DEVELOPING INNOVATIVE SPECTRAL AND MACHINE LEARNING METHODS FOR MINERAL AND LITHOLOGICAL CLASSIFICATION USING MULTI-SENSOR DATASETS

The sustainable exploration of mineral resources plays a significant role in the economic development of any nation. The lithological maps and surface mineral distribution can be vital baseline data to narrow down the geochemical and geophysical analysis potential areas. This study developed innovative spectral and Machine Learning (ML) methods for mineral and lithological classification. Multi-sensor datasets such as Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and Digital Elevation Model (DEM) were utilized. The study mapped the hydrothermal alteration minerals derived from Spectral Mapping Methods (SMMs), including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and SIDSAMtan using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski area (India). The SIDSAMtan outperforms SID and SAM in mineral mapping. A spectral similarity matrix of target and non-target classes based optimum threshold selection was developed to implement the SMMs successfully. Three new effective SMMs such as Dice Spectral Similarity Coefficient (DSSC), Kumar-Johnson Spectral Similarity Coefficient (KJSSC), and their hybrid, i.e., KJDSSCtan has been proposed, which outperforms the existing SMMs (i.e., SAM, SID, and SIDSAMtan) in spectral discrimination of spectrally similar minerals. The developed optimum threshold selection and proposed SMMs are recommended for accurate mineral mapping using hyperspectral data. An integrated spectral enhancement and ML methods have been developed to perform automated lithological classification using AVIRIS-NG hyperspectral data. The Support Vector Machine (SVM) outperforms the Random Forest (RF) and Linear Discriminant Analysis (LDA) in lithological classification. The performance of SVM also shows the least sensitivity to the number and uncertainty of training datasets. This study proposed a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks for accurate lithological classification using ML models. Different input features, such as (a) spectral, (b) spectral and transformed spectral, (c) spectral and morphological, (d) spectral and textural, and (e) optimum hybrid, were evaluated for lithological classification. The developed approach has been assessed in the Chattarpur area (India) consists of similar spectral characteristics and poorly exposed rocks, weathered, and partially vegetated terrain. The optimal hybrid input features outperform other input features to accurately classify different rock types using the SVM and RF models, which is ~15% higher than as obtained using spectral input features alone. The developed integrated approach of spectral enhancement and ML algorithms, and a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks, are recommended for accurate lithological classification. The developed methods can be effectively utilized in other remote sensing applications, such as vegetation/forest mapping and soil classification

Snapshot hyperspectral imaging : near-infrared image replicating imaging spectrometer and achromatisation of Wollaston prisms

Conventional hyperspectral imaging (HSI) techniques are time-sequential and rely on temporal scanning to capture hyperspectral images. This temporal constraint can limit the application of HSI to static scenes and platforms, where transient and dynamic events are not expected during data capture. The Near-Infrared Image Replicating Imaging Spectrometer (N-IRIS) sensor described in this thesis enables snapshot HSI in the short-wave infrared (SWIR), without the requirement for scanning and operates without rejection in polarised light. It operates in eight wavebands from 1.1μm to 1.7μm with a 2.0° diagonal field-of-view. N-IRIS produces spectral images directly, without the need for prior topographic or image reconstruction. Additional benefits include compactness, robustness, static operation, lower processing overheads, higher signal-to-noise ratio and higher optical throughput with respect to other HSI snapshot sensors generally. This thesis covers the IRIS design process from theoretical concepts to quantitative modelling, culminating in the N-IRIS prototype designed for SWIR imaging. This effort formed the logical step in advancing from peer efforts, which focussed upon the visible wavelengths. After acceptance testing to verify optical parameters, empirical laboratory trials were carried out. This testing focussed on discriminating between common materials within a controlled environment as proof-of-concept. Significance tests were used to provide an initial test of N-IRIS capability in distinguishing materials with respect to using a conventional SWIR broadband sensor. Motivated by the design and assembly of a cost-effective visible IRIS, an innovative solution was developed for the problem of chromatic variation in the splitting angle (CVSA) of Wollaston prisms. CVSA introduces spectral blurring of images. Analytical theory is presented and is illustrated with an example N-IRIS application where a sixfold reduction in dispersion is achieved for wavelengths in the region 400nm to 1.7μm, although the principle is applicable from ultraviolet to thermal-IR wavelengths. Experimental proof of concept is demonstrated and the spectral smearing of an achromatised N-IRIS is shown to be reduced by an order of magnitude. These achromatised prisms can provide benefits to areas beyond hyperspectral imaging, such as microscopy, laser pulse control and spectrometry

Development and Characterization of a Chromotomosynthetic Hyperspectral Imaging System

A chromotomosynthetic imaging (CTI) methodology based upon mathematical reconstruction of a set of 2-D spectral projections to collect high-speed (100 Hz) 3-D hyperspectral data cube has been proposed. The CTI system can simultaneously provide usable 3-D spatial and spectral information, provide high-frame rate slitless 1-D spectra, and generate 2-D imagery equivalent to that collected with no prism in the optical system. The wavelength region where prism dispersion is highest (500 nm) is most sensitive to loss of spectral resolution in the presence of systematic error, while wavelengths 600 nm suffer mostly from a shift of the spectral peaks. The quality of the spectral resolution in the reconstructed hyperspectral imagery was degraded by as much as a factor of two in the blue spectral region with less than 1° total angular error in mount alignment in the two axes of freedom. Even with no systematic error, spatial artifacts from the reconstruction limit the ability to provide adequate spectral imagery without specialized image reconstruction techniques as targets become more spatially and spectrally uniform

