Mixture Tuned Matched Filtering Research Articles

Spectral imaging of grass species in arid ecosystems of Namibia

Grasslands across the African continent are under pressure from climate change and human activities, particularly in arid ecosystems. From a remote sensing perspective, these ecosystems have not received much scientific attention, especially in Namibia. To address this knowledge gap, various remote sensing methods were implemented using new generation spaceborne imaging spectrometers amongst others. Therefore, this research provides a first methodological approach aimed at mapping and evaluating the distribution of grasslands within two private nature reserves, namely, the NamibRand Nature Reserve (NRNR) and ProNamib Nature Reserve (PNNR) with surrounding farmlands on the edge of Namib Sand Sea. The multi-sensor approach utilizes Mixture Tuned Matched Filtering (MTMF) and incorporated spectral information collected in the field to analyze grasslands. The research involves a sensor comparison of multispectral Sentinel-2 and PlanetScope data, hyperspectral data from Environmental Mapping and Analysis Programme (EnMAP) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) and an additional data fusion product derived from Sentinel-2 and EnMAP imagery based on a Smoothing Filter-based Intensity Modulation Hypersharpening method (SFIM-HS). Additionally, a unique spectral library of collected field spectra was established and inter-species spectral separability and intra-species spectral homogeneity was analyzed. This library presents newly published spectra of individual species. Due to dry initial conditions, the calculated spectral separability of individual grasses is limited, making only a mean endmember feasible for partial unmixing. The validation results of satellite comparison show that data fusion products (R2 = 0.51 with Normalized Difference Vegetation Index (NDVI); R2 = 0.66 with Soil Adjusted Vegetation Index (SAVI)) are more suitable for mapping arid grasslands than multispectral or hyperspectral data (all R2 < 0.35). More research is required and potential methodological adjustments are discussed to further investigate the spatio-temporal dynamics of arid grasslands and to aid conservation efforts in the Greater Sossusvlei-Namib Landscape in line with the United Nations Decade of Restoration.

ESTIMATION OF MANGROVE FRACTIONAL COVER FROM MULTISPECTRAL AND HYPERSPECTRAL DATA USING MIXTURE TUNED MATCHED FILTERING

Abstract. Mangroves provide various ecosystem services and contribute to climate change adaptation being one of the blue carbon ecosystems. The extents of mangroves are mapped and monitored using commonly available multispectral images, such as Landsat and Sentinel-2, to detect and assess gains and losses. However, this presence or absence per pixel based on crisp classification or index thresholding offers limited information on the dynamics of mangrove growth or decline. In this paper, we evaluated the use of Mixture Tuned Matched Filtering (MTMF) in estimating mangrove fractional cover (MFC) from multispectral (Landsat-8) and hyperspectral (PRISMA) satellite images. We also examined the utility of the mangrove vegetation index (MVI) and enhanced MVI (EMVI) for this purpose. The images were first denoised using Minimum Noise Fraction (MNF). MTMF was then separately applied to the sets of MNF bands, excluding noise bands, to generate Matched Filtering (MF) Score and Infeasibility layers. The endmember (mangrove) spectrum was extracted from a pixel identified using pixel purity index (PPI) and examination of high-resolution Google Earth base image, from which detailed mangrove extents were also delineated. A 30-m vector grid file was created and populated with MFC, MF Score, and Infeasibility values using zonal analysis. Correlation analysis, exploratory regression, and ordinary least squares (OLS) regression were performed. MF Score is moderately and positively correlated with MFC. In contrast, Infeasibility, MVI, and EMVI are uncorrelated or very weakly correlated with MFC. MFC can be estimated using an OLS model with MF Score and Infeasibility as explanatory variables. The performance of the PRISMA-based model (R2Adj=0.30, AIC=98643.20) was found to be better than the Landsat-8-based model (R2Adj=0.36, AIC=97428.89).

