多光谱遥感影像的SAM-SID混合分类技术研究

以SPOT 5多光谱影像为数据源,通过与SAM、SID以及常规的最大似然法(ML)和最小距离法(MD)的对比,研究了基于SAM-SID混合法的土地覆盖多光谱遥感分类技术。研究结果显示,相比于SAM和SID,SID(TAN)和SID(SIN)两个SAM-SID混合参量对多光谱影像上地物识别的能力更强,尤以SID(SIN)的识别能力最强;基于SID(SIN)的多光谱遥感分类验证精度达78.94%,不但明显高于SAM和SID法,而且也高于常规的MD和ML监督分类方法。这说明SAM-SID混合分类方法不但适用于高光谱遥感分类,同时在多光谱遥感分类中也有很强的适用性。     

A new hybrid spectral similarity measure for discrimination among Vigna species

The reflectance spectrum of species in a hyperspectral data can be modelled as an n-dimensional vector. A spectral angle mapper (SAM) computes the angle between the vectors that is used to discriminate the species. Spectral information divergence (SID) models the data as a probability distribution so that the spectral variability between the bands can be extracted using stochastic measures. The hybrid approach of the SAM and SID is found to be a better discriminator than the SAM or SID on their own. The spectral correlation angle (SCA) is computed as a cosine of the angle of the Pearsonian correlation coefficient between the vectors. The SCA is a better measure than the SAM as it considers only standardized values of the vector rather than the absolute values of the vector. In the present article, we propose a new hybrid measure based on the SCA and the SID. The proposed method has been compared with the hybrid approach of the SID and SAM for discriminating species belonging to Vigna genus using measures such as relative spectral discriminatory power, relative discriminatory probability and relative discriminatory entropy in different spectral regions. Experimental results using the laboratory spectra show that the proposed method gives higher relative discriminatory power in the 400–700 nm spectral region.     

Comparison of multi-temporal ASTER images for hydrothermal alteration mapping using a fractal-aided SAM method

The shortwave infrared (SWIR) spectral bands of four multi-temporal images acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on NASA’s Terra platform were analysed for evaluating the effects of acquisition properties and atmospheric pre-processing levels on the resulting hydrothermal alteration maps a using the fractal-aided Spectral Angle Mapper (SAM) method. Three ASTER level-1B products covering the Sar Cheshmeh area in Iran were used for hydrothermal alteration mapping. These images were converted to surface reflectance using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) method. The low reflectance of band 5 of the level-1B products was compensated for by using the spectra of collected rock samples. Level-2 (AST2B05S) SWIR ASTER images that had already been processed were also used. Reference spectra of the main hydrothermal alteration types were extracted for each product. The threshold angles were determined using the real value–area (RV–A) fractal technique. Then, SAM classification was carried out to map hydrothermal alteration for every product. It is concluded that the level-1B products that had been converted to reflectances have a better spectral contrast than the AST2B05S product. Summer images with lower tilt angle and higher solar elevation should be used to increase the accuracy of the image classification and minimize the effect of vegetation on the spectra of index minerals. By comparing the resulting hydrothermal alteration maps with known alteration types using a confusion matrix, it was shown that the application of the RV–A fractal technique to produce less biased threshold angles increases the accuracy of SAM classification.     

Airborne hyperspectral discrimination of tree species with different ages using discrete wavelet transform

In this article, the capability of discrete wavelet transform (DWT) to discriminate tree species with different ages using airborne hyperspectral remote sensing is investigated. The performance of DWT is compared against commonly used traditional methods, i.e. original reflectance and first and second derivatives. The hyperspectral data are obtained from Thetford forest of the UK, which contains Corsican and Scots pines with different ages and broadleaved tree species. The discrimination is performed by employing three different spectral measurement techniques (SMTs) including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and a combination of SAM and SID. Five different mother wavelets with a total of 50 different orders are tested. The wavelet detail coefficient (CD) from each decomposition level and combination of all CDs plus the approximation coefficient from the final decomposition level (C-All) are extracted from each mother wavelet. The results show the superiority of DWT against the reflectance and derivatives for all the three SMTs. In DWT, C-All provided the highest discrimination accuracy compared to other coefficients. An overall accuracy difference of about 20–30% is observed between the finest coefficient and C-All. Amongst the SMTs, SID provided the highest accuracy, while SAM showed the lowest accuracy. Using DWT in combination with SID, an overall accuracy up to around 71.4% is obtained, which is around 13.5%, 14.7%, and 27% higher than the accuracies achieved with reflectance and first and second derivatives, respectively.     

