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This thesis is devoted to the detectability of an inhomogeneous region possibly embedded in a noisy environment. It presents models and algorithms using the theory of the longest significant run and percolation. We analyze the computational results based on simulation. We consider the length of the significant nodes in a chain with good continuation in a square lattice of independent nodes. Inspired by the percolation theory, we first analyze the problem in a tree based model. We give the critical probability and find the decay rate of the probability of having a significant run with length k starting at the origin. We find that the asymptotic rate of the length of the significant run can be powerfully applied in the area of image detection. Examples are detection of filamentary structures in a background of uniform random points and target tracking problems. We set the threshold for the rejection region in these problems so that the false positives diminish quickly as we have more samples. Inspired by the convex set detection, we also give a fast and near optimal algorithm to detect a possibly inhomogeneous chain with good continuation in an image of pixels with white noise. We analyze the length of the longest significant chain after thresholding each pixel and consider the statistics over all significant chains. Such a strategy significantly reduces the complexity of the algorithm. The false positives are eliminated as the number of pixels increases. This extends the existing detection method related to the detection of inhomogeneous line segment in the literature.

Unlike MSS LANDSAT imagery and other photography, the specific characteristics of the intensity of water and shadow in an SAR image make the task of discriminating them extremely difficult. In this thesis, we analyze the reflectivity mechanism of water and shadow on radar imagery and describe a scene analysis system which consists of a texture preserving noise removal procedure as the preprocessing step, a probabilistic relaxation algorithm to do the low level labeling and a spatial reasoning procedure based on a relational model to perform the high level interpretation. The experimental results obtained from the SAR images are presented to illustrate the performance of this system. / M.S.

Orientador: Rubens Augusto Camargo Lamparelli / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola / Made available in DSpace on 2018-08-20T12:04:36Z (GMT). No. of bitstreams: 1 Picoli_MichelleCristinaAraujo_D.pdf: 3300395 bytes, checksum: 8977f82fac78de53ad03a7e436b10bc1 (MD5) Previous issue date: 2012 / Resumo:A cana-de-açúcar (Saccharum spp.) exerce papel fundamental na economia na medida em que fornece matéria prima para a produção das comodities açúcar e álcool. Dentro deste contexto a necessidade de informações, as quais possibilitam o acompanhamento desta, é extremamente importante porque permite avaliar a disponibilidade da matéria prima e sua demanda. Para isto, é necessário o monitoramento constante da cultura e uma das formas de se fazer é com o uso de imagens de sensoriamento remoto. Porém, sabe-se que nem sempre isso é possível devido aos períodos chuvosos que ocorrem durante a safra e, portanto, dificultando a aquisição de imagens de satélite livre de nuvens. Para tentar minimizar este problema, a utilização de sensores ativos, como é o caso dos sensores radar de abertura sintética (SAR - Synthetic Aperture Radar), pode ajudar. Os sensores SAR podem coletar dados da superfície em áreas com freqüente cobertura de nuvens e névoa, operando inclusive à noite. Sendo assim, este trabalho teve como objetivo: analisar o potencial das imagens de radar PALSAR/ALOS, polarizações HH e HV, no monitoramento da cultura de cana-de-açúcar, determinar os efeitos das linhas de plantio em culturas de cana-de-açúcar nessas imagens de radar e estimar a produtividade agrícola em talhões de cana-de-açúcar, por meio de um modelo utilizando imagens de radar do sensor PALSAR/ALOS e dados agronômicos, nas safras: 2007/08, 2008/09, 2009/10 na região nordeste do Estado de São Paulo. Os resultados mostraram que foi capaz de detectar mudanças na estrutura (biomassa) da cana-de-açúcar. Os valores médios do coeficiente de retroespalhamento (?°), provenientes de talhões, com linhas de plantio perpendiculares, na polarização HH, foram mais altos que os dos talhões com linhas paralelas à linha de visada do sensor (0,7 a 2,3 dB mais altos, 1,2 dB em média). Para a polarização HV não houve diferença estatisticamente significativa. Já os modelos de estimativa de produtividade explicaram 79%, 12% e 74% da variação da produtividade agrícola observada nos talhões nos anos safra 2007/08, 2008/09 e 2009/10, respectivamente, e o modelo considerando as três safras juntas explicou 66%. Esses resultados podem ser usados para auxiliar e aprimorar a estimativa da produtividade feita in loco, e, principalmente, proporcionar uma alternativa ao monitoramento da cultura sob quaisquer condições climáticas / Abstract: The sugarcane (Saccharum spp.) plays important role in economy because it is source of raw material for sugar and alcohol production. In this context the information needs, which enables its monitoring is very important due to allows evaluate the availability of raw material and its demands. For this, it is need a crop systematical monitoring and one of the ways to do this is through the use of remote sensing images. However, it known that it is not always possible due to the rainy season that occurs in certain periods during the crop phenological phases, impeding the acquisition of optics satellite images free of clouds. To get around this problem the use of active sensors, such as sensors of synthetic aperture radar (SAR) can help. SAR sensors can collect data in areas with frequent cloud cover and fog and can operate even at night. Therefore, this study aimed to analyze the potential of radar PALSAR/ALOS, in HH and HV polarizations, for monitoring the sugarcane crop, to determine the effects of planting rows on the backscatter and estimate sugarcane yield by means of model using radar sensor PALSAR/ALOS and agronomic data, in crop seasons: 2007/08, 2008/09, 2009/10 in northeastern region of São Paulo. The results are promising because it was able to show changing in biomass enabling the monitoring. The mean values of the backscattering coefficient (?°) from fields with perpendicular rows, in HH polarization, were higher than those from parallel rows to the range direction of the satellite (0.7 to 2.3 dB over high, 1.2 dB on average). For HV polarization, there was no statistically significant difference. The yield model estimation, explained 79%, 12% and 74% of the variation in final yield, observed in the plots, in crop seasons 2007/08, 2008/09 and 2009/10, respectively, and the model built for the three crop seasons together explained 66% . These results can be used to assist and improve the prediction of yield performed in loco, and mainly to afford an alternative for crop monitoring in some weather conditions / Doutorado / Planejamento e Desenvolvimento Rural Sustentável / Doutora em Engenharia Agrícola