Mapping alteration zones in the Southern section of Yulong copper belt, Tibet using multi-source remote sensing data

Methods for extracting mineralized alteration information using remote sensing images have recently become both efficient and cost-effective. Technology involving the extraction of alteration information based on multi-spectral data has been widely practiced and effectively verified. In recent years, research on fine mineral extraction methods based on hyperspectral data has also been rapidly developing. The Yulong copper belt is a porphyry copper belt located in China with high prospects for mineralization. However, most previous studies focused on the northern section of the Yulong copper belt, with limited exploration of the southern section. In this study, alteration information of the southern section of the Yulong copper belt was extracted from remote sensing data from Landsat-8 OLI, ASTER, and ZY1-02D, and the prospecting potential of this area was evaluated. Principal component analysis was used to extract iron oxide and hydroxyl alteration from Landsat-8 data, in addition to Al hydroxyl and propylitic alterations from ASTER data. Considering the challenge of the extraction of too many pseudo-anomalies using traditional methods, the mixture-tuned matched filtering (MTMF) method was used to more accurately extract iron oxide alterations. Regarding hyperspectral data, the minimum noise fraction and pure pixel index algorithms were used to extract white mica and carbonatite endmembers. The MTMF method was also used for alteration mapping, which took advantage of sub-pixel abundance mapping to finely divide the white mica and carbonatite alterations into five classes. The extraction results of multi-source remote sensing data were then compared and analyzed to avoid occasional single-image extraction results, which confirmed the superiority of the hyperspectral remote sensing and MTMF methods. Combined with field verification, the mineralization alteration information coincided with the spatial location of the Secuo, Mamupu, and Jicuo deposits, which confirmed the accuracy of alteration information extraction. The results of this study confirmed the application potential of remote sensing alteration information extraction in the field of mineral resource exploration. The results have important reference significance for further geological prospecting and exploration in the southern section of the Yulong copper belt.

Spectral mixture analysis of AVIRIS-NG hyperspectral data for material identification and classification for the part of Kolkata city

Identification and extraction of material in the urban scenario are always challenging due to the presence of different heterogeneous materials. Airborne hyperspectral sensors with the advantage of both high spectral and spatial resolution can be used in such areas for the identification and mapping of urban materials. Manual extraction of the endmembers in highly dense heterogeneous urban regions is a very difficult task to achieve, but with automated extraction techniques, it yields promising results. To counter the mixed pixels problem in heterogeneous urban areas automated endmember extraction techniques like Endmember Average RMS (EAR), Minimum Average Spectral Angle (MASA), and Count Based Endmember Selection (CoB) was used for pure endmember identification in the densely populated urban area of Kolkata region using AVIRIS NG imagery. Spectral Unmixing Multiple Endmember Spectral Mixture Analysis (MESMA), Linear Spectral Unmixing (LSU), and Mixture Tuned Matched Filtering (MTMF) was performed on AVIRIS NG data using the extracted endmembers of different urban materials. The rule-based classification was performed on abundance images obtained from MESMA, MTMF, and LSU techniques for comparative analysis. Classification using MESMA abundance images outperformed in mapping mixed urban environments with an overall accuracy (OA) of 88.6 % followed by the rule-based classified output of MTMF and LSU with OA of 75.27% and 72.45% respectively.

Mineral mapping in the Duolong porphyry and epithermal ore district, Tibet, using the Gaofen-5 satellite hyperspectral remote sensing data

Remote sensing technology is an exploration method that can directly map distribution of alteration minerals or zones associated with different types of mineral systems. Hyperspectral remote sensing can identify key pathfinder minerals and compositional variations within minerals. The Duolong porphyry-epithermal ore district, which has proven reserves of approximately 20 million tons of Cu and 420 tons of Au, is part of an emerging world-class metallogenic area in the western part of the Bangong Co-Nujiang metallogenic belt, Tibet, China. The Gaofen-5 hyperspectral (GF-HS) reflectance data were used to map the distribution patterns of hydrothermal alteration and to locate areas potentially containing mineral deposits in the Duolong ore district (DOD). Based on the mixture tuned matched filtering (MTMF) technique and the absorption feature wavelength positions of the minerals, an alteration map was generated using GF-HS data for mineral mapping in the DOD with sparse vegetation. The widely distributed advanced argillic alteration (alunite + pyrophyllite + kaolinite ± dickite), phyllic alteration (high content of Al-rich white mica), propylitic alteration (Mg-rich chlorite), and limited argillic alteration (kaolinite ± dickite ± white mica) were mapped in detail. The distribution patterns of alteration in known deposits were fairly consistent with the mineralogical information obtained in previous studies. In addition, we identified potential mineralization areas with characteristics similar to the Duobuza, Bolong and Naruo porphyry deposits.

