Spectral Angle Mapper Method Research Articles

Mapping of Invasive Species Acacia Decurrens in Part of Mount Merbabu National Park Using Prisma Hyperspectral Imagery

Mount Merbabu National Park (TNGMb) is a forest area in the Mount Merbabu. Management and planting changes have caused many changes to the types of plants in TNGMb. Acacia decurrens is an invasive species and its presence in TNGMb can result in a decrease in the diversity of native vegetation types. This research aims to (1) map the distribution of Acacia decurrens plants in TNGMb using PRISMA hyperspectral imagery with the Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) methods. (2) determine and test the level of accuracy of mapping Acacia decurrens plants in TNGMb using PRISMA hyperspectral imagery. Classification results were carried out using the SAM and SID methods on PRISMA images. The best A. decurrens mapping results were SAM classification at a maximum angle of 0.3 radians with PRISMA Imagery. The distribution of Acacia decurrens in the TNGMb area is in the southwest and northwest. The best accuracy test results were from mapping Acacia decurrens with PRISMA images with an accuracy test value of 56.36%.

Glass waste analysis and differentiation by laser-induced breakdown spectroscopy associated to support vector machine: The influence of color and particle size

Glasses are widely known for their unique properties, but improper disposal poses several environmental challenges. Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a promising tool for glass characterization. This study explored the performance of LIBS, associated with machine learning methods and spectral angle mapper, to overcome the matrix effect in glass waste analysis. Using 10-fold cross-validation, an accuracy of 99.04% was achieved in color differentiation, 98.82% in the differentiation of flint glasses from the other glasses, 96.75% in differentiating particle sizes. When particle size and color were analyzed simultaneously, the accuracy remained high at 97.64%. The analytical accuracy was further improved using the spectral angle mapper method, which allowed us to achieve lower standard deviations, particularly for samples of larger particle sizes. These findings highlight the potential of LIBS combined with machine learning to perform a robust analysis of glass waste, contributing to recycling management.

Exploration of ecological environment construction and development paths in towns and cities by applying time series data

Abstract This paper explores the interaction mechanism between urbanization and the ecological environment based on the unique correlation between urbanization and the ecological environment. BISE and Savizky-Golay algorithms are combined to establish the time series data, and for the characteristic curves of the EVI time response of the land cover, the spectral angle mapping method is combined with the minimum distance method to form the spectral angle-minimum distance classification method. The application of time series data involves calculating the time and spatial change of urban land cover and analyzing the evolution of urban ecological environment elements. Based on the temporal and spatial consistency test and land cover analysis of urban ecological environment construction, as well as the development of urbanization construction, relevant development paths are proposed. The comprehensive score of new urbanization in the four districts and one city of Hengyang City is concentrated towards 1.6, indicating that the level of economic development and the construction of new urbanization have reached coupling at different levels, respectively.

The Automated Detection of Fusarium Wilt on Phalaenopsis Using VIS-NIR and SWIR Hyperspectral Imaging

Phalaenopsis, an essential flower for export, is significantly affected by fusarium wilt, which impacts its export quality. Hyperspectral imaging technology offers the potential to detect fusarium wilt on Phalaenopsis. The goal of this study was to establish an automated platform for the rapid detection of fusarium wilt on Phalaenopsis. In this research, the automatic target generation process (ATGP) method was employed to identify outliers in the hyperspectral spectrum. Subsequently, the Spectral Angle Mapper (SAM) method was utilized to detect signals similar to the outliers. To suppress background noise and extract the region of interest (ROI), the Constrained Energy Minimization (CEM) method was implemented. For ROI classification and detection, a deep neural network (DNN), a support vector machine (SVM), and a Random Forest Classifier (RFC) were employed. Model performance was evaluated using three-dimensional receiver operating characteristics (3D ROC), and the automated identification system was integrated into hyperspectrometers. The proposed system achieved an accuracy of 95.77% with a total detection time of 3380 ms ± 86.36 ms, proving to be a practical and effective tool for detecting fusarium wilt on Phalaenopsis in the industry.

