Spectral Feature Fitting Research Articles

The Herschel SPIRE Fourier Transform Spectrometer Spectral Feature Finder – IV. Neutral carbon detection in the SPIRE FTS spectra

ABSTRACT The SPIRE Fourier Transform Spectrometer (FTS) Spectral Feature Finder (FF), developed within the Herschel Spectral and Photometric Imaging Receiver (SPIRE) FTS instrument team, is an automated spectral feature fitting routine that attempts to find significant features in SPIRE FTS spectra. The 3P1–3P0 and 3P2–3P1 neutral carbon fine structure lines are common features in carbon-rich far-infrared astrophysical sources. These features can be difficult to detect using an automated feature detection routine due to their typically low amplitude and line blending. In this paper, we describe and validate the FF subroutine designed to detect the neutral carbon emission observed in SPIRE spectral data.

Comparative analysis of mineral mapping for hyperspectral and multispectral imagery

The traditional approaches of mineral-mapping are time consuming and expensive process. Remote sensing is a tool to map the minerals precisely using their physical, chemical and optical properties. In the present study, Tirunelveli district in Tamil Nadu is selected to extract the abundant mineral such as Limestone using Hyperion and Landsat-8 OLI imageries. The chemical composition of the mineral is identified using scanning electron microscope (SEM) and energy dispersive X-ray spectroscopy (EDS) analysis. The spectral reflectance of minerals is characterized using analytical spectral device (ASD) field spectroradiometer. The minerals showed deep absorption in short wave infrared region from 1800 to 2500 nm. The mineral mapping in hyperspectral data is performed using various preliminary processing such as bad band removal, vertical strip removal, radiance and reflectance generation and postprocessing steps such as data dimensional reduction, endmember extraction and classification. To improve the classification accuracy, the vertical strip removal process is performed using a local destriping algorithm. Absolute reflectance of Hyperion and Landsat-8 OLI (Operational Land Imager) imageries is carried out using the FLAASH (fast line-of-sight atmospheric analysis of hypercubes) module. Spectral data reduction techniques in reflectance bands performed using minimum noise fraction method. The noiseless reflectance bands spatial data reduced by the Pixel Purity Index method in the threshold limit of 2.5 under 10,000 repetitions. The obtained reflectance imagery spectra compared with the spectral libraries such as USGS (United States Geological Survey), JPL (Jet Propulsion Laboratory) and field spectra. Endmembers of minerals are carried out using high probability score obtained from the various methods such as SAM (spectral angle mapper), SFF (spectral feature fitting) and BE (binary encoding). The mineral mapping of both imageries is carried out using a supervised classification approach. The results showed that hyperspectral remote sensing performed good results as compared to multispectral data.

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

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.

Mineral Identification and Mapping by Synthesis of Hyperspectral VNIR/SWIR and Multispectral TIR Remotely Sensed Data With Different Classifiers

Hyperspectral data, which have fine continuous spectrum, have been recognized to be more suitable for the detailed identification and classification of land surface, especially for minerals. The combination of the hyperspectral visible/near-infrared (VNIR) and shortwave infrared (SWIR) data with the hyperspectral thermal infrared (TIR) data is proven to be an effective way. However, how those effects are and what are the effects of introduction of multispectral TIR data on the minerals identification and classification are not well studied. To fully evaluate those effects, this article tries to use both simulated data and real data to testify the practicability of introduction of multispectral TIR data for the accuracies of mineral identification and classification. Four classifiers, i.e., spectral angle mapping, spectral feature fitting, orthogonal subspace projection, and adaptive coherence/cosine estimator, are selected in the experiment. Compared with the results using hyperspectral data alone, the introducing of multispectral TIR data in identification and classification has improved accuracies for both the simulated and real data. The overall accuracies are improved about 4%–13% for the simulated data and about 1%–5% for the real data by using different classifiers. Those improvements prove that the spectral diagnosed characteristics in TIR region even for multispectral data help identify and classify minerals. Although the improvements for real data are not well obvious due to the low spatial resolution, the multispectral TIR data are still effective supplements for hyperspectral VNIR and SWIR data in mineral identification and classification.