Mapping of ferric (Fe3+) and ferrous (Fe2+) iron oxides distribution using ASTER and Landsat 8 OLI data, in Negash Lateritic iron deposit, Northern Ethiopia

ABSTRACT Iron plays an important role in industrial and engineering fields development of a country and as such there is an enormous demand for iron in Ethiopia. However, a search for this valuable primary mineral resource exploration remains challenging and costly. Therefore, this study aims to map iron oxide minerals using Landsat-8/operational land imager (OLI) and advanced space-borne thermal emission and reflection (ASTER) satellite imagery in Negash Lateritic iron deposit, Northern Ethiopia to ease the costs and reduce the time. Different image processing techniques such as band ratio, selective principal component analysis, linear spectral unmixing, and mixture-tuned matched filter were used to produce iron oxide maps. Minimum noise fraction (MNF), pixel purity index (PPI), and N-dimensional visualizer were also applied to extract endmembers in the automated spectral hourglass wizard. In addition to this, the enhanced image thresholding and scatter plot were used to map the potential areas. Ferric iron oxide band ratio of ASTER mapped maximum area of 62.1 km2 followed by a laterite band ratio of ASTER covering 57.8 km2. The result was validated using existing iron oxide polygons and the outcome obtained from selective PCA shows a strong match with the existing iron oxide polygons. The sub-pixel mapping techniques show poor accuracy in mapping goethite and hematite relative to the pixel level. Thus, it is evident from the results that ASTER mapped better than Landsat 8 OLI for band ratios of selective PCA, unmixing, MTMF, and mineralized areas while characterizing with limited fieldwork.

Application and Evaluation of Deep Neural Networks for Airborne Hyperspectral Remote Sensing Mineral Mapping: A Case Study of the Baiyanghe Uranium Deposit in Northwestern Xinjiang, China

Deep learning is a popular topic in machine learning and artificial intelligence research and has achieved remarkable results in various fields. In geological remote sensing, mineral mapping is an appealing application of hyperspectral remote sensing for geological surveyors. Whether deep learning can improve the mineral identification ability in hyperspectral remote sensing images, especially for the discrimination of spectrally similar and intimately mixed minerals, needs to be evaluated. In this study, shortwave airborne spectrographic imager (SASI) hyperspectral images of the Baiyanghe uranium deposit in Northwestern Xinjiang, China, were used as experimental data. Three deep neural network (DNN) models were designed: a fully connected neural network (FCNN), a one-dimensional convolutional neural network (1D CNN), and a one-dimensional and two-dimensional convolutional neural network (1D and 2D CNN). A sample dataset containing five minerals was constructed for model training and validation, which was divided into training, validation and test sets at a ratio of 6:2:2. The final test accuracies of the FCNN, 1D CNN, and 1D and 2D CNN were 91.24%, 93.67% and 94.77%, respectively. The three DNNs were used for mineral identification and mapping of SASI hyperspectral images of the Baiyanghe uranium mining area. The mapping results were compared with the mapping results of the support vector machine (SVM) and the mixture-tuned matched filtering (MTMF) method. Combined with the ground spectral data obtained by the spectrometer, spectral verification and interpretation were carried out on sections that the two kinds of methods identified differently. The verification results show that the mapping results of the 1D and 2D CNN were more accurate than those of the other methods. More importantly, for minerals with similar spectral characteristics, such as short-wavelength white mica and medium-wavelength white mica, the 1D and 2D CNN model had a more accurate discrimination effect than the other DNN models, indicating that the introduction of spatial information can improve the mineral identification ability in hyperspectral remote sensing images. In general, CNNs have good application prospects in geological mapping of hyperspectral remote sensing images and are worthy of further development in future work.