Integration of Remote Sensing and Field Data in Ophiolite Investigations: A Case Study of Logar Ophiolite Complex, SE Afghanistan

Mafic–ultramafics complexes are crucial for their tectonic implication, upper mantle condition, and for hosting industrial minerals in a region. This study aims to highlight and characterize the mafic–ultramafic rocks of the Logar Ophiolite Complex using the integration of geospatial technology and field data. The spatial distribution of the ophiolitic complex was examined in this study using the mineralogical indices (MI), band ratio (BR), and spectral angle mapper (SAM) methods within the framework of geospatial technology using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Additionally, several samples were collected from the identified complexes for validation, petrographic, and mineralogical analyses. Combining geospatial technology and conventional approaches, e.g., field sampling and geological data analysis yields efficient discrimination of mafic–ultramafic rocks with their associated hydrothermal altered minerals. The serpentinization and carbonate processes are predominantly seen along the eastern side of the active fault zone following the detection of ophiolites. Detailed mapping of the ophiolitic complex and associated rocks was achieved using refined mafic index (MI), band ratio 12/14 and 4/8 for rocks and SAM for highlighting the mafic–ultramafic altered minerals, and petrographic analysis of the collected samples. The field works verified the results of the ASTER data. The findings of this study can significantly contribute to detailed tectonic and geologic studies of the detected ophiolites in terms of their emplacement mechanism and ages.

A unified heuristic approach to simultaneously detect fusarium and ergot damage in wheat

Fusarium spp. and Ergot (Claviceps purpurea) are fungal pathogenic species that infect cereals worldwide, causing widespread economic losses. Fusarium-damaged kernels (FDKs) in wheat often contain harmful mycotoxins, while Ergot damage (ERG) is commonly characterized by the presence of alkaloids. Both FDK and ERG damage could cause health hazards to people and animals. Currently, assessment of FDKs and ERG is performed by human visual inspection using distinctly different biochemical assays and/or wet chemistry methods, all of which are subjective, time-consuming and non-repeatable. In this study, the authors present a unified non-destructive optical method using visible-near-infrared (VNIR) (400-1000 nm) and short-wavelength near-infrared (SWIR) (1000-2500 nm) hyperspectral imaging (HI) to simultaneously detect FDK and ERG damage in wheat kernels. The un-mixing technique of spectral angle mapping (SAM) was employed for pixel- and object-wise classification of the three classes i.e. FDK, ERG and control (healthy), of non-touching wheat kernels. Overall classification accuracies of VNIR–93% and SWIR–85% were obtained with 100% classification of ERG samples in the VNIR range. To reduce model complexity, principle component analysis (PCA) was explored, and a subset of 12 significant wavelengths was selected. The SAM model based on these selected wavelengths did not achieve accurate prediction, indicating it requires the entire spectral data for reliable performance. However, a partial least square discriminant analysis (PLSDA) model using the reduced wavelength subset achieved overall classification accuracies of VNIR–94% and SWIR–90%, which were better when compared to the SAM method. This study is the first known attempt to simultaneously identify two important fungal diseases in wheat using a completely non-invasive and non-destructive method. The results confirm the feasibility of developing a fast and cost-effective multi-spectral classification technique for FDK and ERG detection.

Quantitative assessment of white spot (Ramularia tulasnei) disease severity of strawberry based on hyperspectral imaging

This study examined strawberry white spot disease severity using different hyperspectral imaging analyzing methods. The plant leaf images were classified by spectral angle mapper (SAM), by vegetation indices (RENDVI, GNDVI, MCARI) thresholds and by principal component analysis (PCA) method. The SAM method showed the overall accuracy 84% when classifying three types of visual symptoms of the disease.

