Desenvolvimento de um programa computacional para classificação do uso da terra em imagens CBERS 2

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Método de detecção e classificação de lesões de pele em imagens digitais a partir do modelo Chan-Vese e máquina de vetor de suporte

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classificação de áreas de favelas a partir de imagens ikonos: viabilidade de uso de uma abordagem orientada a objetos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classificação automática de cobertura vegetal em imagens aéreas e orbitais para uso em planejamento energético

Neste trabalho é apresentado um modelo de redes neurais que será utilizado como ferramenta para uso no planejamento energético e na construção de cenários energéticos através da identificação e agrupamento de pixels representativos de classes de água, vegetação e antropização no entorno do reservatório de Tucuruí, Estado do Pará (bacia do rio Tocantins). Para o estudo, foram utilizadas fotografias aéreas ortorretificadas e um recorte da imagem do satélite Landsat, ambos obtidos em agosto de 2001 e classificados utilizando a métrica da mínima distância no software Matlab 7.3.0 (Matrix Laboratory - software de matemática aplicada) e no Arcview 3.2a (programa de Sistemas de Informações Geográficas). Para classificação da área no Matlab, foram utilizadas redes neurais competitivas, mais especificamente as redes de Kohonen que são caracterizadas por realizar um mapeamento de um espaço de dimensão n (número de entradas) para um espaço de dimensão m (número de saídas). Os resultados obtidos no classificador utilizando rede neural e no classificador do Arcview foram semelhantes, mas houve uma divergência no que diz respeito à imagem de alta e média resolução que pode ser justificada pelo fato de que a imagem de alta resolução espacial ocasiona muita variação espectral em algumas feições, gerando dificuldades nas classificações. Esse classificador automático é uma ferramenta importante para identificar oportunidades e potenciais a serem desenvolvidos na construção de cenários energéticos programados. Os resultados deste trabalho confirmam que a imagem de média resolução ainda é a mais indicada para resolver a maioria dos problemas que envolvem identificação de cobertura do solo para utilização em planejamento energético.

Classificação de tábuas de madeira usando processamento de imagens digitais e aprendizado de máquina

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Classificação da cobertura da terra da RPPN Foz do Rio Aguapeí: abordagem booleana para a integração de imagens TM-Landsat e dados multifonte

The Companhia Energetica de Sao Paulo - CESP owns six hydroelectric dams in the state of São Paulo. The dams, both in its construction and in operation, cause some environmental impacts, most of them negatives, for example, the flooding in regions before not flooded, deviation of the river’s course, among others, bringing harm to flora and fauna of these environments. As a way to compensating these damages, the CESP has acquired a region that was influenced by Sérgio Motta Hydroelectric Plant Engineer, or Porto Primavera, and turned it into Reserva Particular do Patrimônio Natural Foz do Rio Aguapeí. By law it fits in a Conservation Unit, and thus should be contemplate for a management plan, ie, a multidisciplinary technical document which allows, simply, the practice of actions within and around in a sustainably way. This work aimed at developing a land cover map of the reserve for this plan can be made and executed more efficiently. Initially, the project included field visits and meetings with members of the CESP to be specified classes contained on the map. Later, we ran different types of classifications of multispectral images (TM / Landsat 5)... (Complete abstract click electronic access below)

Avaliação do método de classificação baseada no objeto em imagens de alta resolução espacial aplicado para o monitoramento de faixa de dutos: estudo de caso no entorno da refinaria de Capuava em Mauá - SP

The metropolitan region of São Paulo is the most populous of the country, this happens because of its great importance in the national economy and the job opportunities that are offered to the population. These factors result in intense population growth and urban expansion, reaching some non-habitable places of the metropolis, as areas of pipelines, which are very important for the transportation of natural gas, oil and its derivatives. Before the population growth of the region, these sites were unoccupied, do not presenting problems for the population. However, with the disorderly occupation is generated great anthropogenic pressure on the pipeline stitches, causing risks to people who are around them. Therefore it is extremely important to monitor the strip of pipelines through products and techniques of remote sensing and geoprocessing, enabling, through high spatial resolution images, identification of objects or phenomena that occur on Earth's surface that can alter the functioning and safety of pipelines. Therefore, this study aims to monitor a stretch of the area of the pipeline mesh GASPAL/OSVAT and Capuava Refinery (RECAP), located on the outskirts of the metropolitan area of São Paulo in the city of Mauá, who suffer great human pressure, proving thus the techniques of remote sensing and geographic information system (GIS) as effective tools for monitoring phenomena occurred in urban areas of great complexity. The monitoring was done by object-based classification applied in orbital images Ikonos II and RapidEye, of high spatial resolution and, image processing, detection of objects, segmentation, classification and editing were developed through the eCognition and ArcGis softwares. To determine the statistical accuracy of the mapping of the land cover of the stretch of pipeline in Maua, the results were analyzed by error matrix... (Complete abstract click electronic access below)

Discriminação de classes de cobertura vegetal utilizando técnicas de classificação digital de imagens de sensoriamento remoto