Forest Community Mapping Using Hyperspectral (CHRIS/PROBA) and Sentinel-2 Multispectral Images

The possibility to use hyperspectral images (CHRIS/PROBA) and multispectral images (Sentinel-2) in the classification of forest communities is assessed in this article. The pre-processing of CHRIS/PROBA image included: noise reduction, radiometric correction, atmospheric correction, geometric correction. Due to MNF transformation the number of the hyperspectral image channels was reduced (to 10 channels) and smiling errors were removed. Sentinel-2 image (level 2A) did not require pre-processing. Three tree genera occurring in the study area were selected for the classification: pine (Pinus), alder (Alnus) and birch (Betula). Image classification was carried out with three methods: SAM (Spectral Angle Mapper ), MTMF (Mixture Tuned Matched Filtering), SVM (Support Vector Machine). For the CHRIS/PROBA image, the algorithm SVM turned out to be the best. Its overall accuracy (OA) was 72%. The poorest result (OA = 52%) was for the MTMF classifier. In the classification of Sentinel-2 multispectral image the best result was for the MTMF method: OA = 82%, kappa coefficient 0.7. For other methods, the overall accuracy exceeded 65%. Among the classified genera, the highest producer’s accuracy was obtained for pine (PA = 96%), and the broad-leaf genera: alder and birch had PA ranging from 42% to 85%.

Interpretation of hydrothermal alteration and structural framework of the Huize Pb–Zn deposit, SW China, using Sentinel-2, ASTER, and Gaofen-5 satellite data: Implications for Pb–Zn exploration

The Huize Pb–Zn deposit is one of the largest and high–grade Pb–Zn deposits in the world, containing more than 7 Mt Pb and Zn reserves at an ore grade of ∼30–35 % Pb + Zn. Therefore, brownfield exploration of additional Pb and Zn orebodies at Huize Pb–Zn deposit is of high priority. Hydrothermal alteration minerals and structural features are typically associated with Pb–Zn mineralization, which can be identified quickly and economically using a multi-source remote sensing satellite dataset. In this study, Sentinel-2, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Gaofen-5 (GF-5) satellite remote sensing imagery were employed for brownfield exploration of additional Pb–Zn orebodies in the Huize Pb–Zn deposit. The Sentinel-2 satellite remote sensing imagery was used to interpret the structure and map oxides/hydroxides by implementing the band ratio (BR) and principal components analysis (PCA) method; the ASTER satellite data were applied to map argillic, quartz, and carbonate hydrothermal alteration minerals using the PCA method; the GF-5 satellite data were utilized to map hematite, kaolinite, calcite, and dolomite minerals by the mixture tuned matched filtering (MTMF) method; and the mapped hydrothermal alteration information was verified by field reconnaissance and laboratory analysis. Finally, remote sensing geological exploration characteristics of the Huize Pb–Zn deposit were summarized by comprehensively analyzing the hydrothermal alteration minerals and structural characteristics, and three new brownfield exploration target zones of Pb–Zn mineralization were delineated. The study can provide a reference for the brownfields exploration in the vicinity of an existing Pb–Zn deposit around the world.

Investigation of geochemical characteristics and hydrothermal alteration zone mapping supported by remote sensing of a porphyry copper prospect, southeastern Iran: integrated applications to exploration

