Hyperspectral Analysis of Oil and Oil-Impacted Soils for Remote Sensing Purposes

While conventional multispectral sensors record the radiometric signal only at a handful of wavelengths, hyperspectral sensors measure the reflected solar signal at hundreds contiguous and narrow wavelength bands, spanning from the visible to the infrared. Hyperspectral images provide ample spectral information to identify and distinguish between spectrally similar (but unique) materials, providing the ability to make proper distinctions among materials with only subtle signature differences. Hyperspectral images show hence potentiality for proper discrimination between oil slicks and other natural phenomena (look-alike); and even for proper distinctions between oil types. Additionally they can give indications on oil volume. At present, many airborne hyperspectral sensors are available to collect data, but only two civil spaceborn hyperspectral sensors exist as technology demonstrator: the Hyperion sensor on NASA’s EO-1 satellite and the CHRIS sensor on the European Space Agency’s PROBA satellite. Consequently, the concrete opportunity to use spaceborn hyperspectral remote sensing for operational oil spill monitoring is yet not available. Nevertheless, it is clear that the future of satellite hyperspectral remote sensing of oil pollution in the marine/coastal environment is very promising. In order to correctly interpret the hyperspectral data, the retrieved spectral signatures must be correlated to specific materials. Therefore specific spectral libraries, containing the spectral signature of the materials to be detected, must be built up. This requires that highly accurate reflected light measurements of samples of the investigated material must be performed in the lab or in the field. Accurate measurements of the spectral reflectance of several samples of oil-contaminated soils have been performed in the laboratory, in the 400-2500 nm wavelength range. Samples of the oils spilt from the Erika and the Prestige tankers during the major accidents of 1999 and 2002 were also collected and analyzed in the same spectral range, using a portable spectrophotometer. All measurements showed the typical absorption features of hydrocarbon-bearing substances: the two absorption peaks centered at 1732 and 2310 nm.JRC.G.3-Agricultur

Hydrocarbon quantification using neural networks and deep learning based hyperspectral unmixing

Hydrocarbon (HC) spills are a global issue, which can seriously impact human life and the environment, therefore early identification and remedial measures taken at an early stage are important. Thus, current research efforts aim at remotely quantifying incipient quantities of HC mixed with soils. The increased spectral and spatial resolution of hyperspectral sensors has opened ground-breaking perspectives in many industries including remote inspection of large areas and the environment. The use of subpixel detection algorithms, and in particular the use of the mixture models, has been identified as a future advance that needs to be incorporated in remote sensing. However, there are some challenging tasks since the spectral signatures of the targets of interest may not be immediately available. Moreover, real time processing and analysis is required to support fast decision-making. Progressing in this direction, this thesis pioneers and researches novel methodologies for HC quantification capable of exceeding the limitations of existing systems in terms of reduced cost and processing time with improved accuracy. Therefore the goal of this research is to develop, implement and test different methods for improving HC detection and quantification using spectral unmixing and machine learning. An efficient hybrid switch method employing neural networks and hyperspectral is proposed and investigated. This robust method switches between state of the art hyperspectral unmixing linear and nonlinear models, respectively. This procedure is well suited for the quantification of small quantities of substances within a pixel with high accuracy as the most appropriate model is employed. Central to the proposed approach is a novel method for extracting parameters to characterise the non-linearity of the data. These parameters are fed into a feedforward neural network which decides in a pixel by pixel fashion which model is more suitable. The quantification process is fully automated by applying further classification techniques to the acquired hyperspectral images. A deep learning neural network model is designed for the quantification of HC quantities mixed with soils. A three-term backpropagation algorithm with dropout is proposed to avoid overfitting and reduce the computational complexity of the model. The above methods have been evaluated using classical repository datasets from the literature and a laboratory controlled dataset. For that, an experimental procedure has been designed to produce a labelled dataset. The data was obtained by mixing and homogenizing different soil types with HC substances, respectively and measuring the reflectance with a hyperspectral sensor. Findings from the research study reveal that the two proposed models have high performance, they are suitable for the detection and quantification of HC mixed with soils, and surpass existing methods. Improvements in sensitivity, accuracy, computational time are achieved. Thus, the proposed approaches can be used to detect HC spills at an early stage in order to mitigate significant pollution from the spill areas

