Influence of the daylight illumination a weather conditions on Airborne Thermal Infrared Hyperspectral geological mapping

2021 ◽  
Author(s):  
Stephane Boubanga Tombet ◽  
Jean-Philippe Gagnon ◽  
Holger Eichstaedt ◽  
Joanne Ho
Keyword(s):  
Near Infrared ◽  
Thermal Infrared ◽  
Hyperspectral Data ◽  
Geological Mapping ◽  
Electromagnetic Spectrum ◽  
Spectral Features ◽  
Absorption Bands ◽  
Mineral Mapping ◽  
Atmospheric Compensation ◽  
Spectral Ranges

<p>The use of airborne remote sensing techniques for geological mapping offers many benefits as it allows coverage of large areas in a very efficient way.  While hyperspectral imaging from airborne/spaceborne platforms is now a well-established method applied to resolve many geological problems, it has mostly been developed only in the Visible-Near Infrared (VNIR, 0.4–1.0 mm) and Shortwave Infrared (SWIR, 1.0–2.5 mm) regions of the electromagnetic spectrum. However, the reflectance spectral features measured in the VNIR and SWIR spectral ranges are generally overtones and combination bands from fundamental absorption bands at longer wavelengths, such as in the Longwave Infrared (LWIR, 8–12 mm). The single absorption bands in the VNIR and SWIR spectral ranges are often very closely spaced so that the reflectance features measured by common spectrometers in this spectral region are typically broad and/or suffer from strong overlapping, which raises selectivity issues for mineral identification in some cases.</p><p>The inherent self-emission associated with LWIR under ambient conditions allows airborne mineral mapping in various weather (cloudy, partly cloudy or clear sky) and illumination (day or night) conditions. For this reason, LWIR often refers to the thermal infrared (TIR) spectral range. Solid targets such as minerals not only emit but also reflect TIR radiation. Since the two phenomena occur simultaneously, they end-up mixed in the radiance measured at the sensor level. The spectral features observed in a TIR spectrum of the sky and the atmosphere mostly correspond to ozone, water  vapor, carbon dioxide, methane and nitrous oxide with pretty sharp and narrow features compared with the infrared signature of solid materials such as minerals. The sharp spectral features of atmospheric gases are mixed up with broad minerals features in the collected geological mapping data, to unveil the spectral features associated with minerals from TIR measurements, the respective contributions of self-emission and reflection in the measurement must be «unmixed» and the atmospheric contributions must be compensated. This procedure refers to temperature-emissivity separation (TES). Therefore, to achieve an efficient TES and atmospheric compensation, the collection time and conditions of LWIR airborne hyperspectral data is of importance. Data of a flight mission in Southern Spain collected systematically at different times of the day (morning, mid-day and night) and in different altitudes using the Telops Hyper-Cam airborne system, a passive TIR hyperspectral sensor based on Fourier transform spectroscopy, were analyzed. TES was carried out on the hyperspectral data using<strong> two</strong> different approaches: a) Telops Reveal FLAASH IR software and b) DIMAP In-scene atmospheric compensation algorithm in order to retrieve thermodynamic temperature map and spectral emissivity data. Spectral analysis of the emissivity data with different mineral mapping methods based on commercial spectral libraries was used to compare results obtained during the different flight times and altitudes using the two post-processing methodologies. The results are discussed in the light of optimizing LWIR-based airborne operations in time and altitude to achieve best results for routine field mineral mapping applications such as in mining, soil science or archaeology, where the spatial analysis of mineral and chemical distribution is essential</p>

scholarly journals Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Random Noise ◽  
Synthetic Data ◽  
Regression Function ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Great Opportunity ◽  
Atmospheric Compensation

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

scholarly journals Mineral mapping by hyperspectral remote sensing in West Greenland using airborne, ship-based and terrestrial platforms

2018 ◽  
pp. 47-50 ◽  
Author(s):  
Sara Salehi ◽  
Simon Mose Thaarup
Keyword(s):  
Near Infrared ◽  
Hyperspectral Data ◽  
Geological Mapping ◽  
Infrared Range ◽  
Remote Measurement ◽  
Electromagnetic Spectrum ◽  
Similar Data ◽  
Data Set ◽  
Specific Chemical ◽  
West Greenland