Reflecting on siliceous rocks in central Australia: Using advanced remote sensing to map ancient “tool‐stone” resources

AbstractHyMap™ airborne hyperspectral imagery was used to discriminate and map hydrated silica mineralization in the Dalhousie Springs area of central Australia. A spectral feature fitting algorithm was used to match laboratory reference spectra with image pixel spectra, producing a scaled goodness‐of‐fit raster map of silicified “tool‐stone” sources in our study area. Subsequent fieldwork indicated that the algorithm mapped silcrete, a rock composed of hydrated silica, and incidentally, the most frequently utilized raw material for stone artifact manufacture in this area of the Australian arid zone. The soundness of our hydrated silica mineralization map is supported with field observations and spectroscopic analysis of collected silcrete samples. Independent siliceous rock mapping produced by the Commonwealth Scientific and Industry Research Organization offers additional corroboration of our results. Based on the success of our approach, we suggest that archaeologists working in Australia and in similar arid environments elsewhere have much to benefit in using advanced remote sensing products to map lithic resources, including time and cost‐saving advantages for field logistics, enriched assessments of land suitability for archaeological site types, and an improved understanding of resource distributions.

Evaluation of AVIRIS-NG hyperspectral images for mineral identification and mapping

Advancement of airborne hyperspectral remote sensing techniques provides subtle variations to identify minerals and to make distinctions between rock formations. These techniques clearly define barren land versus economically viable zones containing ores and minerals. As we know profitable mineral zones are commonly associated with hydrothermal alteration zones, hyperspectral remote sensing techniques have the capability to identify and distinguish between altered, weathered, and clay minerals. In this research work, airborne hyperspectral remote sensing image from AVIRIS-NG is used to identify hydrothermally altered minerals in the Jahajpur region of Bhilwara district, Rajasthan, India. The purpose of this study is the identification of the minerals through spectral features of image spectra in corroboration with field sample spectra and USGS laboratory spectra. The spectral angle mapper (SAM) and Spectral Feature Fitting (SFF) algorithms were used for the mapping of the minerals. This study was conducted to prove the capability of AVIRIS-NG hyperspectral remote sensing data to identify zones of profitable mineral deposits.

Generation of an efficient structural evidence layer for mineral exploration targeting

Generation of enhanced and augmented evidence maps corresponding to operation of ore-forming geological processes improves mineral exploration success. Structural lineaments have a substantial impact on such processes. Therefore, precise recognition of the lineaments facilitates mineral exploration targeting toward undiscovered mineralized zones. The paper aims 1) to recognize and analyze structural lineaments, especially those which are spatially and genetically associated with orogenic gold mineral system, through analysis of a multi-disciplinary geospatial dataset including aeromagnetic geophysics, the Advanced Spaceborne Thermal Emission, and Reflection Radiometer imagery and Digital Elevation Model data and 2) to generate an efficient structural evidence layer for exploration targeting. The methods applied are edge enhancement filters with line detection algorithms. The detection algorithm, Hough Transform, and total horizontal derivative of upward continued filter and its tilt derivative) were utilized to extract structural lineaments. Evaluation of the spatial association of known Au mineralization in the study area with the recognized structural features demonstrated that abundance, density, and trend of such features strongly control the localization of Au mineralization. In addition, band ratio, principal component analysis, spectral angle mapper, and spectral feature fitting were applied to map and generate mineralization-related alteration layer. The evidence layers were then integrated through the conventional TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method to delineate reliable exploration targets.

Determination of Hydrothermal Alteration Zones Using Remote Sensing Methods in Tirka Area, Toroud, NE Iran

The aim of current study is the identification of hydrothermal alteration by remote sensing methods in Tirka area, NE Iran. The field of study with area about 13 km2 (scale: 1:20,000) is part of the Toroud–Chahshirin magmatic belt, which is located in central Iran structural zone. Hydrothermal alteration zones are interpreted based on Advanced Spaceborne Thermal Emission and Reflection Radiometer remote sensing data. In the first stage, atmospheric, topographic and geometric corrections were performed on satellite imagery. Then, different image interpretation techniques consisting of band ratio, principal component analysis, minimum noise fraction, least squares fitting (LS-Fit), spectral angle mapper and spectral feature fitting (SFF) methods were used. Therefore, VNIR bands for iron oxide alteration, SWIR bands for argillic, phyllic and propylitic alterations, and TIR bands for silicification were applied. The results showed iron oxide and propylitic alterations occurred at the SW and the argillic and phyllic alteration zones were existed in the northern and SE parts of the area. Silicification happened sporadically in the region. Lineaments were processed by false color composite, high-pass filters and hill-shade digital elevation model methods, and two NW–SE and NE–SW major trends were revealed. Field observation and laboratory analysis were used for accuracy of satellite studies. Finally, alteration and lineament maps were integrated for the recognition of high-potential mineralization. Petrographic studies showed that SFF and LS-Fit methods are more reliable than other techniques for the determination of hydrothermal alterations.

