Hyperspectral fluorescence imaging: robust detection of petroleum in porous sedimentary rock formations

2021 ◽  
pp. 1-57
Author(s):  
Otto Charles Anthony Gadea ◽  
Shuhab Danishwar Khan
Keyword(s):  
Oil Recovery ◽  
Surface Mining ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Natural Bitumen ◽  
Mining Operations ◽  
Spectral Angle Mapper ◽  
Tar Sands ◽  
Robust Detection ◽  
Rock Formations

Examining hand samples can be a necessary step for geological studies, and effective mapping of such samples can be achieved through the high spectral and spatial resolutions of ground-based hyperspectral imaging (HSI) at the millimeter to centimeter scale. We present a simple approach to crude oil identification and characterization—feasible in 16 hours—based on hyperspectral data collected under ultraviolet lighting and normalized with respect to the fluorescence patterns of Spectralon diffuse reflectance material. The samples under consideration were extracted from a core acquired from an Early Cretaceous bituminous sandstone formation in the Athabasca basin located near Fort McMurray, Alberta, Canada. This basin contains the largest natural bitumen deposit in the world, where surface mining operations currently are viable only for approximately 20% of the estimated 164 billion barrels of total recoverable oil reserves. This deposit is unique in that its tar sands are water-wet, which facilitates the separation of bitumen from the sandstone via water-based gravity separation. However, large amounts of water are still required for oil recovery, so a fast and reliable way to mark portions of the deposit where ample petroleum has accumulated and assess its extractability based on its physical characteristics prior to mining can be helpful for optimizing resource usage. For this reason, we test and visually evaluate the ability of three classification methods—Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Supervised Neural Network (SNN)—to distinguish between bitumen, Spectralon, and a non-fluorescent slate background based on the emission of visible light in response to absorbing ultraviolet light of different wavelengths. We also propose spectral indices useful for indicating concentrated bitumen in tar sands. Errors inherent to the methodology are discussed along with ways to mitigate them. After accounting for these, HSI can be a valuable asset alongside other techniques used for production economics evaluation.

scholarly journals Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery

2018 ◽  
Vol 10 (8) ◽  
pp. 1208 ◽  
Author(s):  
Javier Marcello ◽  
Francisco Eugenio ◽  
Javier Martín ◽  
Ferran Marqués
Keyword(s):  
High Resolution ◽  
Aquatic Vegetation ◽  
Hyperspectral Imagery ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Shallow Waters ◽  
Benthic Habitats ◽  
Spectral Angle Mapper ◽  
Seabed Mapping

Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.

scholarly journals Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)

2020 ◽  
Vol 12 (3) ◽  
pp. 408
Author(s):  
Małgorzata Krówczyńska ◽  
Edwin Raczko ◽  
Natalia Staniszewska ◽  
Ewa Wilk
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Spectral Feature ◽  
Hyperspectral Data ◽  
Aerial Photographs ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Asbestos Cement ◽  
Feature Fitting

Due to the pathogenic nature of asbestos, a statutory ban on asbestos-containing products has been in place in Poland since 1997. In order to protect human health and the environment, it is crucial to estimate the quantity of asbestos–cement products in use. It has been evaluated that about 90% of them are roof coverings. Different methods are used to estimate the amount of asbestos–cement products, such as the use of indicators, field inventory, remote sensing data, and multi- and hyperspectral images; the latter are used for relatively small areas. Other methods are sought for the reliable estimation of the quantity of asbestos-containing products, as well as their spatial distribution. The objective of this paper is to present the use of convolutional neural networks for the identification of asbestos–cement roofing on aerial photographs in natural color (RGB) and color infrared (CIR) compositions. The study was conducted for the Chęciny commune. Aerial photographs, each with the spatial resolution of 25 cm in RGB and CIR compositions, were used, and field studies were conducted to verify data and to develop a database for Convolutional Neural Networks (CNNs) training. Network training was carried out using the TensorFlow and R-Keras libraries in the R programming environment. The classification was carried out using a convolutional neural network consisting of two convolutional blocks, a spatial dropout layer, and two blocks of fully connected perceptrons. Asbestos–cement roofing products were classified with the producer’s accuracy of 89% and overall accuracy of 87% and 89%, depending on the image composition used. Attempts have been made at the identification of asbestos–cement roofing. They focus primarily on the use of hyperspectral data and multispectral imagery. The following classification algorithms were usually employed: Spectral Angle Mapper, Support Vector Machine, object classification, Spectral Feature Fitting, and decision trees. Previous studies undertaken by other researchers showed that low spectral resolution only allowed for a rough classification of roofing materials. The use of one coherent method would allow data comparison between regions. Determining the amount of asbestos–cement products in use is important for assessing environmental exposure to asbestos fibres, determining patterns of disease, and ultimately modelling potential solutions to counteract threats.