The Need for Accurate Pre-processing and Data Integration for the Application of Hyperspectral Imaging in Mineral Exploration

Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften.Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications

Manifold learning based spectral unmixing of hyperspectral remote sensing data

Nonlinear mixing effects inherent in hyperspectral data are not properly represented in linear spectral unmixing models. Although direct nonlinear unmixing models provide capability to capture nonlinear phenomena, they are difficult to formulate and the results are not always generalizable. Manifold learning based spectral unmixing accommodates nonlinearity in the data in the feature extraction stage followed by linear mixing, thereby incorporating some characteristics of nonlinearity while retaining advantages of linear unmixing approaches. Since endmember selection is critical to successful spectral unmixing, it is important to select proper endmembers from the manifold space. However, excessive computational burden hinders development of manifolds for large-scale remote sensing datasets. This dissertation addresses issues related to high computational overhead requirements of manifold learning for developing representative manifolds for the spectral unmixing task. Manifold approximations using landmarks are popular for mitigating the computational complexity of manifold learning. A new computationally effective landmark selection method that exploits spatial redundancy in the imagery is proposed. A robust, less costly landmark set with low spectral and spatial redundancy is successfully incorporated with a hybrid manifold which shares properties of both global and local manifolds. While landmark methods reduce computational demand, the resulting manifolds may not represent subtle features of the manifold adequately. Active learning heuristics are introduced to increase the number of landmarks, with the goal of developing more representative manifolds for spectral unmixing. By communicating between the landmark set and the query criteria relative to spectral unmixing, more representative and stable manifolds with less spectrally and spatially redundant landmarks are developed. A new ranking method based on the pixels with locally high spectral variability within image subsets and convex-geometry finds a solution more quickly and precisely. Experiments were conducted to evaluate the proposed methods using the AVIRIS Cuprite hyperspectral reference dataset. A case study of manifold learning based spectral unmixing in agricultural areas is included in the dissertation.Remotely sensed data collected by airborne or spaceborne sensors are utilized to quantify crop residue cover over an extensive area. Although remote sensing indices are popular for characterizing residue amounts, they are not effective with noisy Hyperion data because the effect of residual striping artifacts is amplified in ratios involving band differences. In this case study, spectral unmixing techniques are investigated for estimating crop residue as an alternative approach to empirical models developed using band based indices. The spectral unmixing techniques, and especially the manifold learning approaches, provide more robust, lower RMSE estimates for crop residue cover than the hyperspectral index based method for Hyperion data

