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Open accessJournal ArticleDOI: 10.1088/1757-899X/263/3/032001

Nontronite mineral identification in nilgiri hills of tamil nadu using hyperspectral remote sensing

01 Nov 2017-Vol. 263, Iss: 3, pp 032001
About: This article is published in Microelectronics Systems Education.The article was published on 2017-11-01 and is currently open access. It has received 7 citation(s) till now. The article focuses on the topic(s): Nontronite.

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Topics: Nontronite (52%)
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Open access
Amin Beiranvand Pour, Mazlan Hashim1Institutions (1)
01 Jan 2013-
Abstract: The area under investigation is the Bau gold mining district in the State of Sarawak, East Malaysia, on the island of Borneo. It has tropical climate with limited bedrock exposures. Bau is a gold field similar to Carlin style gold deposits. Geological analyses coupled with remote sensing data were used to detect hydrothermally altered rocks associated with gold mineralization. The Landsat Enhanced Thematic Mapper+ (ETM+) and Hyperion data were used to carry out mineral mapping of mineralized zones in the study area and surrounding terrain. Directed Principal Components Analysis (DPCA) transformation of four appropriate ETM+ band ratios were applied to produce DPC images, allowing the removal of the effects of vegetation from ETM+ data and the detection of separate mineral images at a regional scale. Linear Spectral Unmixing (LSU) was used to produce image maps of hydroxyl-bearing minerals using Hyperion data at a district scale. Results derived from the visible and near infrared and shortwave infrared bands of Hyperion represented iron oxide/hydroxide and clay minerals rich zones associated with the known gold prospects in the Bau district. The results show that the known gold prospects and potentially interesting areas are recognizable by the methods used, despite limited bedrock exposure in this region and the constraints imposed by the tropical environment. The approach used in this study can be more broadly applicable to provide an opportunity for detecting potentially interesting areas of gold mineralization using the ETM+ and Hyperion data in the tropical/sub-tropical regions.

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Topics: Gold mining (52%), Thematic Mapper (51%)

14 Citations


Journal ArticleDOI: 10.1007/S12517-020-06070-7
Abstract: This study aims to detect indicative minerals by spectral unmixing of the Hyperion and HyMap datasets in the Sar Chah-e Shur area. The mineral endmembers and their abundances were therefore determined using a series of hyperspectral processing algorithms. The virtual dimensionality methods including principal component analysis (PCA), minimum noise fraction (MNF), singular valued decomposition (SVD), Harsanyi-Farrand-Chang (HFC)/ (NWHFC), and Hyperspectral signal subspace identification by minimum error (HySime) were applied to estimate the number of endmembers. Five pure pixel-based methods including pixel purity index (PPI), sequential maximum angle convex cone (SMACC), simplex growing algorithm (SGA), N-FINDR, and vertex component analysis (VCA) were then applied for extracting the spectra of endmembers. Clay, serpentine, mica, and zeolite group minerals were identified which are consistent with the geological investigations in the region. The detected minerals were then mapped by the fully constrained least square (FCLS) method. The functionality of the methods and their performances on HyMap and Hyperion data were surveyed by several criteria including the number of recognized endmembers, the matching score of extracted endmembers with the reference spectrum, the agreement of the estimated abundances maps with the relevant lithological units on the geological map, and the average reconstruction error (ARE). Two hybrid maps were generated by combining individual methods that were found highly consistent with the geological map. The XRD analysis of three chips rock samples of two indicative lithological units was used to additionally check the efficiency of the applied methods.

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4 Citations


Book ChapterDOI: 10.1007/978-981-15-0135-7_25
01 Jan 2020-
Abstract: Hyperspectral imagery is one of the research areas in the field of remote sensing. Hyperspectral sensors record reflectance of object or material or region across the electromagnetic spectrum. Mineral identification is an urban application in the field of remote sensing of Hyperspectral data. Challenges with the hyperspectral data include high dimensionality and size of the hyperspectral data. Principle component analysis (PCA) is used to reduce the dimension of data by band selection approach. Unsupervised classification technique is one of the hot research topics. Due to the unavailability of ground truth data, unsupervised algorithm is used to classify the minerals present in the remotely sensed hyperspectral data. K-means is unsupervised clustering algorithm used to classify the mineral and then further SVM is used to check the classification accuracy. K-means is applied to end member data only. SVM used k-means result as a labelled data and classify another set of dataset.