scholarly journals Urban land cover classification using hyperspectral data

2014 ◽  
Vol XL-8 ◽  
pp. 751-754 ◽  
Author(s):  
G. Hegde ◽  
J. Mohammed Ahamed ◽  
R. Hebbar ◽  
U. Raj
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Land Cover Classification ◽  
Urban Land ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Atmospheric Effects ◽  
Spectral Angle Mapper ◽  
Urban Land Cover ◽  
Lower Accuracy

Urban land cover classification using remote sensing data is quite challenging due to spectrally and spatially complex urban features. The present study describes the potential use of hyperspectral data for urban land cover classification and its comparison with multispectral data. EO-1 Hyperion data of October 05, 2012 covering parts of Bengaluru city was analyzed for land cover classification. The hyperspectral data was initially corrected for atmospheric effects using MODTRAN based FLAASH module and Minimum Noise Fraction (MNF) transformation was applied to reduce data dimensionality. The threshold Eigen value of 1.76 in VNIR region and 1.68 in the SWIR region was used for selection of 145 stable bands. Advanced per pixel classifiers <i>viz.</i>, Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) were used for general urban land cover classification. Accuracy assessment of the classified data revealed that SVM was quite superior (82.4 per cent) for urban land cover classification as compared to SAM (67.1 per cent). Selecting training samples using end members significantly improved the classification accuracy by 20.1 per cent in SVM. The land cover classification using multispectral LISS-III data using SVM showed lower accuracy mainly due to limitation of spectral resolution. The study indicated the requirement of additional narrow bands for achieving reasonable classification accuracy of urban land cover. Future research is focused on generating hyperspectral library for different urban features.

scholarly journals UAV-BASED HYPERSPECTRAL DATA ANALYSIS FOR URBAN AREA MAPPING

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 621-626
Author(s):  
N. A. Suran ◽  
H. Z. M. Shafri ◽  
N. S. N. Shaharum ◽  
N. A. W. M. Radzali ◽  
V. Kumar
Keyword(s):  
Urban Area ◽  
Low Cost ◽  
Hyperspectral Data ◽  
Pollution Monitoring ◽  
Support Vector ◽  
Land Resource ◽  
Easy Method ◽  
Spectral Angle Mapper ◽  
Aerial Vehicle ◽  
Artificial Neural Network Ann

Abstract. A recent development in low-cost technology such as Unmanned Aerial Vehicle (UAV) offers an easy method for collecting geospatial data. UAV plays an important role in land resource surveying, urban planning, environmental protection, pollution monitoring, disaster monitoring and other applications. It is a highly adaptable technology that is continuously changing in innovative ways to provide greater utility. Thus, this study aimed to evaluate the capability of UAV-based hyperspectral data for urban area mapping. In order to do the mapping, Artificial Neural Network (ANN), Support Vector Machine (SVM), Maximum Likelihood (ML) and Spectral Angle Mapper (SAM) were used to classify the urban area. The classifications involved seven classes: concrete, aluminium, flexible pavement, clay tile, interlocking block, tree and grass. Then, the overall accuracies obtained from ANN, SVM, ML and SAM for 0.3 m spatial resolution images were 92.33%, 85.86%, 83.41% and 46.55% with the kappa coefficient of 0.91, 0.83, 0.80 and 0.38 respectively. Thus, the classification results showed that the powerful and intelligent ANN algorithm produced the highest accuracy compared to the other three algorithms. Overall, mapping of urban area using UAV-based hyperspectral data and advanced algorithms could be the way forward in producing updated urban area maps.

scholarly journals Quantifying Changes on Forest Succession in a Dry Tropical Forest Using Angular-Hyperspectral Remote Sensing

2018 ◽  
Vol 10 (12) ◽  
pp. 1865 ◽  
Author(s):  
Virginia Garcia Millan ◽  
Arturo Sanchez-Azofeifa
Keyword(s):  
Large Scale ◽  
Forest Succession ◽  
Early Stage ◽  
Tropical Dry Forest ◽  
Intermediate Stage ◽  
Hyperspectral Data ◽  
Dry Forest ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Different Levels