The area discussed in this exploration methodology case study is situated along the Sistan Suture Zone where the granitoid suite is a mantle-derived multiphase intrusive complex. One of the characteristics of this region is the presence of large areas of sulfide-bearing, quartz-rich stockwork and pyritic veins. Geochemical findings show that it is limited to the calc-alkaline to shoshonitic series intrusions and is associated with a volcanic arc (I-type) formed within an active continental margin subduction setting. The associated intrusive complex has characteristics consistent with Cu productive porphyries, supported by high K-adakitic Sr/Y, La/Yb, Y and Al 2 O 3 /TiO 2 geochemical signatures. The stockwork mineralization includes the hypogene (chalcopyrite and bornite), with locally superimposed supergene (covellite, malachite, goethite and hematite) zones. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images (visible-near infrared (VNIR), and shortwave infrared (SWIR) bands) and mixture tuned matched filtering (MTMF) algorithm enable identification of each hydrothermal alteration type, especially where pictures were innovatively classified using a concentration–number (C–N) fractal method. Four alteration types are evident (i.e. phyllic, argillic and propylitic), as well as secondary (supergene) jarosite, that are associated with gossans, which are an indicator of the hypogene pyritic shell. The propylitic alteration envelopes the phyllic and argillic varieties, forming a belt around the pyritic shell; alteration assemblages were confirmed by X-ray powder diffraction (XRD) analysis. Finally, all results show a mineralization alteration pattern within the case study region that is similar to those of known porphyry copper and associated molybdenum- and gold-bearing systems in this region of Iran and worldwide.

Ground-based visible–near infrared hyperspectral imaging for monitoring cliff weathering of a volcanic island in Dokdo, South Korea

Coastal cliffs undergo erosion and weathering more rapidly under the influence of strong waves and sea winds, leading to stability and environmental conservation issues. Ground-based hyperspectral imaging is useful for the identification and geological interpretation of minerals or rocks in vertical outcrops that are difficult to confirm from an aerial view or through in situ investigation for safety reasons. High spatial and spectral resolutions of visible–near infrared (VNIR) sensors can be advantageous for detecting weathering in cliffs made of volcanic rocks; however, their potential is not well known. In this study, two classification techniques, mixture-tuned matched filtering (MTMF) and support vector machine (SVM), were applied to VNIR hyperspectral data of the cliff face of a volcanic island in Dokdo, South Korea, and the classification results were compared. Results show that SVM is superior to MTMF for the classification of volcanic rocks and weathering minerals. The distinction between volcanic rocks with similar compositions and textures deteriorated using both methods. The shading of the surface owing due to unevenness and stratification also affected the accuracy of classification. This study shows that ground-based VNIR hyperspectral image analysis is a powerful and an effective approach to predict possible geomorphological changes and safety on volcanic islands, as it can explore the weathering of sea cliffs and highlight potentially vulnerable locations.

Gold-related Alteration Zones Detection at the Um Balad Area, Egyptian Eastern Desert, using Remote Sensing, Geophysical, and GIS Data Analysis

The Um Balad area is a part of the Arabian Nubian Shield (ANS), which hosts various gold and base metal deposits. Gold can be prospected in the alterations and sheared zones in the widely exposed metagabbro-diorite rocks in the Um Balad area. In this regard, the image processing and analysis of the ASTER data is a rapid and efficient technique for such studies, where the structural pattern and associated lineaments are probable indicators of hydrothermal flow and activities developing in mineral-bearing alteration zones as targets for gold exploration. The Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Mixture Tuned Matched Filtering (MTMF) techniques have identified the alteration zones and the characterized altered minerals of each alteration. The automatic lineament extraction method was used by ASTER's visible and near-infrared bands to produce a lineament density map. Using gamma-ray spectrometry data, the potassium percent (K %) and hydrothermal alteration parameters (F-parameter and Th-normalization) were calculated. As well as ratio maps (eTh/K and eU/K) were used to determine the potassic and phyllic hydrothermal alteration. The model of exploration for gold-related alterations was developed in the Um Balad area, using multiple layers derived from remote sensing and gamma-ray spectrometry data through a GIS (Weighted Sum Overlay) approach. The probability of gold-related alterations was graded as low, medium, good, and very good. A final map has been generated showing the gold-potential alteration zones in four localities (Area-1, Area-2, Area-3, and Area-4). In these altered zones, valuable minerals such as sulphides and gold may be precipitated, making them ideal gold exploration sites. The field observations and spectral signature analyses of the collected samples from the various identified alterations supported the results of the remote sensing and aero-spectrometry data analysis. Therefore, these techniques have been suggested for similar investigations in other areas.