While multispectral images have been in regular use since the 1970s, the widespread use of hyperspectral images is a relatively recent trend. This technology comprises remote measurement of specific chemical and physical properties of surface materials through imaging spectroscopy. Regional geological mapping and mineral exploration are among the main applications that may benefit from hyperspectral technology. Minerals and rocks exhibit diagnostic spectral features throughout the electromagnetic spectrum that allow their chemical composition and relative abundance to be mapped. Most studies using hyperspectral data for geological applications have concerned areas with arid to semi-arid climates, and using airborne data collection. Other studies have investigated terrestrial outcrop sensing and integration with laser scanning 3D models in ranges of up to a few hundred metres, whereas less attention has been paid to ground-based imaging of more distant targets such as mountain ridges, cliffs or the walls of large pits. Here we investigate the potential of using such data in well-exposed Arctic regions with steep topography as part of regional geological mapping field campaigns, and to test how airborne hyperspectral data can be combined with similar data collected on the ground or from moving platforms such as a small ship. The region between the fjords Ikertoq and Kangerlussuaq (Søndre Strømfjord) in West Greenland was selected for a field study in the summer of 2016. This region is located in the southern part of the Palaeoproterozoic Nagssugtoqidian orogen and consists of high-grade metamorphic ortho- and paragneisses and metabasic rocks (see below). A regional airborne hyperspectral data set (i.e. HyMAP) was acquired here in 2002 (Tukiainen & Thorning 2005), comprising 54 flight lines covering an area of c. 7500 km2; 19 of these flight lines were selected for the present study (Fig. 1). The target areas visited in the field were selected on the basis of preliminary interpretations of HyMap scenes and geology (Korstgård 1979). Two different sensors were utilised to acquire the new hyperspectral data, predominantly a Specim AisaFenix hyperspectral scanner due to its wide spectral range covering the visible to near infrared and shortwave infrared parts of the electromagnetic spectrum. A Rikola Hyperspectral Imager constituted a secondary imaging system. It is much smaller and lighter than the Fenix scanner, but is spectrally limited to the visible near infrared range. The results obtained from combining the airborne hyperspectral data and the Rikola instrument are presented in Salehi (2018), this volume. In addition, representative samples of the main rock types were collected for subsequent laboratory analysis. A parallel study was integrated with geological and 3D photogrammetric mapping in Karrat region farther north in West Greenland (Rosa et al. 2017; Fig. 1).

scholarly journals Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 3117
Author(s):  
Huize Liu ◽  
Ke Wu ◽  
Honggen Xu ◽  
Ying Xu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Near Infrared ◽  
Three Dimensional ◽  
Thermal Infrared ◽  
Hyperspectral Data ◽  
Gansu Province ◽  
Electromagnetic Spectrum ◽  
Lithological Mapping ◽  
Classification Feature

In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.

scholarly journals MULTI-LABEL CLASSIFICATION FOR DRILL-CORE HYPERSPECTRAL MINERAL MAPPING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 383-388
Author(s):  
I. C. Contreras ◽  
M. Khodadadzadeh ◽  
R. Gloaguen
Keyword(s):  
Near Infrared ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Training Data ◽  
Electromagnetic Spectrum ◽  
Drill Core ◽  
Data Set ◽  
Mineral Mapping ◽  
Mapping Task ◽  
Liberation Analysis

Abstract. A multi-label classification concept is introduced for the mineral mapping task in drill-core hyperspectral data analysis. As opposed to traditional classification methods, this approach has the advantage of considering the different mineral mixtures present in each pixel. For the multi-label classification, the well-known Classifier Chain method (CC) is implemented using the Random Forest (RF) algorithm as the base classifier. High-resolution mineralogical data obtained from Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software are used for generating the training data set. The drill-core hyperspectral data used in this paper cover the visible-near infrared (VNIR) and the short-wave infrared (SWIR) range of the electromagnetic spectrum. The quantitative and qualitative analysis of the obtained results shows that the multi-label classification approach provides meaningful and descriptive mineral maps and outperforms the single-label RF classification for the mineral mapping task.