Caracterização e estudo comparativo de exsudações de hidrocarbonetos e plays petrolíferos em bacias terrestres das regiões central do Irã e sudeste do Brasil usando sensoriamento remoto espectral

Orientador: Carlos Roberto de Souza FilhoTese (doutorado) - Universidade Estadual de Campinas, Instituto de GeociênciasResumo: O objetivo desta pesquisa foi explorar as assinaturas de exsudações de hidrocarbonetos na superfície usando a tecnologia de detecção remota espectral. Isso foi alcançado primeiro, realizando uma revisão abrangente das capacidades e potenciais técnicas de detecção direta e indireta. Em seguida, a técnica foi aplicada para investigar dois locais de teste localizados no Irã e no Brasil, conhecidos por hospedar sistemas ativos de micro-exsudações e afloramentos betuminosos, respectivamente. A primeira área de estudo está localizada perto da cidade de Qom (Irã), e está inserida no campo petrolífero Alborz, enterrado sob sedimentos datados do Oligoceno da Formação Upper Red. O segundo local está localizado perto da cidade de Anhembi (SP), na margem oriental da bacia do Paraná, no Brasil, e inclui acumulações de betume em arenitos triássicos da Formação Pirambóia. O trabalho na área de Qom integrou evidências de (i) estudos petrográficos e geoquímicos em laboratório, (ii) investigações de afloramentos em campo, e (iii) mapeamento de anomalia em larga escala através de conjuntos de dados multi-espectrais ASTER e Sentinel-2. O resultado deste estudo se trata de novos indicadores mineralógicos e geoquímicos para a exploração de micro-exsudações e um modelo de micro-exsudações atualizado. Durante este trabalho, conseguimos desenvolver novas metodologias para análise de dados espectroscópicos. Através da utilização de dados simulados, indicamos que o instrumento de satélite WorldView-3 tem potencial para detecção direta de hidrocarbonetos. Na sequência do estudo, dados reais sobre afloramentos de arenitos e óleo na área de Anhembi foram investigados. A área foi fotografada novamente no chão e usando o sistema de imagem hiperespectral AisaFENIX. Seguiu-se estudos e amostragem no campo,incluindo espectroscopia de alcance fechado das amostras no laboratório usando instrumentos de imagem (ou seja, sisuCHEMA) e não-imagem (ou seja, FieldSpec-4). O estudo demonstrou que uma abordagem espectroscópica multi-escala poderia fornecer uma imagem completa das variações no conteúdo e composição do betume e minerais de alteração que acompanham. A assinatura de hidrocarbonetos, especialmente a centrada em 2300 nm, mostrou-se consistente e comparável entre as escalas e capaz de estimar o teor de betume de areias de petróleo em todas as escalas de imagemAbstract: The objective of this research was to explore for the signatures of seeping hydrocarbons on the surface using spectral remote sensing technology. It was achieved firstly by conducting a comprehensive review of the capacities and potentials of the technique for direct and indirect seepage detection. Next, the technique was applied to investigate two distinctive test sites located in Iran and Brazil known to retain active microseepage systems and bituminous outcrops, respectively. The first study area is located near the city of Qom in Iran, and consists of Alborz oilfield buried under Oligocene sediments of the Upper-Red Formation. The second site is located near the town of Anhembi on the eastern edge of the Paraná Basin in Brazil and includes bitumen accumulations in the Triassic sandstones of the Pirambóia Formation. Our work in Qom area integrated evidence from (i) petrographic, spectroscopic, and geochemical studies in the laboratory, (ii) outcrop investigations in the field, and (iii) broad-scale anomaly mapping via orbital remote sensing data. The outcomes of this study was novel mineralogical and geochemical indicators for microseepage characterization and a classification scheme for the microseepage-induced alterations. Our study indicated that active microseepage systems occur in large parts of the lithofacies in Qom area, implying that the extent of the petroleum reservoir is much larger than previously thought. During this work, we also developed new methodologies for spectroscopic data analysis and processing. On the other side, by using simulated data, we indicated that WorldView-3 satellite instrument has the potential for direct hydrocarbon detection. Following this demonstration, real datasets were acquired over oil-sand outcrops of the Anhembi area. The area was further imaged on the ground and from the air by using an AisaFENIX hyperspectral imaging system. This was followed by outcrop studies and sampling in the field and close-range spectroscopy in the laboratory using both imaging (i.e. sisuCHEMA) and nonimaging instruments. The study demonstrated that a multi-scale spectroscopic approach could provide a complete picture of the variations in the content and composition of bitumen and associated alteration mineralogy. The oil signature, especially the one centered at 2300 nm, was shown to be consistent and comparable among scales, and capable of estimating the bitumen content of oil-sands at all imaging scalesDoutoradoGeologia e Recursos NaturaisDoutor em Geociências2015/06663-7FAPES

Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives

Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector

Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Hyperspectral remote sensing images provide rich spectral information which is beneficial for the monitoring of oil spills in complex ocean scenarios. However, most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs), which require a huge amount of effort to annotate a certain number of high-quality training sets. In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest for HSIs. First, considering that the noise level varies among different bands, a noise variance estimation method is exploited to evaluate the noise level of different bands, and the bands corrupted by severe noise are removed. Second, kernel principal component analysis (KPCA) is employed to reduce the high dimensionality of the HSIs. Then, the probability of each pixel belonging to one of the classes of seawater and oil spills is estimated with the isolation forest, and a set of pseudo-labeled training samples is automatically produced using the clustering algorithm on the detected probability. Finally, an initial detection map can be obtained by performing the support vector machine (SVM) on the dimension-reduced data, and then, the initial detection result is further optimized with the extended random walker (ERW) model so as to improve the detection accuracy of oil spills. Experiments on airborne hyperspectral oil spill data (HOSD) created by ourselves demonstrate that the proposed method obtains superior detection performance with respect to other state-of-the-art detection approaches

Oil Spill Detection and Mapping Along the Gulf of Mexico Coastline Based on Imaging Spectrometer Data

The Deepwater Horizon oil spill in the Gulf of Mexico between April and July 2010 demonstrated the importance of synoptic oil-spill monitoring in coastal environments via remote-sensing methods. This study focuses on terrestrial oil-spill detection based on hyperspectral images acquired along the coastline of the Gulf of Mexico. A number of AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) imaging spectrometer images were investigated in this research collected over Bay Jimmy and Wilkinson Bay within Barataria Bay, Louisiana, USA during September 2010. Various remote-sensing image processing techniques were employed to detect/identify oiled vegetation. Image-derived endmembers were extracted from the atmospherically- and geometrically-corrected hyperspectral AVIRIS data via Minimum Noise Fraction transform, Pixel Purity Index generation, and n-dimensional visualization. Extracted endmembers are then used as input to endmember-mapping algorithms Spectral Information Divergence (SID) and Mixture Tuned Matched Filtering (MTMF) to yield fractional-abundance images and crisp classification images. Field based observations of the degree of oil accumulation along the coastline were also employed, as well as in situ measurements from the literature. Multiple Endmember Spectral Mixture Analysis (MESMA) was employed for oiled-vegetation detection and mapping in order to enable the number and types of endmembers to vary on a per-pixel basis, in contrast to simple Spectral Mixture Analysis (SMA). MESMA thus better allows accounting for spectral variability of oil (e.g., due to varying oil thicknesses, states of degradation, and the presence of different oil types, etc.) and other materials, including soils and salt marsh vegetation of varying types, which may or may not be affected by the oil spill. The classification results demonstrated that MESMA provides advantageous capabilities for mapping several oiled-vegetation classes along the Gulf of Mexico coastline, relative to the conventional approaches tested

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists

Zrakoplovni hiperspektralni nadzor uljnog onečišćenja s brodova u hrvatskom dijelu Jadranskog mora