Airborne hyperspectral data for mineral mapping in Southeastern Rajasthan, India

Geology majorly deals with the structures, features, minerals, rocks, etc. of the planet Earth. Geologists are using the technology of remote sensing for structural interpretation and regional mapping. Ores and minerals identification are also done with the help of remote sensing. This paper is mainly focused on the identification of minerals and mapping with the help of various algorithms such as Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) using airborne hyperspectral data. Minerals are identified on the basis of the visible and near-infrared spectral reflectance. Spectral reflectance is having absorption features at different positions and absorption peaks are used for the analysis of imagery. This technique provides surface mineralogical details. SAM algorithm mainly computes the angle between the unknown pixel spectrum and unique pixel spectrum. SFF algorithm matched the continuum removed the spectrum of the imagery pixel from the continuum removed reference spectra. MTMF algorithm detects the abundance of the minerals and removes the erroneous positive. Total 13 endmembers (minerals) were identified in the study area. These minerals are grouped into clay minerals, iron minerals, carbonate minerals, and other minerals. These endmembers are used for the mineral map creation from different algorithms. Algorithms produce the diverse kind of mineral map and these are compared with each other on the basis of the mineralogy and discrimination of mineralized region from settlements. Mineral map produced by MTMF algorithm provides convenient results with better accuracy than other algorithms.

Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area

Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant (SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set’s reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis (PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting (SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.

Identification of hydrocarbon microseepage induced alterations with spectral target detection and unmixing algorithms

Hydrocarbon micro and macro seeps alter chemical and mineral composition of the Earth’s surface, providing prospects for detection with remote sensing tools. There have been several studies focusing on mapping these anomalies by utilizing ever evolving multispectral and hyperspectral imaging instruments, which has proven their capacity for mapping both hydrocarbons and hydrocarbon-induced alterations so far. These studies broadly comprise of methods like calculating band ratios, spectral angle mapping, spectral feature fitting, and principal component analysis as detection techniques. However, there is a lack of concentration on advanced signature based detection algorithms and unmixing methods for mapping surface manifestations of hydrocarbon microseeps. Signature based detection algorithms utilize target spectra to correlate with each pixel’s spectrum in order to allocate possible target locations. Unmixing methods, on the other hand, require no input spectra beforehand, aiming to resolve each pixel’s spectral constituents and their corresponding abundance fractions. In this paper, the potential of all these methods in mapping microseepage related anomalies are evaluated by implementing and comparing them for Gemrik Anticline, one of the prospective hydrocarbon exploration fields in Turkey. Hence, it provides a complete knowledge on determination surface manifestations of hydrocarbon microseeps with the help of well known supervised target detection algorithms and hyperspectral unmixing algorithms. The study area is located in the Southeastern Anatolia, between the cities of Adıyaman and Şanlıurfa. The spectral signatures were collected with Analytical Spectral Devices Inc. (ASD) spectrometer during the field studies conducted by Avcıoğlu (2010), to be utilized as an input to the signature based detection algorithms as well as a reference to select the related abundance map among the outputs of unmixing methods. Advanced Space Borne Thermal Emission and Radiometer (ASTER) image of the study region, with an atmospheric correction before running the algorithms, is selected for the applications. Among the applied algorithms, Simplex Identification via Split Augmented Lagrangian (SISAL) is selected as a base of comparison, as it possess minimum calculated error metrics in the experiments. Another unmixing method, the Minimum Volume Simplex Algorithm (MVSA), and signature-based techniques, Desired Target Detection and Classification Algorithm (DTDCA) & Spectral Matched Filter (SMF) follow the success of the SISAL, respectively. The Crosta technique, which is performed as a conventional approach for experimental comparisons, has also shown its capability, succeeding these algorithms. The study provides an overall assessment for methodologies to be used for hydrocarbon microseepage mapping, which also serves guidance for further exploration studies in the region. The potential of ASTER data for hydrocarbon-induced alterations is also emphasized as a cost effective tool for the future applications.