scholarly journals Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 742
Author(s):  
Canh Nguyen ◽  
Vasit Sagan ◽  
Matthew Maimaitiyiming ◽  
Maitiniyazi Maimaitijiang ◽  
Sourav Bhadra ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Viral Diseases ◽  
Support Vector ◽  
Spectral Features ◽  
Feature Spaces

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

scholarly journals Two-photon excited deep-red and near-infrared emissive organic co-crystals

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Yu Wang ◽  
Huang Wu ◽  
Penghao Li ◽  
Su Chen ◽  
Leighton O. Jones ◽  
...  
Keyword(s):  
Cross Sections ◽  
Optical Materials ◽  
Near Infrared ◽  
Infrared Emission ◽  
Photon Absorption ◽  
Electromagnetic Spectrum ◽  
Absorption Bands ◽  
Two Photon Absorption ◽  
Two Photon ◽  
Near Infrared Emission

Abstract Two-photon excited near-infrared fluorescence materials have garnered considerable attention because of their superior optical penetration, higher spatial resolution, and lower optical scattering compared with other optical materials. Herein, a convenient and efficient supramolecular approach is used to synthesize a two-photon excited near-infrared emissive co-crystalline material. A naphthalenediimide-based triangular macrocycle and coronene form selectively two co-crystals. The triangle-shaped co-crystal emits deep-red fluorescence, while the quadrangle-shaped co-crystal displays deep-red and near-infrared emission centered on 668 nm, which represents a 162 nm red-shift compared with its precursors. Benefiting from intermolecular charge transfer interactions, the two co-crystals possess higher calculated two-photon absorption cross-sections than those of their individual constituents. Their two-photon absorption bands reach into the NIR-II region of the electromagnetic spectrum. The quadrangle-shaped co-crystal constitutes a unique material that exhibits two-photon absorption and near-infrared emission simultaneously. This co-crystallization strategy holds considerable promise for the future design and synthesis of more advanced optical materials.

Hylite: a hyperspectral toolbox for open pit mapping

2020 ◽  
Author(s):  
Sam Thiele ◽  
Sandra Lorenz ◽  
Moritz Kirsch ◽  
Richard Gloaguen
Keyword(s):  
Short Wave ◽  
Mineral Deposit ◽  
Hyperspectral Data ◽  
Geological Mapping ◽  
Open Pit ◽  
Electromagnetic Spectrum ◽  
Mining Operations ◽  
Open Pit Mines ◽  
Volcanogenic Massive Sulphide ◽  
Potential Applications

<p>Hyperspectral imaging is a powerful tool for mapping mineralogy and lithology in core and outcrops, as many minerals show distinct spectral features in the commonly analysed visible, near, short-wave and long-wave infrared regions of the electromagnetic spectrum. High resolution ground and UAS (unmanned aerial system)-based sensors thus have significant potential as a tool for rapid and non-invasive geological mapping in mining operations, exploration campaigns and scientific research. However, the geometrical complexity of many outcrops (e.g. cliffs, open-pit mines) can result in significant technical challenges when acquiring and processing hyperspectral data. In this contribution we present updates to the previously published MEPHySTo python toolbox for correcting, georeferencing, projecting and analysing geometrically complex hyperspectral scenes. We showcase these methods using datasets covering volcanogenic massive sulphide (VMS) mineralisation exposed within open pit mines in Rio Tinto (Spain), and interpret possible structural and lithological controls on mineralization. Potential applications of hyperspectral mapping for grade control, outcrop mapping and the characterisation of different mineral deposit styles are also discussed.</p>

A supervised technique for drill-core mineral mapping using Hyperspectral data

2020 ◽  
Author(s):  
Cecilia Contreras ◽  
Mahdi Khodadadzadeh ◽  
Laura Tusa ◽  
Richard Gloaguen
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Drill Core ◽  
Data Set ◽  
Mineral Mapping ◽  
Core Logging ◽  
Learning Techniques