The aim of this work is to discriminate vegetation classes throught remote sensing images from the satellite CBERS-2, related to winter and summer seasons in the Campos Gerais region Paraná State, Brazil. The vegetation cover of the region presents different kinds of vegetations: summer and winter cultures, reforestation areas, natural areas and pasture. Supervised classification techniques like Maximum Likelihood Classifier (MLC) and Decision Tree were evaluated, considering a set of attributes from images, composed by bands of the CCD sensor (1, 2, 3, 4), vegetation indices (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), mixture models (soil, shadow, vegetation) and the two first main components. The evaluation of the classifications accuracy was made using the classification error matrix and the kappa coefficient. It was defined a high discriminatory level during the classes definition, in order to allow separation of different kinds of winter and summer crops. The classification accuracy by decision tree was 94.5% and the kappa coefficient was 0.9389 for the scene 157/128. For the scene 158/127, the values were 88% and 0.8667, respectively. The classification accuracy by MLC was 84.86% and the kappa coefficient was 0.8099 for the scene 157/128. For the scene 158/127, the values were 77.90% and 0.7476, respectively. The results showed a better performance of the Decision Tree classifier than MLC, especially to the classes related to cultivated crops, indicating the use of the Decision Tree classifier to the vegetation cover mapping including different kinds of crops.

Análise do descritor de padrões mapeados localmente em multiescala para classificação de textura em imagens digitais

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Classificação das amostras do ensaio de Baumann através do processamento digital de imagens.

O presente trabalho apresenta uma alternativa ao processo de classificação do defeito da segregação central em amostras de aço, utilizando as imagens digitais que são geradas durante o ensaio de Baumann. O algoritmo proposto tem como objetivo agregar as técnicas de processamento digital de imagens e o conhecimento dos especialistas sobre o defeito da segregação central, visando a classificação do defeito de referência. O algoritmo implementado inclui a identificação e a segmentação da linha segregada por meio da aplicação da transformada de Hough e limiar adaptativo. Adicionalmente, o algoritmo apresenta uma proposta para o mapeamento dos atributos da segregação central nos diferentes graus de severidade do defeito, em função dos critérios de continuidade e intensidade. O mapeamento foi realizado por meio da análise das características individuais, como comprimento, largura e área, dos elementos segmentados que compõem a linha segregada. A avaliação do desempenho do algoritmo foi realizada em dois momentos específicos, de acordo com sua fase de implementação. Para a realização da avaliação, foram analisadas 255 imagens de amostras reais, oriundas de duas usinas siderúrgicas, distribuídas nos diferentes graus de severidade. Os resultados da primeira fase de implementação mostram que a identificação da linha segregada apresenta acurácia de 93%. As classificações oriundas do mapeamento realizado para as classes de criticidade do defeito, na segunda fase de implementação, apresentam acurácia de 92% para o critério de continuidade e 68% para o critério de intensidade.

Técnica para segmentação automática de imagens microscópicas de componentes sanguíneos e classificação diferencial de leucócitos baseada em lógica fuzzy

Automatic detection of blood components is an important topic in the field of hematology. The segmentation is an important stage because it allows components to be grouped into common areas and processed separately and leukocyte differential classification enables them to be analyzed separately. With the auto-segmentation and differential classification, this work is contributing to the analysis process of blood components by providing tools that reduce the manual labor and increasing its accuracy and efficiency. Using techniques of digital image processing associated with a generic and automatic fuzzy approach, this work proposes two Fuzzy Inference Systems, defined as I and II, for autosegmentation of blood components and leukocyte differential classification, respectively, in microscopic images smears. Using the Fuzzy Inference System I, the proposed technique performs the segmentation of the image in four regions: the leukocyte’s nucleus and cytoplasm, erythrocyte and plasma area and using the Fuzzy Inference System II and the segmented leukocyte (nucleus and cytoplasm) classify them differentially in five types: basophils, eosinophils, lymphocytes, monocytes and neutrophils. Were used for testing 530 images containing microscopic samples of blood smears with different methods. The images were processed and its accuracy indices and Gold Standards were calculated and compared with the manual results and other results found at literature for the same problems. Regarding segmentation, a technique developed showed percentages of accuracy of 97.31% for leukocytes, 95.39% to erythrocytes and 95.06% for blood plasma. As for the differential classification, the percentage varied between 92.98% and 98.39% for the different leukocyte types. In addition to promoting auto-segmentation and differential classification, the proposed technique also contributes to the definition of new descriptors and the construction of an image database using various processes hematological staining

Classificação de nódulos pulmonares em imagens tomográficas utilizando redes neurais artificiais em cascata

Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.

Processamento digital de imagens orbitais para obtenção de classificação do uso da terra da indicação geográfica vitivinícola Campanha, Brasil.

O Projeto Indicação de Procedência Campanha coordenado pela Embrapa Uva e Vinho é um estudo multidisciplinar cujo foco é a caracterização da área da indicação geográfica vitivinícola, limitada a oeste pela Argentina, a sul-sudoeste pelo Uruguai, abrangendo grande parte da ?Metade Sul? do Estado do Rio Grande do Sul. A viticultura ocorre em polos produtores sob condições de uso da terra diversos e distantes entre si dentro da região. Então, foram definidos nove setores de ocorrência de vinhedos, onde foi testado o método de classificação digital de imagem (PDI). A escolha da setorização para emprego de PDI se baseia na premissa de que quanto menor a região melhor seria a identificação das classes de uso por uma imagem de satélite com melhor resolução possível, propiciando qualidade maior de classificação.