Improvement of a mineral discrimination method using multispectral image and surrounding hyperspectral image

Hyperspectral (HS) images are highly accurate for mineral discrimination, but available areas are limited. For this reason, several methods have been proposed to extend the mineral map of the overlap region between HS and multispectral (MS) images to the surrounding area with no HS image. One such method, proposed by Hirai and Tonooka, discriminates minerals using MS images by obtaining the endmembers of MS images from the positions of the endmember pixels of HS images in the overlap region. While this method (referred to as HT method) has the advantage of being less susceptible to the spectral distortions of HS and MS images, it also has the problem of reduced accuracy due to misalignment between HS and MS images. We proposed an improved HT method that reduces the effects of the above problems by incorporating a process that improves the robustness against misalignment by searching for the best MS endmember pixel around the position of the HS endmember pixel and a process that determines more optimum threshold value of each mineral in the spectral angle mapper method used in the HT method. As a result of evaluation using an AVIRIS image as an HS image and a World View-3 image as an MS image at Cuprite, Nevada, the improved method improved the overall accuracy by 2.6% compared with the original HT method, and in the case that the HS and the MS images were misaligned, the overall accuracy of the original method decreased by 7.0%, while the improved method decreased by only 1.5%. These results indicate that the improved method can perform as expected.

Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration zones associated with porphyry copper deposits. The main objective of this investigation is to practice an algorithm that can accurately model the best training data as input for supervised methods such as SVM. For this purpose, the Zefreh porphyry copper deposit located in the Urumieh-Dokhtar Magmatic Arc (UDMA) of central Iran was selected and used as training data. Initially, using ASTER data, different alteration zones of the Zefreh porphyry copper deposit were detected by Band Ratio, Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Spectral Feature Fitting (SFF), and Orthogonal Subspace Projection (OSP) techniques. Then, using the DP method, the exact extent of each alteration was determined. Finally, the detected alterations were used as training data to identify similar alteration zones in full scene of ASTER using SVM and Spectral Angle Mapper (SAM) methods. Several high potential zones were identified in the study area. Field surveys and laboratory analysis were used to validate the image processing results. This investigation demonstrates that the application of the SVM algorithm for mapping hydrothermal alteration zones associated with porphyry copper deposits is broadly applicable to ASTER data and can be used for prospectivity mapping in many metallogenic provinces around the world.

An Application of the “Traffic Lights” Idea to Crop Control in Integrated Administration Control System

The aim of the paper is to discuss the idea of marking agricultural parcels in the control of direct payments to agriculture. The method of using remote sensing to monitor crops and mark them according to the idea of “traffic lights” is introduced. Classification into a given “traffic lights” color gives clear information about the status of the parcel. The image classification was done on Sentinel-1 and Sentinel-2 datasets by calculating the NDVI and SIGMA time series in the season from autumn 2016 to autumn 2017. Two approaches are presented: semi-automated and automated classifications. Semi-automated classification based on NDVI_index and SIGMA_index. Automated classification was performed on NDVI by Spectral Angle Mapper method and on SIGMA by Artificial Neural Network (Multilayer Perceptron, MLP method). The following overall accuracy was obtained for NDVI_SAM: 70.35%, while for SIGMA_CNN it was: 62.01%. User accuracy (UA) values were adopted for traffic lights analysis, in machine learning: positive predictive value (PPV). The UA/PPV for rapeseed were in NDVI_index method: 88.1% (6,986 plots), NDVI_SAM: 85.0% (199 plots), SIGMA_index: 61.3% (4,165 plots) and in SIGMA_CNN: 88.9% (2,035 plots). In order to present the idea of “traffic lights”, a website was prepared using data from the NDVI_index method, which is a trade-off between the number of plots and UA/PPV accuracy.

Deep Learning for Effective Refugee Tent Extraction Near Syria–Jordan Border

Rukban is a desert area crossing the border between Syria and Jordan, and thousands of Syrian refugees fled into this area since the Syrian civil war in 2014. In the past few years, the number of refugee shelters for the forcibly displaced Syrian refugees in this area has increased rapidly. Estimating the location and number of refugee tents has become a key factor to maintain the sustainability of the refugee shelter camps. Manually counting the shelters is labor-intensive and sometimes prohibitive given the large quantities. In addition, these shelters/tents are usually small in size, irregular in shape, and sparsely distributed in a very large area and could be easily missed by the traditional image-analysis techniques, making the image-based approaches also challenging. In this letter, we proposed a deep fully convolutional neural network (FCN) model to extract automatically the refugee shelters/tents in the worldview-2 (WV-2) satellite images. In addition, we transferred knowledge in the pretrained VGG-16 model to improve the detection accuracy and network training convergence. We compared the proposed approach with the traditional spectral angle mapper (SAM) method, deep convolutional neural network (CNN) models, and the mask Region-based CNN (R-CNN) model. The experimental results show that the FCN model improved the overall accuracy by 4.49%, 3.54%, and 0.88% compared with the CNNs, SAM, and mask R-CNN models, and improved the precision by 34.61%, 41.99%, and 11.87%, respectively.