Airborne imaging spectrometer dataset for spectral characterization and predictive mineral mapping using sub-pixel based classifier in parts of Udaipur, Rajasthan, India

Imaging spectroscopy is used for the mapping of different minerals using their characteristics absorption depths. The present study covers the parts of the Udaipur region for mineral mapping with the help of a Mixture Tuned Matched Filtering (MTMF) soft classifier using the Airborne Visible Infrared Imaging Spectrometer – Next Generation dataset. The preprocessed and classified dataset by MTMF soft classifier identified carbonates, serpentine minerals, and alteration products of ultramafics, clay and mica minerals, and intimate mixture of kaolinite and muscovite in the region. These minerals show the characteristic absorption features at different wavelength positions of the SWIR region. The comparison of the image spectrum was carried out with the reference (mineral library) and laboratory collected reflectance spectral curves. The derived mineral map accurately mapped the minerals and discriminated the minerals belonging to a similar mineral class. Validation was done using field evidences, photomicrographs, XRD, laboratory spectral curves, and already published maps. The accuracy for the generated mineral map is 83.93%.

Band processing of Landsat 8-OLI multi-spectral images as a tool for delineating alteration zones associated with porphyry prospects: A case from Suyoc, Benguet, Philippines

The Mankayan Mineral District in Luzon is marked by several porphyry and epithermal mineralizations. The possibility of blind porphyry mineralization in Mangga, Basig, and Palidan was forwarded based on the results of recent ground magnetic surveys in Suyoc, Mankayan, Benguet. These areas exhibited the diagnostic magnetic anomaly patterns expected of porphyry copper deposits. This signature, a magnetic core surrounded by a non-magnetic rim, is due to the alteration haloes common to porphyry systems. The magnetic core corresponds to the potassic zone dominated by iron-oxide-bearing minerals (e.g., magnetite). Meanwhile, the abundance of non-magnetic clay minerals at the periphery of the mineralized region corresponds to propylitic-argillic alteration zones. Delineation of alteration zones using remote sensing was done to further support the suggested mineralization prospects. Mixture tuned matched filtering (MTMF) was carried out to reduce the effects of vegetation cover from Landsat 8 data. Principal Components Analysis (PCA) was applied to generate bands exploiting the greatest difference between the clay and magnetite alterations with background earth materials. A classified composite map depicts the altered regions. The three sites previously identified from magnetic anomalies also display the expected alterations. Another prospect in the Lapangan area is being proposed based on magnetic anomalies and mapped alterations.

Application of per-pixel and sub-pixel unmixing methods on ASTER images to map hydrothermal alterations in a highly metamorphosed terrain: a case of the Musina copper deposit field

This study focuses on mapping hydrothermal alterations in Musina Copper Deposit Field which is characterized by unique concentric zonation of hydrothermal alterations from an outer zone of sericitization (phyllic) followed by inward zones of chloritization and epidotization (propylitic). Per-pixel and mixture tuned matched filtering (MTMF) sub-pixel unmixing methods were applied on ASTER data to map hydrothermal alterations. False colour composite (FCC) generated from combination of propylitic, phyllic and host rock index was effective in delineating alteration zones. The spatial distribution of alteration group minerals: chlorite–epidote and sericite–muscovite from MTMF classification correspond with their associated alteration zones. Correlation coefficient (R 2) was utilized to assess accuracy of the results by comparing MF scores of alteration minerals with copper concentrations. R 2 was 0.78 for chlorite–epidote and 0.69 for sericite– muscovite. Copper concentration levels increase towards chlorite–epidote region which represents the inward propylitic zone. Following the geometry and alteration zonation, follow-up targets were generated.

Hydrothermal alteration mapping using ASTER data, Takab-Baneh area, NW Iran: a key for further exploration of polymetal deposits

A large number of polymetallic deposits exist in the Takab-Baneh area, NW Iran. Different methods such as minimum noise fraction (MNF), pixel purity index (PPI), n-D visualization, mixture tuned matched filtering (MTMF), relative absorption band depth (RBD), band ratio (BR), and principal component analysis (PCA) were used for extraction of alteration zone characteristics from ASTER data in the Takab-Baneh area. The overall accuracy of MTMF is around 97% for phyllic, and 98% for argillic propylitic, which validates the potential and suitability of ASTER imagery for classification of alteration type. Subsequently, the alteration maps were confirmed by ground-truthing, i.e., field observation and petrographic studies. The results show a wide distribution of three main phyllic, argillic and propylitic alteration zones, that can be considered as targets for future exploration in NW Iran.