The effect of spectral and spatial degradation of hyperspectral imagery for the Sclerophyll tree species classification

Hyperspectral imaging can be a useful remote-sensing technology for classifying tree species. Prior to the image classification stage, effective mapping endeavours must first identify the optimal spectral and spatial resolutions for discriminating the species of interest. Such a procedure may contribute to improving the classification accuracy, as well as the image acquisition planning. In this work, we address the effect of degrading the original bandwidth and pixel size of a hyperspectral and hyperspatial image for the classification of Sclerophyll forest tree species. A HySpex-VNIR 1600 airborne-based hyperspectral image with submetric spatial resolution was acquired in December 2009 for a native forest located in the foothills of the Andes of central Chile. The main tree species of this forest were then sampled in the field between January and February 2010. The original image spectral and spatial resolutions (160 bands with a width of 3.7 nm and pixel sizes of 0.3 m) were systematically degraded by resampling using a Gaussian model and a nearest neighbour method, respectively (until reaching 39 bands with a width of 14.8 nm and pixel sizes of 2.4 m). As a result, 12 images with different spectral and spatial resolution combinations were created. Subsequently, these images were noise-reduced using the minimum noise fraction procedure and 12 additional images were created. Statistical class separabilities from the spectral divergence measure and an assessment of classification accuracy of two supervised hyperspectral classifiers (spectral angle mapper (SAM) and spectral information divergence (SID)) were applied for each of the 24 images. The best overall and per-class classification accuracies (>80%) were observed when the SAM classifier was applied on the noise-reduced reflectance image at its original spectral and spatial resolutions. This result indicates that pixels somewhat smaller than the tree canopy diameters were the most appropriate to represent the spatial variability of the tree species of interest. On the other hand, it suggests that noise-reduced bands derived from the full image spectral resolution rendered the best discrimination of the spectral properties of the tree species of interest. Meanwhile, the better performance of SAM over SID may result from the ability of the former to classify tree species regardless of the illumination differences in the image. This technical approach can be particularly useful in native forest environments, where the irregular surface of the uppermost canopy is subject to a differentiated illumination.     

Integrating visible,near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite,Nevada: a rule-based system

Previous research has shown that integrating hyperspectral visible and near-infrared (VNIR) / short-wave infrared (SWIR) with multispectral thermal infrared (TIR) data can lead to improved mineral and rock identification. However, inconsistent results were found regarding the relative accuracies of different classification methods for dealing with the integrated data set. In this study, a rule-based system was developed for integration of VNIR/SWIR hyperspectral data with TIR multispectral data and evaluated using a case study of Cuprite, Nevada. Previous geological mapping, supplemented by field work and sample spectral measurements, was used to develop a generalized knowledge base for analysis of both spectral reflectance and spectral emissivity. The characteristic absorption features, albedo and the location of the spectral emissivity minimum were used to construct the decision rules. A continuum removal algorithm was used to identify absorption features from VNIR/SWIR hyperspectral data only; spectral angle mapper (SAM) and spectral feature fitting (SFF) algorithms were used to estimate the most likely rock type. The rule-based system was found to achieve a notably higher performance than the SAM, SFF, minimum distance and maximum likelihood classification methods on their own.     