Evaluating pixel-based vs. object-based image analysis approaches for lithological discrimination using VNIR data of WorldView-3

The object-based against pixel-based image analysis approaches were assessed for lithological mapping in a geologically complex terrain using Visible Near Infrared (VNIR) bands of WorldView-3 (WV-3) satellite imagery. The study area is Hormuz Island, southern Iran, a salt dome composed of dominant sedimentary and igneous rocks. When performing the object-based image analysis (OBIA) approach, the textural and spectral characteristics of lithological features were analyzed by the use of support vector machine (SVM) algorithm. However, in the pixel-based image analysis (PBIA), the spectra of lithological end-members, extracted from imagery, were used through the spectral angle mapper (SAM) method. Several test samples were used in a confusion matrix to assess the accuracy of classification methods quantitatively. Results showed that OBIA was capable of lithological mapping with an overall accuracy of 86.54% which was 19.33% greater than the accuracy of PBIA. OBIA also reduced the salt-and-pepper artifact pixels and produced a more realistic map with sharper lithological borders. This research showed limitations of pixel-based method due to relying merely on the spectral characteristics of rock types when applied to high-spatial-resolution VNIR bands of WorldView-3 imagery. It is concluded that the application of an object-based image analysis approach obtains a more accurate lithological classification when compared to a pixel-based image analysis algorithm.

Using geochemical imaging data to map nickel sulfide deposits in Daxinganling, China

Nickel sulfide deposits occur in ultramafic rocks in the Daxinganling area, China; however, the prospectivity of these deposits has received little attention. This study transformed rasterized regional 1:200,000 geochemical data into spectral-like data and then used hyperspectral tools of the spectral angle mapper (SAM) to classify possible ultramafic lithologies and the multirange spectral feature fitting (MRSFF) method to classify prospective areas that are similar to a typical Gaxian Ni deposit. The prospective area map generated by the MRSFF implies the possible occurrence of ultramafic rocks classified by the SAM method. These results confirm the suitability of this innovative approach for prospectivity mapping of Ni sulfide deposits.

Characterization of Fire Severity in the Moroccan Rif Using Landsat-8 and Sentinel-2 Satellite Images

Forest ecosystems are exposed increasingly to a variety of human activities and accentuated by climate change. With its Mediterranean climate, Northern Morocco is very hot, which exposes forests to widespread fires. This work aims at the delineation of wildfires and the spectral characterization of burnt vegetation as well as the characterization of the fire severity in the North of Morocco by using Landsat-8, Sentinel-2 spectral data, and topographic data. The methods used include the derivation of wildfires spectral indices and the computation of topographic parameters (elevation, slope, exposure) from SRTM and PALSAR digital elevation models. Then, the Spectral Angle Mapper (SAM) classification was used to map forest fires' severity. Furthermore, we have compared the severity classes obtained from the SAM method applied to Landsat 8 and Sentinel 2 data, with different spectral indices specialized in detecting wildfires, on the one hand, and topographic data, on the other hand. Results showed that MIRBI and NBR indices allow a better characterization of burned areas than BAI index. For its part, SAM classification provides a fair characterization of the severity classes of burnt forests. It has also been shown that the MIRBI index and sun exposure are strongly correlated with severity classes. The obtained maps show the spatial heterogeneity of burns severity and how they interact with topography. These maps may help land resource managers and fire officials predict areas of potential fire hazards and study vegetation regrowth areas after fires.