The tropical dry forest (TDF) is one the most threatened ecosystems in South America, existing on a landscape with different levels of ecological succession. Among satellites dedicated to Earth observation and monitoring ecosystem succession, CHRIS/PROBA is the only satellite that presents quasi-simultaneous multi-angular pointing and hyperspectral imaging. These two characteristics permit the study of structural and compositional differences of TDFs with different levels of succession. In this paper, we use 2008 and 2014 CHRIS/PROBA images from a TDF in Minas Gerais, Brazil to study ecosystem succession after abandonment. Using a −55° angle of observation; several classifiers including spectral angle mapper (SAM), support vector machine (SVM), and decision trees (DT) were used to test how well they discriminate between different successional stages. Our findings suggest that the SAM is the most appropriate method to classify TDFs as a function of succession (accuracies ~80 for % for late stage, ~85% for the intermediate stage, ~70% for early stage, and ~50% for other classes). Although CHRIS/PROBA cannot be used for large-scale/long-term monitoring of tropical forests because of its experimental nature; our results support the potential of using multi-angle hyperspectral data to characterize the structure and composition of TDFs in the near future.

scholarly journals Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Prakash Nimbalkar ◽  
Anna Jarocinska ◽  
Bogdan Zagajewski
Keyword(s):  
Surface Characterization ◽  
Imaging Spectroscopy ◽  
Selection Method ◽  
Hyperspectral Data ◽  
Band Selection ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Supervised Classifiers ◽  
Reduction Methods ◽  
Transformation Methods

Imaging spectroscopy in the remote sensing is an ever emerging platform that has offered the hyperspectral imaging (HSI) which delivers the Earth’s object information in hundreds of bands. HSI integrates conventional imaging with spectroscopy to get rich spectral and spatial features of the object. However, the challenges associated with HSI are its huge dimensionality and data redundancy that requests huge space, complex computations, and lengthier processing time. Therefore, this study aims to find the optimal bands to characterize the roof surfaces using supervised classifiers. To deal with high dimensionality of hyperspectral data, this study assesses the band selection method over data transformation methods. This study provides the comparison between data reduction methods and used classifiers. The height information from LiDAR was used to characterize urban roofs above the height of 2.5 meters. The optimal bands were investigated using supervised classifiers such as artificial neural network (ANN), support vector machine (SVM), and spectral angle mapper (SAM) by comparing accuracies. The classification result shows that ANN and SVM classifiers outperform whereas SAM performed poorly in roof characterization. The band selection method worked efficiently than the transformation methods. The classification algorithm successfully identifies the optimum bands with significant accuracy.

scholarly journals Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data

2014 ◽  
Vol XL-8 ◽  
pp. 667-670 ◽  
Author(s):  
R. Vidhya ◽  
D. Vijayasekaran ◽  
M. Ahamed Farook ◽  
S. Jai ◽  
M. Rohini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Health Status ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Svm Classification ◽  
Sensing Data

Mangrove ecosystem plays a crucial role in costal conservation and provides livelihood supports to humans. It is seriously affected by the various climatic and anthropogenic induced changes. The continuous monitoring is imperative to protect this fragile ecosystem. In this study, the mangrove area and health status has been extracted from Hyperspectral remote sensing data (EO- 1Hyperion) using support vector machine classification (SVM). The principal component transformation (PCT) technique is used to perform the band reduction in Hyperspectral data. The soil adjusted vegetation Indices (SAVI) were used as additional parameters. The mangroves are classified into three classes degraded, healthy and sparse. The SVM classification is generated overall accuracy of 73 % and kappa of 0.62. The classification results were compared with the results of spectral angle mapper classification (SAM). The SAVI also included in SVM classification and the accuracy found to be improved to 82 %. The sparse and degraded mangrove classes were well separated. The results indicate that the mapping of mangrove health is accurate when the machine learning classifier like SVM combined with different indices derived from hyperspectral remote sensing data.

Advanced Systems to Prevent Collisions of Mining Equipment and Personnel Accidents in Surface Mining

2020 ◽  
pp. 24-29
Author(s):  
A.F. Klebanov ◽  
M.V. Kadochnikov ◽  
V.V. Ulitin ◽  
D.N. Sizemov
Keyword(s):  
Human Life ◽  
Surface Mining ◽  
Production Cycle ◽  
Mining Equipment ◽  
Mining Operations ◽  
Emergency Situations ◽  
Surface Mines ◽  
Technical Requirements ◽  
Personal Devices ◽  
Blind Spots

The article addresses the issues of ensuring safe operation of mining equipment in surface mining. It describes the main factors and situations that pose a high risk to human life and health. The most dangerous incidents are shown to be related to limited visibility and blind spots for operators of mining equipment, which can result in collisions and personnel run over. The main technologies and specific solutions used to design collision avoidance systems are described and their general comparison is provided. A particular focus is placed on monitoring the health of employees at their workplace by means of portable personal devices that promptly inform the dispatcher of emergency situations. General technical requirements are formulated for designing of the system to prevent equipment collisions and personnel run over in surface mining operations. The paper emphasizes the importance of introducing a multifunctional safety system in surface mines in order to minimise the possibility of incidents and accidents throughout the entire production cycle.

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