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Topics: Hyperspectral imaging (59%), Ground truth (51%)

3 Citations


Open access
01 Mar 2020-
Abstract: The traditional approaches to estimate the Iron ore involves large manpower, cost and time. Iron ore identification is necessary due to the rapid increase in construction work, industries and population. Hyperspectral Imagery analysis used to estimate the Iron ore precisely depends on the spectral signature. The spectral signature of Iron ore shows huge absorption in 865 nm due to the presence of Iron content in the sample spectra. Hyperspectral imagery contains a large number of spectral bands and involves various processing steps such as identification of the calibration bands, absolute reflectance generation, data dimensional minimization, Iron ore endmembers extraction and classification. The radiance imagery absolute reflectance bands are carried out using FLAASH atmospheric correction module. The noiseless pure pixels are obtained using data dimensionality reduction techniques as spectral data reduction and spatial data reduction. The comparative analysis is performed between sub-pixel (LSU) and per-pixel (SAM) classification. The results showed that the sub-pixelbased classification produces a better distribution of Iron ore than the per pixel-based classification.

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2 Citations


Journal ArticleDOI: 10.1007/S12517-018-4149-3
Vignesh Kumar1, Kiran Yarrakula1Institutions (1)
Abstract: Hyperspectral remote sensing consolidates imaging and spectroscopy in a solitary system which frequently comprises big datasets and necessitates the novel processing methods. In the present study, Cheranmadevi Block of Tirunelveli District in Tamil Nadu is selected to extract the abundant limestone mineral. Hyperion is one of the freely available hyperspectral imagery containing 242 spectral bands with 10-nm intervals in the wavelength between 400 and 2500 nm. The main objectives of the present research work are to enhance the imagery visualization, end member extraction, and classification, and estimate the abundant limestone quantity by removing the striping error in Hyperion imagery. The scanning electron microscope with energy-dispersive X-ray spectroscopy analysis is performed to identify the chemical composition of limestone mineral. The spectral reflectance of limestone is characterized using analytical spectral devices like a field spectroradiometer. Limestone has deep absorption in the short-wave infrared region (1900–2500 nm) around 2320–2340 nm due to their calcite composition (CaCO3). The feature extraction in Hyperion data is performed using various preprocessing steps like bad bands removal, vertical strip removal, and radiance and reflectance creation. To improve the classification accuracy, vertical strip removal process is performed using a local destriping algorithm. The absolute reflectance is achieved by the atmospheric correction module using Fast Line-of-sight Atmospheric Analysis of Hypercubes. The acquired reflectance image spectra are compared with the spectral libraries of USGS, JPL, and field spectra. Destriping enhances qualities of Hyperion data interims of the spectral profile, radiance, reflectance, and data reduction methods. The present research work focused on the local destriping algorithm to increase the quality and quantity of limestone deposit extraction.

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1 Citations


References
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Open accessJournal ArticleDOI: 10.1016/J.ENVPOL.2015.05.041
Paul Arellano1, Paul Arellano2, Kevin Tansey2, Heiko Balzter2  +1 moreInstitutions (3)
Abstract: The global demand for fossil energy is triggering oil exploration and production projects in remote areas of the world. During the last few decades hydrocarbon production has caused pollution in the Amazon forest inflicting considerable environmental impact. Until now it is not clear how hydrocarbon pollution affects the health of the tropical forest flora. During a field campaign in polluted and pristine forest, more than 1100 leaf samples were collected and analysed for biophysical and biochemical parameters. The results revealed that tropical forests exposed to hydrocarbon pollution show reduced levels of chlorophyll content, higher levels of foliar water content and leaf structural changes. In order to map this impact over wider geographical areas, vegetation indices were applied to hyperspectral Hyperion satellite imagery. Three vegetation indices (SR, NDVI and NDVI705) were found to be the most appropriate indices to detect the effects of petroleum pollution in the Amazon forest.