Lithological discrimination of the Phenaimata felsic–mafic complex,Gujarat, India,using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

This study deals with an evaluation of the efficacy of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image for lithological mapping. ASTER level-1B data in the visible near-infrared (VNIR), short wave infrared (SWIR) and thermal infrared (TIR) regions have been processed to generate a lithological map of the study area in and around the Phenaimata igneous complex, in mainland Gujarat, India. ASTER band combinations, band ratio images and spectral angle mapper (SAM) processing techniques were evaluated for mapping various lithologies. The reflectance and emissivity spectra of rock samples collected from the study area were obtained in the laboratory and were used as reference spectra for ASTER image analysis. The original data in the scaled digital number (DN) values were converted to radiance and then to relative reflectance by using a scene-derived correction technique prior to SAM classification. The SAM classification in the VNIR–SWIR region is found to be effective in differentiating felsic and mafic lithologies. The relative band depth (RBD) images were generated from the continuum-removed images of ASTER VNIR–SWIR bands. Four RBD combinations (3, 5, 6 and 8) were used to identify Al-OH (aluminium hydroxide), Fe-OH (iron hydroxide), Mg-OH (magnesium hydroxide) and CO₃ (carbonate) absorption from various lithological components. ASTER TIR spectral emittance data and the laboratory emissivity measurements show the presence of a number of discrete Si-O spectral features that can differentiate mafic and felsic rock types reflecting the lithological diversity around the regions of Phenaimata igneous complex. SAM classification using emittance data failed to distinguish the felsic and mafic lithology due to the wider spectral bandwidth. The felsic class comprises the granitoid composition of rocks. RBD12 and 13 images in the TIR region were used to derive the mafic index (MI) and the silica index (SI). The MI shows the highest value in regions of gabbro–basalt occurrence, while the SI indicates regions of high silica content. The MI is lowest in regions where granophyres occur. The complimentary attributes based on the spectral reflectance and emittance data resulted in the discrimination of silica-rich and silica-poor lithologies.     

岩石实验室反射光谱的相似系数聚类分析

以n维向量空间中的向量夹角为分类依据,对内蒙乌拉尔山地区53个岩石实验室反射光谱进行相似系数聚类分析,揭示出0.4～2.5μm岩石谱形的总体相似性差异。结果表明,岩石的电磁波属性不同于岩石学的分类属性,提出按谱形的相似性对岩石进行归类的方法及其意义。同时分析了成像光谱技术中光谱角度填图方法的优劣及改善措施,为成像光谱图像处理方法提供了十分有意义的光谱依据。     

Utilization of Landsat ETM+ data for mineral-occurrences mapping over Dalma and Dhanjori,Jharkhand, India: an Advanced Spectral Analysis approach

The Advanced Spectral Analysis (ASA) technique, one of the most advanced remote-sensing tools, has been used as a possible means of identifying mineral occurrences over Dalma and Dhanjori. The ASA technique is a sixfold tool, which includes the continuous processes of (1) the reflectance calibration of Landsat Enhanced Thematic Mapper (ETM+) images of the study area, (2) the generation of minimum noise fraction (MNF) transformation, (3) the calculation of the pixel purity index (PPI), (4) the n-dimensional visualization and extraction of endmember spectra, (5) the identification of endmember spectra for mineral occurrences and (6) the mapping of mineral occurrences. The identification of the extracted endmember spectra is obtained by comparing it with available pre-defined library spectra (United States Geological Survey (USGS), John Hopkins University (JHU) and Jet Propulsion Laboratory (JPL) spectral libraries) using the Spectral Analyst tool of ENVI 4.1 software (Research Systems Inc., Boulder, CO, US), which provides scores of matching. Three techniques, namely Spectral Feature Fitting (SFF), Spectral Angle Mapping (SAM) and Binary Encoding (BE), are used for identification of the collected endmember spectra to produce a score between 0 and 1, where the value of 1 equals a perfect match showing the exact mineral type. A total of six endmember spectra are identified and extracted in the study area. Mapping of mineral occurrences is carried out using the Mixture-Tuned Matched Filtering (MTMF) technique over the study area on the basis of collected and identified endmember spectra. Results of the present study using the ASA technique ascertain that Landsat ETM+?data can be used to generate valuable mineralogical information.     

Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area,India

ABSTRACT

The present study exploits high-resolution hyperspectral imagery acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor from the Hutti-Maski gold deposit area, India, to map hydrothermal alteration minerals. The study area is a volcanic-dominated late Archean greenstone belt that hosts major gold mineralization in the Eastern Dharwar Craton of southern India. The study encompasses pre-processing, spectral and spatial image reduction using Minimum Noise Fraction (MNF) and Fast Pixel Purity Index (FPPI), followed by endmember extraction using n-dimensional visualizer and the United States Geological Survey (USGS) mineral spectral library. Image derived endmembers such as goethite, chlorite, chlorite at the mine site (chlorite mixed with mined materials), kaolinite, and muscovite were subsequently used in spectral mapping methods such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID) and its hybrid, i.e. SIDSAM_tan. Spectral similarity matrix of the target and non-target-based method has been proposed to find the possible optimum threshold needed to obtain mineral map using spectral mapping methods. Relative Spectral Discrimination Power (RSDPW) and Confusion Matrix (CM) have been used to evaluate the performance of SAM, SID, and SIDSAM_tan. The RSDPW and CM illustrate that the SIDSAM_tan benefits from the unique characteristics of SAM and SID to achieve better discrimination capability. The Overall Accuracy (OA) and kappa coefficient (?) of SAM, SID, and SIDSAM_tan were computed using 900 random validation points and obtained 90% (OA) and 0.88 (?), 91.4% and 0.90, and 94.4% and 0.93, respectively. Obtained mineral map demonstrates that the northern portion of the area mainly consists of muscovite whereas the southern part is marked by chlorite, goethite, muscovite and kaolinite, indicating the propylitic alteration. Most of these minerals are associated with altered metavolcanic rocks and migmatite.     

Reference spectra to classify Amazon water types

Reference spectra extracted from spectral libraries can distinguish different water types in images when associated with limnological information. In this study, we compiled available databases into a single spectral library, using field water reflectance spectra and limnological data collected by different researchers and campaigns in the Amazonian region. By using an iterative clustering procedure based on the combination of reflectance and optically active components (OACs), reference spectra representative of the major Amazonian water types were defined from this library. Differences between the resultant limnological classes were also evaluated by paired t-tests at significance level 0.05. Finally, reference spectra were tested for Spectral Angle Mapper (SAM) classification of waters in Hyperion/Earth Observing-One (EO-1) and Medium Resolution Imaging Spectrometer (MERIS)/Environment Satellite (Envisat) images acquired simultaneously as the field campaigns. Results showed highly variable concentrations of OACs due to the complexity of the Amazonian aquatic environments. Ten classes were defined to represent this complexity, broadly grouped into four limnological characteristics: clear waters with low concentrations of OACs (class 1); black waters rich in dissolved organic carbon (DOC) (class 2); waters with large concentrations of inorganic suspended solids (ISSs) (classes 3–7); and waters dominated by chlorophyll-a (chl-a) (classes 8–10). Using the ten reference spectra, SAM classification of the field water curves produced an overall accuracy of 86% with the highest values observed for classes 3, 4, 6 and 7 and the lowest accuracy for classes 1 and 2. The results of paired t-tests confirmed the class differences based on the concentrations of OACs. SAM classification of the Hyperion and MERIS images using ground truth information resulted in overall classification accuracies of 48% and 67%, respectively, with the highest errors associated with specific portions of the scenes that were not adequately represented in the spectral library.     

Spectral analysis and classification of metamorphic rocks from laboratory reflectance spectra in the 0.4-2.5 μ m interval: A tool for hyperspectral data interpretation

Laboratory reflectance (0.4-2.5 w m ) spectra of 41 samples of metamorphic rocks from the Precambrian basement of Madagascar were analysed on the basis of absorption band position and shape, and classified on the basis of recurrent associations of absorption bands. Petrographic analyses allowed us to interpret the absorption features in compositional terms. Spectral and petrographic classes coincided when the principal mineralogy was also spectrally dominant (e.g. in carbonate rocks). When the principal mineralogy did not produce diagnostic spectral features (e.g. in siliceous rocks in the visible-short wave infrared region), the classification was based on spectrally dominant secondary phases. The reflectance spectra were measured on both freshly cut and exposed surfaces of the samples. Apart from a few cases of spectral features obliteration due to kaolinization, or overall albedo change related to texture variation, the two sets of spectra did not significantly differ. The responses of airborne MIVIS and AVIRIS hyperspectral sensors were simulated from spectra representative of the spectral classes, showing that significant identification and classification of well exposed metamorphic rocks are potentially possible using remote instruments providing high quality spectra. Although at present there are no plans for a spaceborne instrument of this quality, TM simulations and band composite images showed that a preliminary gross discrimination of the rocks belonging to the different classes was however possible.     