<p>Drilling is a key task in exploration campaigns to characterize mineral deposits at depth. Drillcores<br>are first logged in the field by a geologist and with regards to, e.g., mineral assemblages,<br>alteration patterns, and structural features. The core-logging information is then used to<br>locate and target the important ore accumulations and select representative samples that are<br>further analyzed by laboratory measurements (e.g., Scanning Electron Microscopy (SEM), Xray<br>diffraction (XRD), X-ray Fluorescence (XRF)). However, core-logging is a laborious task and<br>subject to the expertise of the geologist.<br>Hyperspectral imaging is a non-invasive and non-destructive technique that is increasingly<br>being used to support the geologist in the analysis of drill-core samples. Nonetheless, the<br>benefit and impact of using hyperspectral data depend on the applied methods. With this in<br>mind, machine learning techniques, which have been applied in different research fields,<br>provide useful tools for an advance and more automatic analysis of the data. Lately, machine<br>learning frameworks are also being implemented for mapping minerals in drill-core<br>hyperspectral data.<br>In this context, this work follows an approach to map minerals on drill-core hyperspectral data<br>using supervised machine learning techniques, in which SEM data, integrated with the mineral<br>liberation analysis (MLA) software, are used in training a classifier. More specifically, the highresolution<br>mineralogical data obtained by SEM-MLA analysis is resampled and co-registered<br>to the hyperspectral data to generate a training set. Due to the large difference in spatial<br>resolution between the SEM-MLA and hyperspectral images, a pre-labeling strategy is<br>required to link these two images at the hyperspectral data spatial resolution. In this study,<br>we use the SEM-MLA image to compute the abundances of minerals for each hyperspectral<br>pixel in the corresponding SEM-MLA region. We then use the abundances as features in a<br>clustering procedure to generate the training labels. In the final step, the generated training<br>set is fed into a supervised classification technique for the mineral mapping over a large area<br>of a drill-core. The experiments are carried out on a visible to near-infrared (VNIR) and shortwave<br>infrared (SWIR) hyperspectral data set and based on preliminary tests the mineral<br>mapping task improves significantly.</p>

scholarly journals The Zugspitze radiative closure experiment for quantifying water vapor absorption over the terrestrial and solar infrared – Part 3: Quantification of the mid- and near-infrared water vapor continuum in the 2500 to 7800 cm<sup>−1</sup> spectral range under atmospheric conditions

2016 ◽  
Vol 16 (18) ◽  
pp. 11671-11686 ◽  
Author(s):  
Andreas Reichert ◽  
Ralf Sussmann
Keyword(s):  
Water Vapor ◽  
Spectral Range ◽  
Near Infrared ◽  
Atmospheric Conditions ◽  
Water Vapor Absorption ◽  
Absorption Bands ◽  
Continuum Absorption ◽  
Upper Limits ◽  
Vapor Absorption ◽  
Spectral Ranges

Abstract. We present a first quantification of the near-infrared (NIR) water vapor continuum absorption from an atmospheric radiative closure experiment carried out at the Zugspitze (47.42° N, 10.98° E; 2964 m a.s.l.). Continuum quantification is achieved via radiative closure using radiometrically calibrated solar Fourier transform infrared (FTIR) absorption spectra covering the 2500 to 7800 cm−1 spectral range. The dry atmospheric conditions at the Zugspitze site (IWV 1.4 to 3.3 mm) enable continuum quantification even within water vapor absorption bands, while upper limits for continuum absorption can be provided in the centers of window regions. Throughout 75 % of the 2500 to 7800 cm−1 spectral range, the Zugspitze results agree within our estimated uncertainty with the widely used MT_CKD 2.5.2 model (Mlawer et al., 2012). In the wings of water vapor absorption bands, our measurements indicate about 2–5 times stronger continuum absorption than MT_CKD, namely in the 2800 to 3000 cm−1 and 4100 to 4200 cm−1 spectral ranges. The measurements are consistent with the laboratory measurements of Mondelain et al. (2015), which rely on cavity ring-down spectroscopy (CDRS), and the calorimetric–interferometric measurements of Bicknell et al. (2006). Compared to the recent FTIR laboratory studies of Ptashnik et al. (2012, 2013), our measurements are consistent within the estimated errors throughout most of the spectral range. However, in the wings of water vapor absorption bands our measurements indicate typically 2–3 times weaker continuum absorption under atmospheric conditions, namely in the 3200 to 3400, 4050 to 4200, and 6950 to 7050 cm−1 spectral regions.

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