Applying various satellite image processing methods on Aster and Landsat ETM+ data to identify and separate the alteration zones around gold mine of Akhtarchi, Khomein, Iran

The study area is located 100 km southeast of Arak and in two structural zones of Central Iran in the north and Sanandaj-Sirjan in the southern part. Regarding its geological structures, the area has become the source of important mines including the Akhtarchi gold mine, Aliabad iron mine, Ochestan feldspar mine, and Dali gold and copper mines. Therefore, promising areas for exploration activities are identified using the analysis of satellite images of ASTER and Landsat ETM + in the region to identify alteration areas. For this purpose, the necessary corrections were applied to the satellite images. Then, to identify the alteration parts related to the gold deposits, different satellite image processing methods of ETM + and ASTER were used. These methods include making a false-color composite, band ratio, Selective Principal Components Analysis (SPCA), Spectral Angle Mapper (SAM) method, Spectral Information Divergence Classification (SID), Endmember Collection Dialog Components (ECDC), and innovative methods such as Principal Component Analysis (PCA) and Spectral Angle Mapper, as well as unsupervised classification methods. In the end, the major alterations in the region were observed. In the obtained images, the prophylitic zone and the phyllic and argillic zones in the region were observed. To introduce the optimal method, the results of the various methods mentioned were compared with each other and with the current situation of the mines. The alteration zones were identified through band ratio and SAM methods and the combined methods with more power. Finally, SAM, 2:1 ratio, and the combined methods were identified as successful methods for more accurate separation of the alteration zones.

ASTER mapping of gypsum deposits of Thumrait region of southern Oman

AbstractThis study demonstrates the use of ASTER data for the mapping of gypsum deposits and associated geological formations that occurred in the Thumrait region of southern Oman. The measurement of spectra over samples of gypsum in the 1,300–2,500 nm wavelength using a PIMA spectrometer showed the presence of distinct absorptions at 1400–1600, 1750, 1940, 2,100, and 2,400 nm characteristics to OH stretching, H2O combinations, and SO bending overtones and stretching, respectively. Studying the unique spectral absorption characters of gypsum samples, we developed a false color composite (FCC) and an image by decorrelation stretch using the spectral bands 7, 3, and 2 of ASTER. The results FCC showed the regions of gypsum occurrences, and the decorrelated image discriminated the gypsum occurrences from other geological formations of the area. The study of surface mineralogy of the region using the VNIR‐SWIR bands by the spectral angle mapper method showed the presence of sulfate, carbonate, and clay minerals of the geological formations in the study area. We compared the results of ASTER with the results obtained using spectral bands 12, 8, and 4 of Sentinel‐2A processed by the same methods. The study showed that the spectral bands of ASTER can be used for mapping the gypsum and associated geological formations.

Comparison of Conventional Change Detection Methodologies Using High-Resolution Imagery to Find Forest Damage Caused by Typhoons

The number of intense tropical cyclones is expected to increase in the future, causing severe damage to forest ecosystems. Remote sensing plays an important role in detecting changes in land cover caused by these tropical storms. Remote sensing techniques have been widely used in different phases of disaster risk management because they can deliver information rapidly to the concerned parties. Although remote sensing technology is already available, an examination of appropriate methods based on the type of disaster is still missing. Our goal is to compare the suitability of three different conventional classification methods for fast and easy change detection analysis using high-spatial-resolution and high-temporal-resolution remote sensing imagery to identify areas with windthrow and landslides caused by typhoons. In August 2016, four typhoons hit Hokkaido, the northern island of Japan, creating large areas of windthrow and landslides. We compared the normalized difference vegetation index (NDVI) filtering method, the spectral angle mapper (SAM) method, and the support vector machine (SVM) method to identify windthrow and landslides in two different study areas in southwestern Hokkaido. These methodologies were evaluated using PlanetScope data with a resolution of 3 m/px and validated with reference data based on Worldview2 data with a very high resolution of 0.46 m/px. The results showed that all three methods, when applied to high-spatial-resolution imagery, can deliver sufficient results for windthrow and landslide detection. In particular, the SAM method performed better at windthrow detection, and the NDVI filtering method performed better at landslide detection.