基于光谱信息散度与光谱角匹配的高光谱解混算法

针对采用线性逆卷积(LD)算法进行端元初选过程中,端元子集中存在相似端元光谱,影响解混精度的问题,提出了一种基于光谱信息散度(SID)与光谱角匹配(SAM)算法的端元子集优选光谱解混算法。通过在端元进行二次选择时,采用以光谱信息散度和光谱角(SID-SA)混合法准则作为最相似端元选择的判据,去除相似端元,降低相似端元对解混精度的影响。实验结果表明,基于SID与SAM的高光谱解混算法将重构影像的均方根误差(RMSE)降低到0.0104,该方法比传统方法提高了端元的选择精度,减少了丰度估计误差,误差分布更加均匀。     

Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada

This study investigated the potential value of integrating hyperspectral visible, near-infrared, and short-wave infrared imagery with multispectral thermal data for geological mapping. Two coregistered aerial data sets of Cuprite, Nevada were used: Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data, and MODIS/ASTER Airborne Simulator (MASTER) multispectral thermal data. Four classification methods were each applied to AVIRIS, MASTER, and a combined set. Confusion matrices were used to assess the classification accuracy. The assessment showed, in terms of kappa coefficient, that most classification methods applied to the combined data achieved a marked improvement compared to the results using either AVIRIS or MASTER thermal infrared (TIR) data alone. Spectral angle mapper (SAM) showed the best overall classification performance. Minimum distance classification had the second best accuracy, followed by spectral feature fitting (SFF) and maximum likelihood classification. The results of the study showed that SFF applied to the combination of AVIRIS with MASTER TIR data are especially valuable for identification of silicified alteration and quartzite, both of which exhibit distinctive features in the TIR region. SAM showed some advantages over SFF in dealing with multispectral TIR data, obtaining higher accuracy in discriminating low albedo volcanic rocks and limestone which do not have unique, distinguishing features in the TIR region.     

A Landsat-based study of black rock coatings proximal to base metal smelters,Sudbury, Ontario,Canada

Past emission of metal-bearing particulate matter, sulphur dioxide (SO₂), and sulphuric acid by base metal smelters in the Sudbury region led to widespread loss of vegetation, contamination of soils, and formation of black coatings on rock surfaces. These black coatings formed through the incorporation of smelter-borne particulate matter into the partly dissolved uppermost layers of siliceous minerals on exposed rock, and are characterized by high heavy-metal content. This study involved assessment of the reflectance properties of black coatings in the Sudbury region, and determination of the geographic distribution of coatings through supervised classification of reflectance data derived from a Landsat Enhanced Thematic Mapper Plus (ETM+) image. Classifications involved the use of the Spectral Angle Mapper (SAM), Maximum Likelihood, and Feedforward Backpropagation Neural Network algorithms. The reflectance spectra of black coatings in the Sudbury region are relatively flat and featureless, and are characterized by reflectance values less than ~13% across the visible, near-infrared, and short-wave infrared. Spectral properties are similar to those of magnetite, a spinel-group mineral known to be present in Sudbury coatings. The presence of carbon-rich soot particles may be an important influence on the reflectance properties of coatings. SAM classification results are characterized by the widespread mislabelling of uncoated urban and open-pit sites as mantled by black coatings, and neural network results problematically mislabel some uncoated wetland sites as coated. Results generated by the Maximum Likelihood algorithm most usefully depict the distribution of exposed black coatings in the Sudbury region. The mapping of black coatings using remote-sensing methods can provide useful information on the spatial character of environmental degradation in the vicinity of smelters, and should be helpful in the monitoring of environmental recovery where emissions have been reduced or eliminated.     