Impact of Image-Processing Routines on Mapping Glacier Surface Facies from Svalbard and the Himalayas Using Pixel-Based Methods

Glacier surface facies are valuable indicators of changes experienced by a glacial system. The interplay of accumulation and ablation facies, followed by intermixing with dust and debris, as well as the local climate, all induce observable and mappable changes on the supraglacial terrain. In the absence or lag of continuous field monitoring, remote sensing observations become vital for maintaining a constant supply of measurable data. However, remote satellite observations suffer from atmospheric effects, resolution disparity, and use of a multitude of mapping methods. Efficient image-processing routines are, hence, necessary to prepare and test the derivable data for mapping applications. The existing literature provides an application-centric view for selection of image processing schemes. This can create confusion, as it is not clear which method of atmospheric correction would be ideal for retrieving facies spectral reflectance, nor are the effects of pansharpening examined on facies. Moreover, with a variety of supervised classifiers and target detection methods now available, it is prudent to test the impact of variations in processing schemes on the resultant thematic classifications. In this context, the current study set its experimental goals. Using very-high-resolution (VHR) WorldView-2 data, we aimed to test the effects of three common atmospheric correction methods, viz. Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH); and two pansharpening methods, viz. Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on thematic classification of facies using 12 supervised classifiers. The conventional classifiers included: Mahalanobis Distance (MHD), Maximum Likelihood (MXL), Minimum Distance to Mean (MD), Spectral Angle Mapper (SAM), and Winner Takes All (WTA). The advanced/target detection classifiers consisted of: Adaptive Coherence Estimator (ACE), Constrained Energy Minimization (CEM), Matched Filtering (MF), Mixture-Tuned Matched Filtering (MTMF), Mixture-Tuned Target-Constrained Interference-Minimized Filter (MTTCIMF), Orthogonal Space Projection (OSP), and Target-Constrained Interference-Minimized Filter (TCIMF). This experiment was performed on glaciers at two test sites, Ny-Ålesund, Svalbard, Norway; and Chandra–Bhaga basin, Himalaya, India. The overall performance suggested that the FLAASH correction delivered realistic reflectance spectra, while DOS delivered the least realistic. Spectra derived from HCS sharpened subsets seemed to match the average reflectance trends, whereas GS reduced the overall reflectance. WTA classification of the DOS subsets achieved the highest overall accuracy (0.81). MTTCIMF classification of the FLAASH subsets yielded the lowest overall accuracy of 0.01. However, FLAASH consistently provided better performance (less variable and generally accurate) than DOS and QUAC, making it the more reliable and hence recommended algorithm. While HCS-pansharpened classification achieved a lower error rate (0.71) in comparison to GS pansharpening (0.76), neither significantly improved accuracy nor efficiency. The Ny-Ålesund glacier facies were best classified using MXL (error rate = 0.49) and WTA classifiers (error rate = 0.53), whereas the Himalayan glacier facies were best classified using MD (error rate = 0.61) and WTA (error rate = 0.45). The final comparative analysis of classifiers based on the total error rate across all atmospheric corrections and pansharpening methods yielded the following reliability order: MXL > WTA > MHD > ACE > MD > CEM = MF > SAM > MTMF = TCIMF > OSP > MTTCIMF. The findings of the current study suggested that for VHR visible near-infrared (VNIR) mapping of facies, FLAASH was the best atmospheric correction, while MXL may deliver reliable thematic classification. Moreover, an extensive account of the varying exertions of each processing scheme is discussed, and could be transferable when compared against other VHR VNIR mapping methods.