Mapping the distribution of iron ore minerals and spatial correlation with environmental variables in hilltop mining areas

The prime contribution of this assignment was to examine the hyperspectral remote sensing, based on iron ore minerals identification using spectral angle mapper (SAM) technique. Correlation analyses between field iron contents and environmental variables (soil, water, and vegetation) have been performed. Spectral feature fitting (SFF) and multi-range spectral feature fitting (MRSFF) methods were used for accuracy assessment in extracting iron ore minerals from Hyperion EO-1 data. Spectral inspections as a reference were used in SAM technique for image classification for iron ore minerals: Hematite (24.26%), Goethite (32.98%) and Desert (42.76). Iron ore minerals classification is justified by the United States Geological Survey (USGS) spectral library and field sample points. The regression analysis of USGS and Hyperion reflectance spectra has shown the moderate positive correlation. The regression analyses between iron ore contents and environmental parameters (soil, water, and vegetation) have shown the moderate negative correlation. The examination was significantly effectual in extracting iron ore minerals: Hematite (SFF RMSE ≤ 0.51 MRSFF RMSE ≤ 0.48), Goethite (SFF RMSE ≤ 0.047 MRSFF RMSE ≤ 0.438) and Desert (SFF RMSE ≤ 0.63 and MRSFF RMSE ≤ 0.50); and the MRSFF RMSE histograms indicate the above result likened to a conventional SFF RMSE. MRSFF RMS error result is best because multiple absorption features typically characterize spectral signatures. This analysis demonstrates the potential applicability of the methodology for iron minerals identification framework and iron minerals impact on environmental parameters.

Exploration of Kahang porphyry copper deposit using advanced integration of geological, remote sensing, geochemical, and magnetics data

The purpose of mineral exploration is to find ore deposits. The main aim of this work is to use the fuzzy inference system to integrate the exploration layers including the geological, remote sensing, geochemical, and magnetic data. The studied area was the porphyry copper deposit of the Kahang area in the preliminary stage of exploration. Overlaying of rock units and tectonic layers were used to prepare the geological layer. ASTER images were used for the purpose of recognition of the alterations. The processes used for preparation of the alteration layer were the image-based methods including RGB, band ratio, and principal component analysis as well as the spectrum-based methods including spectral angel mapper and spectral feature fitting. In order to prepare the geochemical layer, the multivariate statistical methods such as the Pearson correlation matrix and cluster analysis were applied on the data, which showed that both copper and molybdenum were the most effective elements of mineralization. Application of the concentration-number multi-fractal modeling was used for geochemical anomaly separation, and finally, the geochemical layer was obtained by the overlaying of two prepared layers of copper and molybdenum. In order to prepare the magnetics layer, the analytical signal map of the magnetometry data was selected. Finally, the FIS integration was applied on the layers. Ultimately, the mineral potential map was obtained and compared with the 33 drilled boreholes in the studied area. The accuracy of the model was validated upon achieving the 70.6% agreement percentage between the model results and true data from the boreholes, and consequently, the appropriate areas were suggested for the subsequent drilling.

Mapping of Aluminous Rich Laterite Depositions through Hyper Spectral Remote Sensing

Increased dimensionality of the satellite data proves to be very useful for discriminating features with very close spectral matching. Present study concentrates on the retrieval of reflectance spectra from the level one radiometrically corrected data in Koraput district (Orissa) for the Bauxite ore. In the present study, atmospheric correction model FLAASH has been used to retrieve reflectance from the radiance data. Preprocessing of the dataset has been done before applying atmospheric correction on the dataset. Spectral subsetting of noise prone bands has been successfully done. Local destriping of the affected bands has been done using a 3*3 local mean filter. Spectral signatures of samples were derived from the processed data. Spectral signature of each sample and derived features vectors were correlated with the satellite image of the area and distribution of each feature was demarcated. Spatial abundance of each feature was used in preparation of mineral abundance map. Accuracy of the map was assessed using training sets of representative geological units. The mineral abundance mapping using the spectral analysis of the reflectance image involves the endmember collection using the N-Dimensional visualizer tool in ENVI software. Laterite, Bauxite, Iron and silica rich Aluminous laterite soil, Alluvium and Forest were selected as the end members after understanding the geology and analysis of the reflectance image. Various mapping techniques were applied to generate the final classified mineral abundance Map, Linear Spectral Unmixing, Mixture Tune Matched Filtering, Spectral Feature Fitting, Spectral Angle Mapper were the techniques used. Results have revealed the ability of Hyper spectral Remote sensing data for the identification and mapping of Hydrothermal altered products like Bauxite, Aluminous Laterite. This technology can be utilized for targeting minerals in the altered zone.