Integrating visible,near‐infrared and short wave infrared hyperspectral and multispectral thermal imagery for geologic mapping: simulated data

Hyperspectral and thermal infrared (TIR) multispectral remote sensing have great potential for surface geological mapping. This paper investigates the potential impact of combining these data on the comparative accuracy of different classification methods. A series of simulated datasets based on the characteristics of Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER) sensors was created from surface reflectance and emissivity data derived from library spectra of 16 common minerals and rocks occurring in Cuprite, Nevada. System noise, illumination effects, the presence of vegetation, and spectral mixing were added to create the simulated data. Five commonly used classification algorithms, minimum distance, maximum likelihood classification, binary encoding, spectral angle mapper (SAM) and spectral feature fitting (SFF), were applied to all datasets. All the classification methods, excluding binary encoding, achieved nominal to significant improvement in overall accuracy when applied to the combined datasets in comparison to using only the AVIRIS dataset. Furthermore, certain classification methods of the combined datasets show a marked increase in individual rock or mineral class accuracies. Limestone, silicified and muscovite, for instance, show an improvement of almost 30% or greater in either producer's or user's accuracy using the combined datasets with SAM. SFF provides a great improvement in accuracy for limestone, quartz and muscovite. In terms of overall comparative accuracy for the individual and the combined datasets, maximum likelihood classification shows the best performance. For the simulated AVIRIS data, SFF was generally superior to SAM, although the accuracy of SAM applied to the combined datasets was slightly better than that of SFF. SAM applied to the combined datasets increases classification accuracy for some minerals and rocks which do not exhibit distinct absorption feature in the TIR region, while for SFF, only the accuracy of minerals and rocks with characteristic absorption features in the TIR region is improved.     

Hyperspectral aerial images. A valuable tool for submerged vegetation recognition in the Orbetello Lagoons,Italy

Results obtained in mapping algal belts in the Orbetello Lagoons are described using Daedalus/MIVIS hyperspectral scanner aerial images. MIVIS has a spectral coverage in the Visible, Near-IR, Mid-IR and Thermal-IR regions, with 102 channels. The objective of the work is a procedure for the algal species recognition, using methods of spectral data analysis. The 1-2m deep brackish, shallow water basin areas of the Orbetello Lagoons have poor water circulation and considerable eutrophication phenomena. During MIVIS overflight, spectra of submerged vegetation (Cymodocea sp., Cladophora sp., Chaetomorpha sp., Gracilaria sp., Enteromorpha sp., Ruppia sp. and Ulva sp.) were collected from a boat equipped with a field portable multi-spectral radiometer operating between 380 and 780nm. In situ collected spectra and MIVIS spectra, in the visible and the near infrared region for prototype area, were compared to select the representative spectra of submerged vegetation. The Spectral Angle Mapper (SAM) has been the method adopted for the spectral classification.     

Variation and directional anisotropy of reflectance at the crown scale — Implications for tree species classification in digital aerial images

Tree species classification is still solved at insufficient reliability in airborne optical data. The variation caused by directional reflectance anisotropy hampers image-based solutions. In addition, trees show considerable within-species variation in reflectance properties. We examined these phenomena at the single-tree level, using the Leica ADS40 line sensor and XPro software, which constitute the first photogrammetric large-format multispectral system to provide target reflectance images. To analyze the influence of illumination conditions in the canopy, we developed a method in which the crown shape as well as between-tree occlusions and shading were modeled, using dense LiDAR data. The precision of the ADS40 reflectance images in well-defined surfaces was 5% as coefficient of variation when 1−4-km image data were fused. The range of reflectance anisotropy was ± 30% for trees near the solar principal plane, with differences between bands and species. Because of the anisotropy differences observed, the spectral separability of the tree species in different bands is dependent on the view-illumination geometry. The within-species variation was high; the coefficient of variation was 13−31%. The contribution of tree and stand variables to anisotropy-normalized reflectance variation was examined. The effects of the species composition of adjacent trees were substantial in NIR and this variation hampers spectral classification in mixed stands. We also studied species- and band-specific intracrown brightness patterns, and we suggest their use as high-order image features in species classification. A species classification accuracy of up to 80% was obtained using 4-km data, which showed the high potential of the ADS40.     