The airborne hyperspectral and multisensor surveillance of the ship sourced oil pollution of the sea was researched by the airborne system developed in the frame of the project „System for the multisensor airborne reconnaissance and surveillance in crisis situations and the protection of the environment“ (MZOS 2007). While different methodologies, methods, technologies and techniques were used, the multilevel fusion was applied for linking the data, the processes and the outcomes. Fusion includes the aerial hyperspectral and the colour imagery, the visually detected oil spills, the formalised knowledge for the estimation of the oil spill area and oil’s quantity based on Bonn Agreement Oil Appearance Code – BAOAC, the data about the spectral response of the clean sea and polluted sea, the results of hyperspectral classification. Besides the information acquired by the airborne multisensor system, the information provided by space based system CleanSeaNet of the European Maritime Safety Agency – EMSA was included in the fusion process (in the frame of the large trial – operational exercise in 2008). The advantages of the airborne remote sensing of the oil spills are reliable detection of the oil spills, accurate mapping of its position and the shape in geographic coordinates, classification of the contents of the spill, measurements of the oil spill’s features, estimation of the oil quantity.Istražen je hiperspektralni i multisenzorski nadzor uljnog onečišćenja mora s brodova pomoću zrakoplovnog sustava razvijenog u okviru projekta “Sustav za multisenzorsko zrakoplovno izviđanje i nadzor u kriznim situacijama i zaštiti okoliša” (MZOS 2007). Budući da su korištene različite metodologije, metode, tehnologije i tehnike, primijenjena je višerazinska fuzija za povezivanje podataka, procesa i njihovih izlaza. Fuzija uključuje zrakoplovne hiperspektralne i kolor snimke, vizualno detektiranu uljnu mrlju, formalizirano znanje za procjenu površine uljne mrlje i količine ulja, na temelju koda iz sporazuma iz Bonna o pojavnosti ulja (BAOAC), podatke o spektralnom odzivu čiste i uljem onečišćene morske površine, rezultate hiperspektralne klasifikacije. Osim informacija prikupljenih zrakoplovnim multisenzorskim sustavom, u proces fuzije bile su uključene informacije dobivene od svemirskog sustava CleanSeaNet Europske agencije za pomorsku sigurnost – EMSA (u okviru velike pokusne aktivnosti – operativne vježbe u 2008.). Prednosti zrakoplovnih daljinskih istraživanja uljnih mrlja su pouzdana detekcija uljnih mrlja, precizno definiranje njezinog položaja i oblika u geografskim koordinatama, klasifikacija sadržaja uljne mrlje, mjerenje njezinih obilježja, procjenu količine ulja

Tree species mapping around reclaimed oil and gas wells sites using hyperspectral and Light Detection and Ranging (LiDAR) remote sensing

Oil and gas activities in Alberta require disturbing forested lands, among other ecosystems, in order to extract resources. Due to the number of oil and gas sites requiring reclamation, monitoring can be problematic. Remote sensing provides cost-effective, timely, and repeatable data of these areas in support of monitoring efforts. Support Vector Machine (SVM) and Multiple Endmember Spectral Mixture Analysis (MESMA) were tested in order to identify tree species around reclaimed and abandoned well sites near Cold Lake, Alberta using CHRIS satellite imagery with and without airborne LiDAR data. A hierarchical classification approach was employed, which achieved an accuracy of 83.4 % when using SVM together with CHRIS imagery and LiDAR. This positive result indicates the ability of remote sensing to support reclamation management and monitoring objectives within Alberta’s forested areas.Natural Science and Engineering Research Council of Canada (NSERC) CREATE scholarship (Advanced Methods, Education and Training in Hyperspectral Science and Technology; AMETHYST). Alberta Terrestrial Imaging Centre (ATIC). TECTERRA. Oil Sands Research and Information Network (OSRIN). Alberta Environment and Sustainable Resource Development (ESRD

Multi-scale Adaptive Fusion Network for Hyperspectral Image Denoising

Removing the noise and improving the visual quality of hyperspectral images (HSIs) is challenging in academia and industry. Great efforts have been made to leverage local, global or spectral context information for HSI denoising. However, existing methods still have limitations in feature interaction exploitation among multiple scales and rich spectral structure preservation. In view of this, we propose a novel solution to investigate the HSI denoising using a Multi-scale Adaptive Fusion Network (MAFNet), which can learn the complex nonlinear mapping between clean and noisy HSI. Two key components contribute to improving the hyperspectral image denoising: A progressively multiscale information aggregation network and a co-attention fusion module. Specifically, we first generate a set of multiscale images and feed them into a coarse-fusion network to exploit the contextual texture correlation. Thereafter, a fine fusion network is followed to exchange the information across the parallel multiscale subnetworks. Furthermore, we design a co-attention fusion module to adaptively emphasize informative features from different scales, and thereby enhance the discriminative learning capability for denoising. Extensive experiments on synthetic and real HSI datasets demonstrate that the proposed MAFNet has achieved better denoising performance than other state-of-the-art techniques. Our codes are available at \verb'https://github.com/summitgao/MAFNet'.Comment: IEEE JSTASRS 2023, code at: https://github.com/summitgao/MAFNe