Mapping Mangrove Species Using Hyperspectral Data: A Case Study of Pichavaram Mangrove Ecosystem, Tamil Nadu

There are many studies on mangrove mapping, zonal demarcation, landuse and land cover changes, and also topics like its loss and restoration using multispectral data. In the recent past, advanced remote-sensing data have been used for species discrimination, mapping, etc. The current research aims at identifying and mapping mangroves species along Pichavarm coast of Tamil Nadu, India, using hyperspectral remote-sensing data. The study attempts to map the species by generating the reference spectra from the existing reports and research papers, as surrogate to expensive field work in conjunction with Hyperion data of January 2013. Image was pre-processed followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analysed by visualizing it in n-dimensions for end-member extraction. There were eleven spectra taken from the end-members, which were matched with reference spectra. The spectra—matched—, have been used as an input for classification of data with classifiers like Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Spectral Information Diversion (SID) to identify and map mangroves species. Further to monitor the exact presence of the species at sub-pixel level, linear spectral un-mixing (LSU) was also performed. The study found SAM with LSU as the best approach for mangrove species mapping in Pichavaram coast.

Advanced Investigation of Remote Sensing to Geological Mapping of Zefreh Region in Central Iran

This study has tried to prove the ability of remote sensing techniques to extract information necessary for preparation of geological mapping of the earth’s surface using multi-spectral satellite images which are rich sources of Earth’s surface information. In this study, the surface geological mappings of Zefreh region have been investigated through ASTER, OLI, and IRS-PAN remote sensing data. To prepare the geological map, preprocessing steps and reducing noises from data using MNF algorithm were firstly carried out. Then a set of processing algorithms and image classification methods are included; the band rationing, color composite and pixel classification based on maximum likelihood, spectral and sub-pixel classification methods of spectral angle mapper (SAM), spectral feature fitting (SFF), linear spectral differentiation (LSU), hill-shade images and automatic lineament extraction were used. Confusion matrix was formed for all classified images through control points were randomly selected from 1:25,000 map of the region to determine the accuracy of obtained results, which indicated the maximum accuracy (up to 90%) of output images. Comparing the results obtained from these methods with the map prepared by ground operations confirmed accuracy results. Finally, the surface geology and fault map of Zafreh region was produced by combining detected geological formations and tectonic lineaments.

Ground-Based Hyperspectral Image Analysis of the Lower Mississippian (Osagean) Reeds Spring Formation Rocks in Southwestern Missouri

Ground-based hyperspectral imaging is fairly new for studying near-vertical rock exposures where airborne or satellite-based imaging fail to provide useful information. In this study, ground-based hyperspectral image analysis was performed on a roadcut, where diagenetic tripolite facies is observed in southwestern Missouri. Laboratory-based reflectance spectroscopy and hyperspectral image analyses were also performed on collected samples. Image classification was performed using Spectral Feature Fitting (SFF) and Mixture-tuned Match Filtering (MTMF) with laboratory- and image-derived end-member spectra. SFF provided thorough yet detailed classification, whereas MTMF provided information on the relative abundances of the lithologies. Ground-based hyperspectral imaging demonstrated its potential to aid geological studies providing valuable information on mineralogical and lithological variations rapidly and with two-dimensional continuity in inaccessible rock faces of near-vertical outcrops. The results showed decreasing tripolite abundance going downward in the investigated vertical succession. Also, a leaching pattern has been observed such that persistent and continuous limestone layers become lenses and patches towards the upper portion of the outcrop. These observations show that the effect of tripolitization decreases when going deeper in the succession, suggesting that the fluid responsible for the weathering of siliceous precursors may have been flowing from top to bottom and thus have had a meteoric origin.