Utilization of Hyperion data over Dongargarh,India, for mapping altered/weathered and clay minerals along with field spectral measurements

Hyperion data acquired over Dongargarh area, Chattisgarh (India), in December 2006 have been analysed to identify dominant mineral types present in the area, with special emphasis on mapping the altered/weathered and clay minerals present in the rocks and soils. Various advanced spectral processes such as reflectance calibration of the Hyperion data, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) have been used for comparison/mapping in conjunction with spectra of rocks and soils that have been collected in the field using Analytical Spectral Devices's FieldSpec instrument. In this study, 40 shortwave infrared channels ranging from 2.0 to 2.4 μm were analysed mainly to identify and map the major altered/weathered and clay minerals by studying the absorption bands around the 2.2 and 2.3 μm wavelength regions. The absorption characteristics were the results of O–H stretching in the lattices of various hydrous minerals, in particular, clay minerals, constituting altered/weathered rocks and soils. SAM and SFF techniques implemented in Spectral Analyst were applied to identify the minerals present in the scene. A score of 0–1 was generated for both SAM and SFF, where a value of 1 indicated a perfect match showing the exact mineral type. Endmember spectra were matched with those of the minerals as available in the United States Geological Survey Spectral Library. Four minerals, oligoclase, rectorite, kaolinite and desert varnish, have been identified in the studied area. The SAM classifier was then applied to produce a mineral map over a subset of the Hyperion scene. The dominant lithology of the area included Dongargarh granite, Bijli rhyolite and Pitepani volcanics of Palaeo-Proterozoic age. Feldspar is one of the most dominant mineral constituents of all the above-mentioned rocks, which is highly susceptible to chemical weathering and produces various types of clay minerals. Oligoclase (a feldspar) was found in these areas where mostly rock outcrops were encountered. Kaolinite was also found mainly near exposed rocks, as it was formed due to the weathering of feldspar. Rectorite is the other clay mineral type that is observed mostly in the southern part of the studied area, where Bijli rhyolite dominates the lithology. However, the most predominant mineral type coating observed in this study is desert varnish, which is nothing but an assemblage of very fine clay minerals and forms a thin veneer on rock/soil surfaces, rendering a dark appearance to the latter. Thus, from this study, it could be inferred that Hyperion data can be well utilized to identify and map altered/weathered and clay minerals based on the study of the shape, size and position of spectral absorption features, which were otherwise absent in the signatures of the broadband sensors.     

Spectral Angle Mapper and aeromagnetic data integration for gold-associated alteration zone mapping: a case study for the Central Eastern Desert Egypt

The Spectral Angle Mapper (SAM) classification technique is integrated with the surface structure and aeromagnetic data to map the potential gold mineralization sites associated within alteration zones in Central Eastern Desert (CED), Egypt. The surface reflectances of the Enhanced Thematic Mapper Plus (ETM+) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were classified using the SAM classifier. Five spectral reflectance curves of the alteration minerals (haematite, illite, kaolinite, chlorite, and quartz) were utilized as end-members for the SAM classification. The surface lineation, and shear zone systems were delineated using ETM+ bands. The deep-seated faults were defined using the Euler deconvolution filter on the gridded aeromagnetic data. The magnetic data analysis inferred the subsurface structural depths range from 500 m to 2000 m. Geographic information system (GIS) overlaying operation was performed using the surface lineation and the subsurface faults layers to identify the structural continuity and to extract the possible migratory pathways of the hydrothermal solutions. Within Multiple Criteria Decision Analysis (MCDA), fuzzy membership operations were applied to identify the prospective alteration sites. The mapped results were compared with global positioning system (GPS) locations of existing alteration zones. The current proposed mapping method is considered a robust tool for decision-making and potential site selection technique for further mineral exploration in CED.