ASTER and GF-5 Satellite Data for Mapping Hydrothermal Alteration Minerals in the Longtoushan Pb-Zn Deposit, SW China

Hydrothermal alteration minerals are an effective prospecting indicator. Advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite data are some of the most commonly adopted multispectral data for the mapping of hydrothermal alteration minerals. Compared to multispectral data, hyperspectral data have stronger ground object recognition ability. Chinese Gaofen-5 (GF-5) is the first hyperspectral satellite independently developed by China that has the advantages of both wide-width and high-spectral-resolution technology. However, the mapping ability of GF5 data for hydrothermal alteration minerals requires further study. In this study, ASTER and GF-5 satellite data were implemented to map hydrothermal alteration minerals in the Longtoushan Pb-Zn deposit, SW China. Selective principal component analysis (SPCA) technology was employed to map iron oxide/hydroxides, argillic, quartz, and carbonate minerals at the pixel level using ASTER data, and the mixture tuned matched filtering (MTMF) method was implemented for the extracted hematite, kaolinite, calcite, and dolomite at the sub-pixel level using GF-5 data. When mapping the hydrothermal alteration minerals, the distribution features of the hydrothermal alteration minerals from the Longtoushan Pb-Zn deposit were systematically revealed. A comprehensive field investigation and petrographic study were conducted to verify the extraction accuracy of the hydrothermal alteration minerals. The results showed that the overall accuracies for the ASTER and GF-5 data were 82.6 and 92.9 and that the kappa coefficients were 0.78 and 0.90, respectively. This indicates that the GF-5 data are able to map hydrothermal alteration minerals well and that they can be promoted as a hyperspectral data source for mapping systematic hydrothermal alteration minerals in the future.

Integrating remote sensing data, GIS analysis and field studies for mapping alteration zones at Wadi Saqia area, central Eastern Desert, Egypt

Remote sensing data, spectral analyses, Geographic Information System (GIS) tools and field work were integrated for detecting the spatial distribution of the alteration zones in the study area. Remote sensing studies including; spectral signatures analyses and satellite image processing, were used for identifying the alteration minerals setting of the study area then extracting the alteration zones. A field study was carried out to the study area and several tens of samples were collected. The spectral behavior of the collected samples was obtained using the “Advanced Spectral Devices (ASD) TerraSpec Halo mineral identifier” handheld spectro-radiometer. Spectral analyses were carried out on signatures of selected samples to obtain the characterizing absorption features and to reveal the identity of the alteration minerals (end-members) representing the alteration zones in the area. The identified end-members included; Clay minerals, white Mica, Epidote and Iron oxides. Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), Constrained Energy Minimization (CEM), and Mixture Tuned Matched Filtering (MTMF) alteration mapping techniques were used to map the abundance of the identified alteration minerals from ASTER satellite image data. Lineaments were extracted automatically using combined hill-shade images obtained at multi-azimuths derived from Allos PALSAR 12.5 m Digital Elevation Model (DEM) data. The alteration and lineament data were integrated together using the GIS “Weighted Overlay Analysis” tool to delineate the most potential areas for alteration zones existence. The results were validated through plotting the field observation points on the resulted final alteration map; the accuracy was estimated by about 80%.

Testing ASTER and Sentinel-2 MSI Images to Discriminate Igneous and Metamorphic Rock Units in the Chadormalu Paleocrater, Central Iran

In the last fifty years, satellite images have been used to map the Earth’s surface at a variety of scales. Two satellite multispectral sensors (Sentinel-2 MSI and ASTER) have great utility for lithological discrimination in areas of good rock exposures. This study was conducted in order to test the ability of these sensors to discriminate igneous and metamorphic lithologies in the Chadormalu paleocrater and evaluate the image types and processing methodologies. The MNF (Minimum Noise Fraction) transform, Mathematical Evaluation Method (MEM), Spectral Angle Mapper (SAM), Mixture Tuned Matched Filter (MTMF), and band ratios were performed on near and short wave infrared ASTER and Sentinel-2 bands. Comparison of the results from several methods demonstrates that the MEM method can detect lithological units with very low false detection and better matching with ground truth data. Moreover, this study indicates that the results produced by the MEM algorithm on Sentinel-2 MSI data are more accurate than the results produced with ASTER data in the same area. Therefore, the MEM algorithm seems to be well suited for image classification involving multispectral databases such as ASTER and Sentinel-2 images.