DETERMINING WHERE INDIVIDUAL VEHICLES SHOULD NOT DRIVE IN SEMIARID TERRAIN IN VIRGINIA CITY, NV

This thesis explored elements involved in determining and mapping where a vehicle should not drive off-road in semiarid areas. Obstacles are anything which slows or obstructs progress (Meyer et al., 1977) or limits the space available for maneuvering (Spenko et al., 2006). This study identified the major factors relevant in determining which terrain features should be considered obstacles when off-road driving and thus should be avoided. These are elements relating to the vehicle itself and how it is driven as well as terrain factors of slope, vegetation, water, and soil. Identification of these in the terrain was done using inferential methods of Terrain Pattern Recognition (TPR), analyzing of remotely sensing data, and Digital Elevation Map (DEM) data analysis. Analysis was further refined using other reference information about the area. Other factors such as weather, driving angle, and environmental impact are discussed. This information was applied to a section of Virginia City, Nevada as a case-study. Analysis and mapping was done purposely without field work prior to mapping to determine what could be assessed using only remote means. Not all findings from the literature review could be implemented in this trafficability study. Some methods and trafficability knowledge could not be implemented and were omitted due to data being unavailable, un-acquirable, or being too coarsely mapped to be useful. Examples of these are Lidar mapping of the area, soil profiling of the terrain, and assessment of plant species present in the area for driven-over traction and tire punctures. The Virginia City section was analyzed and mapped utilizing hyperspectral remotely sensed image data, remote-sensor-derived DEM data was used in a Geographical Information Systems (GIS). Stereo-paired air photos of the study site were used in TPR. Other information on flora, historical weather, and a previous soil survey map were used in a Geographical Information System (GIS). Field validation was used to check findings.The case study's trafficability assessment demonstrated methodologies of terrain analysis which successfully classified many materials present and identified major areas where a vehicle should not drive. The methods used were: Manual TPR of the stereo-paired air photo using a stereo photo viewer to conduct drainage-tracing and slope analysis of the DEM was done using automated methods in ArcMap. The SpecTIR hyperspectral data was analyzed using the manual Environment for Visualizing Images (ENVI) software hourglass procedure. Visual analysis of the hyperspectral data and air photos along with known soil and vegetation characteristics were used to refine analyses. Processed data was georectified using SpecTIR Geographic Lookup Table (GLT) input geometry, and exported to and analyzed in ArcMap with the other data previously listed. Features were identified based on their spectral attributes, spatial properties, and through visual analysis. Inaccuracies in mapping were attributable largely to spatial resolution of Digital Elevation Maps (DEMs) which averaged out some non-drivable obstacles and parts of a drivable road, subjective human and computer decisions during the processing of the data, and grouping of spectral end-members during hyperspectral data analysis. Further refinements to the mapping process could have been made if fieldwork was done during the mapping process.Mapping and field validation found: several manmade and natural obstacles were visible from the ground, but these obstacles were too fine, thin, or small to be identified from the remote sensing data. . Examples are fences and some natural terrain surface roughness - where the terrain's surface deviated from being a smooth surface, exhibiting micro- variations in surface elevation and/or textures. Slope analysis using the 10-meter and 30-meter resolution DEMs did not accurately depict some manmade features [eg. some of the buildings, portions of roads, and fences], evident with a well-trafficked paved road showing in DEM analysis as having too steep a slope [beyond 15°] to be drivable. Some features had been spectrally grouped together during analysis, due to similar spectral properties. Spectral grouping is a process where the spectral class's pixel areas are reviewed and classes which have too few occurrences are averaged into similar classes or dropped entirely. This is done to reduce the number of spectrally unique material classes to those that are most relevant to the terrain mapped. These decisions are subjective and in one case two similar spectral material classes were combined. In later evaluation should have remained as two separate material classes. In field sample collection, some of the determined features; free-standing water and liquid tanks, were found to be inaccessible due to being on private land and/or fence secured. These had to be visually verified - photos were also taken. Further refinements to the mapping could have been made if fieldwork was done during the mapping process. Determining and mapping where a vehicle should not drive in semiarid areas is a complex task which involves many variables and reference data types. Processing, analyzing, and fusing these different references entails subjective manual and automated decisions which are subject to errors and/or inaccuracies at multiple levels that can individually or collectively skew results, causing terrain trafficability to be depicted incorrectly. That said, a usable reference map is creatable which can assist decision makers when determining their route(s)

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

System of System Integration for Hyperspectral Imaging Microscopy

Hyperspectral imaging (HSI) has become a leading tool in the medical field due to its capabilities for providing assessments of tissue pathology and separation of fluorescence signals. Acquisition speeds have been slow due to the need to acquire signal in many spectral bands and the light losses associated with technologies of spectral filtering. Traditional methods resulted in limited signal strength which placed limitations on time sensitive and photosensitive assays. For example, the distribution of cyclic adenosine monophosphate (cAMP) is largely undetermined because current microscope technologies lack the combination of speed, resolution, and spectral ability to accurately measure Forster resonance energy transfer (FRET). The work presented in this dissertation assesses the feasibility of integrating excitation-scanning hyperspectral imaging methods in widefield and confocal microscopy as a potential solution to improving acquisition speeds without compromising sensitivity and specificity. Our laboratory has previously proposed excitation-scanning approaches to improve signal-to-noise ratio (SNR) and showed that by using excitation-scanning, most-to-all emitted light at each excitation wavelength band can be detected which in turn, increases the SNR. This dissertation describes development and early feasibility studies for two novel prototype concepts as an alternative excitation-scanning HSI technology that may xvi increase acquisition speeds without compromising sensitivity or specificity. To achieve this, two new technologies for excitation-scanning HSI were conceptually designed: - LED-based spectral illumination for widefield microscopy - Supercontinuum-laser-based spectral illumination for spinning disk confocal microscopy. Next, design concepts were theoretically evaluated and optimized, leading to prototype testing. To evaluate the performance of each concept, prototype systems were integrated with other systems and subsystems, calibrated and feasibility assays were executed. This dissertation is divided into three main sections: 1) early development feasibility results of an excitation-scanning widefield system of systems prototype utilizing LED-based HSI, 2) Excitation-scanning HSI and image analysis methods used for endmember identification in fluorescence microscopy studies, and 3) early development feasibility of an excitation-scanning confocal SoS prototype utilizing a supercontinuum laser light source. Integration and testing results proved initial feasibility of both LED-based and broadband-based SoSs. The LED-based light source was successfully tested on a widefield microscope, while the broadband light source system was successfully tested on a confocal microscope. Feasibility for the LED-based system showed that further optical transmission optimization is needed to achieve high acquisition rates without compromising sensitivity or specificity. Early feasibility study results for the broadband-based system showed a successful proof of concept. Findings presented in this dissertation are expected to impact the fields of cellular physiology, medical sciences, and clinical diagnostics by providing the ability for high speed, high sensitivity microscopic imaging with spectroscopic discrimination

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

Summaries of the Sixth Annual JPL Airborne Earth Science Workshop

This publication contains the summaries for the Sixth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on March 4-8, 1996. The main workshop is divided into two smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on March 4-6. The summaries for this workshop appear in Volume 1; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on March 6-8. The summaries for this workshop appear in Volume 2