Remote Sensing Exploration of Nb-Ta-LREE-Enriched Carbonatite (Epembe/Namibia)

On the example of the Epembe carbonatite-hosted Nb-Ta-LREE deposit, we demonstrate the use of hyperspectral reflectance data and geomorphic indicators for improving the accuracy of remote sensing exploration data of structurally-controlled critical raw material deposits. The results further show how exploration can benefit from a combination of expert knowledge and remotely-sensed relief, as well as imaging data. In the first stage, multi-source remote sensing data were used in lithological mapping based on Kohonen Self-Organizing Maps (SOM). We exemplify that morphological indices, such as Topographic Position Index (TPI), and spatial coordinates are crucial parameters to improve the accuracy of carbonate classification as much as 10%. The resulting lithological map shows the spatial distribution of the ridge forming carbonatite dyke, the fenitization zone, syenite plugs and mafic intrusions. In a second step, the internal zones of the carbonatite complex were identified using the Multi-Range Spectral Feature Fitting (MRSFF) algorithm and a specific decision tree. This approach allowed detecting potential enrichment zones characterized by an abundance of fluorapatite and pyroxene, as well as dolomite-carbonatite (beforsite). Cross-validation of the mineral map with field observations and radiometric data confirms the accuracy of the proposed method.

Hydrocarbon micro-seepage detection by altered minerals mapping from airborne hyper-spectral data in Xifeng Oilfield, China

Hydrocarbon micro-seepage can cause oxidation reduction reactions and produce altered minerals in surface sediments and soil. The typical altered minerals mapping by their diagnostic spectral features on hyper-spectral images is an important tool for the petroleum exploration industry. In this study, the airborne hyper-spectral data were used to investigate the altered minerals induced by hydrocarbon micro-seepages by spectral feature fitting (SFF) in the loess coverage area of Xifeng Oilfield. The results reveal that the distribution region of the altered minerals induced by hydrocarbon micro-seepage is larger than the known oilfield exploration area. The potential hydrocarbon micro-seepage region was also revealed by the distribution of altered minerals besides the known hydrocarbon area. A fast index was proposed by the absorption depths of clay and carbonate minerals for assessment of hydrocarbon micro-seepage. And it gave much clearer boundaries for the hydrocarbon micro-seepage in the loess coverage area than those by the altered mineral mapping. In addition, some field samples were analyzed by X-ray diffraction (XRD) and atomic absorption spectrophotometer to validate the results. Within the extents of hydrocarbon micro-seepage, there are lower contents of ferric iron and higher contents of carbonate minerals in these samples. Therefore, it is satisfactory to have the airborne hyper-spectral data to outline the extents of hydrocarbon micro-seepage for further hydrocarbon exploration in the loess coverage area.

Spatial distribution of altered minerals in the Gadag Schist Belt (GSB) of Karnataka, Southern India using hyperspectral remote sensing data

Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GSB) is carried out using Hyperion data of March 2013. The entire spectral range is processed with emphasis on VNIR (0.4–1.0 μm) and SWIR regions (2.0–2.4 μm). Processing methodology includes Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes correction, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) in conjunction with spectra collected, using an analytical spectral device spectroradiometer. A total of 155 bands were analysed to identify and map the major altered minerals by studying the absorption bands between the 0.4–1.0-μm and 2.0–2.3-μm wavelength regions. The most important and diagnostic spectral absorption features occur at 0.6–0.7 μm, 0.86 and at 0.9 μm in the VNIR region due to charge transfer of crystal field effect in the transition elements, whereas absorption near 2.1, 2.2, 2.25 and 2.33 μm in the SWIR region is related to the bending and stretching of the bonds in hydrous minerals (Al-OH, Fe-OH and Mg-OH), particularly in clay minerals. SAM and SFF techniques are implemented to identify the minerals present. A score of 0.33–1 was assigned for both SAM and SFF, where a value of 1 indicates the exact mineral type. However, endmember spectra were compared with United States Geological Survey and John Hopkins University spectral libraries for minerals and soils. Five minerals, i.e. kaolinite-5, kaolinite-2, muscovite, haematite, kaosmec and one soil, i.e. greyish brown loam have been identified. Greyish brown loam and kaosmec have been mapped as the major weathering/altered products present in soils and rocks of the GSB. This was followed by haematite and kaolinite. The SAM classifier was then applied on a Hyperion image to produce a mineral map. The dominant lithology of the area included greywacke, argillite and granite gneiss.