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Topics: Petroleum Pollution (53%), Vegetation (52%), Pollution (52%) ...read more

107 Citations


Open accessJournal ArticleDOI: 10.1186/2193-1801-3-130
Amin Beiranvand Pour1, Mazlan Hashim1Institutions (1)
07 Mar 2014-SpringerPlus
Abstract: This paper provides a review of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and Hyperion data and applications of the data as a tool for ore minerals exploration, lithological and structural mapping. Spectral information extraction from ASTER, ALI, and Hyperion data has great ability to assist geologists in all disciplines to map the distribution and detect the rock units exposed at the earth’s surface. The near coincidence of Earth Observing System (EOS)/Terra and Earth Observing One (EO-1) platforms allows acquiring ASTER, ALI, and Hyperion imagery of the same ground areas, resulting accurate information for geological mapping applications especially in the reconnaissance stages of hydrothermal copper and gold exploration, chromite, magnetite, massive sulfide and uranium ore deposits, mineral components of soils and structural interpretation at both regional and district scales. Shortwave length infrared and thermal infrared bands of ASTER have sufficient spectral resolution to map fundamental absorptions of hydroxyl mineral groups and silica and carbonate minerals for regional mapping purposes. Ferric-iron bearing minerals can be discriminated using six unique wavelength bands of ALI spanning the visible and near infrared. Hyperion visible and near infrared bands (0.4 to 1.0 μm) and shortwave infrared bands (0.9 to 2.5 μm) allowed to produce image maps of iron oxide minerals, hydroxyl-bearing minerals, sulfates and carbonates in association with hydrothermal alteration assemblages, respectively. The techniques and achievements reviewed in the present paper can further introduce the efficacy of ASTER, ALI, and Hyperion data for future mineral and lithological mapping and exploration of the porphyry copper, epithermal gold, chromite, magnetite, massive sulfide and uranium ore deposits especially in arid and semi-arid territory.

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74 Citations


Open accessJournal ArticleDOI: 10.1080/01431161.2011.600350
Abstract: Imaging spectrometer data (also known as ‘hyperspectral imagery’ or HSI) are well established for detailed mineral mapping from airborne and satellite systems. Overhead data, however, have substantial additional potential when used together with ground-based measurements. An imaging spectrometer system was used to acquire airborne measurements and to image in-place outcrops (mine walls) and boxed drill core and rock chips using modified sensor-mounting configurations. Data were acquired at 5 nm nominal spectral resolution in 360 channels from 0.4 to 2.45 μm. Analysis results using standardized hyperspectral methodologies demonstrate rapid extraction of representative mineral spectra and mapping of mineral distributions and abundances in map-plan, with core depth, and on the mine walls. The examples shown highlight the capabilities of these data for mineral mapping. Integration of these approaches promotes improved understanding of relations between geology, alteration and spectral signatures in three dime...

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67 Citations


Open accessJournal ArticleDOI: 10.18520/CS/V108/I5/848-859
10 Mar 2015-Current Science
Abstract: Remote sensing is being increasingly used in different agricultural applications. Hyperspectral remote sensing in large continuous narrow wavebands provides significant advancement in understanding the subtle changes in biochemical and biophysical attributes of the crop plants and their different physiological processes, which otherwise are indistinct in multispectral remote sensing. This article describes spectral properties of vegetation both in the optical and thermal range of the electromagnetic spectrum as affected by its attributes. Different methods have been discussed to reduce data dimension and minimize the information redundancy. Potential applications of hyperspectral remote sensing in agriculture, i.e. spectral discrimination of crops and their genotypes, quantitative estimation of different biophysical and biochemical parameters through empirical and physical modelling, assessing abiotic and biotic stresses as developed by different researchers in India and abroad are described.

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64 Citations


Open accessJournal ArticleDOI: 10.18520/CS/V108/I5/879-891
D. Ramakrishnan1, Rishikesh Bharti1Institutions (1)
10 Mar 2015-Current Science
Abstract: This article reviews the potential of Hyperspectral Remote Sensing (HRS) technique in various geological applications ranging from lithological mapping to exploration of economic minerals of lesser crustal abundance. This work updates understanding on the subject starting from spectroscopy of minerals to its application in exploring mineral deposits and hydrocarbon reservoirs through different procedures such as atmospheric correction, noise reduction, retrieval of pure spectral endmembers and unmixing. Besides linear unmixing, nonlinear unmixing and parameters attributed to nonlinear behaviour of reflected light are also addressed. A few case studies are included to demonstrate the efficacy of this technique in different geological explorations. Finally, recent developments in this field like ultra spectral imaging from unmanned aerial vehicles and its consequences are pointed out.

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Topics: Hyperspectral imaging (55%), Spectral imaging (52%)

40 Citations


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