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A number of MIR sensing platforms and methods were developed in this research work demonstrating potential applicability of MIR spectroscopy for studying hydrocarbon systems in extreme environments. First of all, the quantitative determination of the diamondoid compound adamantane in organic media utilizing IR-ATR spectroscopy at waveguide surfaces was established. The developed analytical strategy further enabled the successful detection of adamantane in real world crude oil samples. These reported efforts provide a promising outlook for detection and monitoring of diamondoid constituents in naturally occurring crudes and petroleum samples. IR-ATR spectroscopy was further utilized for evaluating and characterizing distribution, variations, and origin of carbonate minerals within sediment formations surrounding a hydrocarbon seep site - MC 118 in the Gulf of Mexico. An analytical model for direct detection of 13C-depleted authigenic carbonates associated with cold seep ecosystems was constructed. Potential applicability of IR-ATR spectroscopy as direct on-ship - and in future in situ - analytical tool for characterizing hydrocarbon seep sites was demonstrated. MIR evanescent field absorption spectroscopy was also utilized to expand the understanding on the role of surfactants during gas hydrate formation at surfaces. This experimental method allowed detailed spectroscopic observations of detergent-related surface processes during SDS mediated gas hydrate formation. The obtained IR data enabled proposing a mechanism by which SDS decreases the induction time for hydrate nucleation, and promotes hydrate formation. Potential of MIR fiberoptic evanescent field spectroscopy for studying surface effects during gas hydrate nucleation and growth was demonstrated. Next, quantifying trace amounts of water content in hexane using MIR evanescent field absorption spectroscopy is presented. The improvement in sensitivity and of limit of detection was obtained by coating an optical fiber with layer of a hydrophilic polymer. The application of the polymer layer has enabled the on-line MIR detection of water in hexane at low ppm levels. These results indicate that the MIR evanescent filed spectroscopy method shows potential for in-situ detection and monitoring of water in industrial oils and petroleum products. Finally, quantification of trace amounts of oil content in water using MIR evanescent field absorption spectroscopy is reported. Unmodified and modified with grafted hydrophobic polymer layer silver halide optical fibers were employed for the measurements. The surface modification of the fiber has enabled the on-line MIR analysis of crude oil in water at the low ppb level. Potential application of MIR fiber-optic evanescent field spectroscopy using polymer modified waveguides toward in-situ low level detection of crude oil in open waters was demonstrated.

[pt] A crescente disponibilidade de dados de sensoriamento remoto vem criando novas oportunidades e desafios em aplicações de monitoramento de processos naturais e antropogénicos em escala global. Nos últimos anos, as técnicas de aprendizado profundo tornaram-se o estado da arte na análise de dados de sensoriamento remoto devido sobretudo à sua capacidade de aprender automaticamente atributos discriminativos a partir de grandes volumes de dados. Um dos problemas chave em análise de imagens é a segmentação semântica, também conhecida como rotulação de pixels. Trata-se de atribuir uma classe a cada sítio de imagem. As chamadas redes totalmente convolucionais de prestam a esta função. Os anos recentes têm testemunhado inúmeras propostas de arquiteturas de redes totalmente convolucionais que têm sido adaptadas para a segmentação de dados de observação da Terra. O presente trabalho avalias cinco arquiteturas de redes totalmente convolucionais que representam o estado da arte em segmentação semântica de imagens de sensoriamento remoto. A avaliação considera dados provenientes de diferentes plataformas: veículos aéreos não tripulados, aeronaves e satélites. Cada um destes dados refere-se a aplicações diferentes: segmentação de espécie arbórea, segmentação de telhados e desmatamento. O desempenho das redes é avaliado experimentalmente em termos de acurácia e da carga computacional associada. O estudo também avalia os benefícios da utilização do Campos Aleatórios Condicionais (CRF) como etapa de pósprocessamento para melhorar a acurácia dos mapas de segmentação. / [en] The increasing availability of remote sensing data has created new opportunities and challenges for monitoring natural and anthropogenic processes on a global scale. In recent years, deep learning techniques have become state of the art in remote sensing data analysis, mainly due to their ability to learn discriminative attributes from large volumes of data automatically. One of the critical problems in image analysis is the semantic segmentation, also known as pixel labeling. It involves assigning a class to each image site. The so-called fully convolutional networks are specifically designed for this task. Recent years have witnessed numerous proposals for fully convolutional network architectures that have been adapted for the segmentation of Earth observation data. The present work evaluates five fully convolutional network architectures that represent the state of the art in semantic segmentation of remote sensing images. The assessment considers data from different platforms: unmanned aerial vehicles, airplanes, and satellites. Three applications are addressed: segmentation of tree species, segmentation of roofs, and deforestation. The performance of the networks is evaluated experimentally in terms of accuracy and the associated computational load. The study also assesses the benefits of using Conditional Random Fields (CRF) as a post-processing step to improve the accuracy of segmentation maps.