Mapping and accuracy assessment of siltation of recharge dams using remote sensing technique

This study demonstrates the use of ASTER data to map the siltation of the Al-Khod Dam constructed at the lower reaches of Samail Catchment area, north Muscat, Sultanate of Oman. In this study, the decorrelated image of spectral bands 6, 3, and 1 clearly discriminated the distribution of siltation. The occurrence of siltation is confirmed by the detection of minerals using the VNIR-SWIR bands by the Spectral Angle Mapper (SAM) method. In addition, the siltation of the dam is studied for the years 1987 to 2018 using Landsat satellite images. Interpretation of images showed a gradual increase of the siltation. Mapping accuracy for the occurrence of siltation is assessed by a confusion matrix which produced an overall accuracy of 97.88% and Kappa coefficient of 0.98 in the Maximum Likelihood method. Results of image analyses are verified in the field and confirmed through laboratory analyses. The study of spectral absorption characters of field samples using a Portable Infrared Mineral Analyzer (PIMA) showed the presence of OH molecule bearing minerals (clays, serpentines, etc.) and carbonate minerals (calcite and dolomite) in the silt deposits. The occurrence of these minerals is confirmed further by X-ray diffraction (XRD) and Scanning Electron Microscope (SEM) analyses.

Integration of remote sensing, gravity and geochemical data for exploration of Cu-mineralization in Alwar basin, Rajasthan, India

In spite of the dominance of traditional mineral exploration methods that demand physical characterization of rocks and intense field work, remote sensing technologies have also evolved in the recent past to facilitate mineral exploration. In the present study, we have processed visible near infrared (VNIR) and shortwave infrared (SWIR) bands of Advanced space-borne thermal emission and reflection radiometer (ASTER) data to detect surface mineralization signatures in Mundiyawas - Khera area in Alwar basin, north-eastern Rajasthan, India using spectral angle mapper (SAM). The potential of SAM method to detect target under variable illumination condition was used to delineate galena, chalcopyrite, malachite etc. as surface signatures of mineralization. It was ensured that the identified surface anomalies were spectrally pure using pixel purity index. Spectral anomalies were validated in the field and also using X-Ray diffraction data. Spectral anomaly maps thus derived were integrated using weight of evidence method with the lineament density, geochemical anomaly, bouger anomaly maps to identify few additional potential areas of mineralization. This study thus establishes the importance of remote sensing in mineral exploration to zero in on potentially ore rich but unexplored zones.

Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data

Salt pans are highly dynamic environments that are difficult to study by in situ methods because of their harsh climatic conditions and large spatial areas. Remote sensing can help to elucidate their environmental dynamics and provide important constraints regarding their sedimentological, mineralogical, and hydrological evolution. This study utilizes spaceborne multitemporal multispectral optical data combined with spectral endmembers to document spatial distribution of surface crust types over time on the Omongwa pan located in the Namibian Kalahari. For this purpose, 49 surface samples were collected for spectral and mineralogical characterization during three field campaigns (2014–2016) reflecting different seasons and surface conditions of the salt pan. An approach was developed to allow the spatiotemporal analysis of the salt pan crust dynamics in a dense time-series consisting of 77 Landsat 8 cloud-free scenes between 2014 and 2017, covering at least three major wet–dry cycles. The established spectral analysis technique Sequential Maximum Angle Convex Cone (SMACC) extraction method was used to derive image endmembers from the Landsat time-series stack. Evaluation of the extracted endmember set revealed that the multispectral data allowed the differentiation of four endmembers associated with mineralogical mixtures of the crust’s composition in dry conditions and three endmembers associated with flooded or muddy pan conditions. The dry crust endmember spectra have been identified in relation to visible, near infrared, and short-wave infrared (VNIR–SWIR) spectroscopy and X-ray diffraction (XRD) analyses of the collected surface samples. According these results, the spectral endmembers are interpreted as efflorescent halite crust, mixed halite–gypsum crust, mixed calcite quartz sepiolite crust, and gypsum crust. For each Landsat scene the spatial distribution of these crust types was mapped with the Spectral Angle Mapper (SAM) method and significant spatiotemporal dynamics of the major surface crust types were observed. Further, the surface crust dynamics were analyzed in comparison with the pan’s moisture regime and other climatic parameters. The results show that the crust dynamics are mainly driven by flooding events in the wet season, but are also influenced by temperature and aeolian activity in the dry season. The approach utilized in this study combines the advantages of multitemporal satellite data for temporal event characterization with advantages from hyperspectral methods for the image and ground data analyses that allow improved mineralogical differentiation and characterization.