Alteration mineral mapping in inaccessible regions using target detection algorithms to ASTER data

Spectral mapping techniques were applied on shortwave infrared of advanced spaceborne thermal emission and reflection radiometer (ASTER) data for discriminating between hydrothermal alteration zones, and the identification of high potential mineralized lithologic unit associated with hydrothermal porphyry copper mineralization in the Central Iranian Volcanic Belt. In this study, shortwave infrared radiations of ASTER data were processed with spectral angle mapping, linear spectral unmixing, matched filtering, and mixture tuned matched filtering techniques. Two copper mining districts were used as reference to demonstrate the applicability of the spectral mapping techniques for spectral distinction of specific hydrothermal alteration zones such as argillic, phyllic and propylitic. The results of this study showed that spectral information can be used for generating spatial alteration mineral maps for purely virgin regions, and it can provide a cost-effective method to discriminate the new prospects for porphyry copper exploration prior to detailed and costly ground investigations. Key words: ASTER, spectral mapping techniques, hydrothermal alteration minerals, copper exploration.

Porphyry systems are critical sources of economically valuable metals, making their identification and exploration a key focus in mineral resource studies. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data have emerged as a powerful tool for mapping the geochemical and mineralogical characteristics of these systems, enabling more precise exploration efforts. This review examines the geochemical and mineralogical indicators of deep porphyry systems and evaluates the effectiveness of ASTER in their exploration. It begins by exploring the geological framework and alteration zones typical of porphyry deposits, emphasizing the unique advantages of ASTER in remote sensing applications. The methodology focuses on evaluating case studies where ASTER data were utilized to map alteration minerals and detect geochemical anomalies, alongside techniques for data processing and ground-truth validation. Results reveal that ASTER effectively identifies critical alteration zones and mineralogical features, though challenges remain in distinguishing overlapping mineral signatures in complex terrains. Recommendations include integrating ASTER with complementary geophysical and geochemical methods and advancing data processing techniques to enhance exploration accuracy. This study highlights ASTER’s significant potential in driving efficient and sustainable exploration of deep porphyry systems.

Our primary objective was to explore a classification algorithm for thermal hyperspectral data. Minimum noise fraction is applied to thermal hyperspectral data and eight pixel-based classifiers, i.e., constrained energy minimization, matched filter, spectral angle mapper (SAM), adaptive coherence estimator, orthogonal subspace projection, mixture-tuned matched filter, target-constrained interference-minimized filter, and mixture-tuned target-constrained interference minimized filter are tested. The long-wave infrared (LWIR) has not yet been exploited for classification purposes. The LWIR data contain emissivity and temperature information about an object. A highest overall accuracy of 90.99% was obtained using the SAM algorithm for the combination of thermal data with a colored digital photograph. Similarly, an object-oriented approach is applied to thermal data. The image is segmented into meaningful objects based on properties such as geometry, length, etc., which are grouped into pixels using a watershed algorithm and an applied supervised classification algorithm, i.e., support vector machine (SVM). The best algorithm in the pixel-based category is the SAM technique. SVM is useful for thermal data, providing a high accuracy of 80.00% at a scale value of 83 and a merge value of 90, whereas for the combination of thermal data with a colored digital photograph, SVM gives the highest accuracy of 85.71% at a scale value of 82 and a merge value of 90.

In this paper, we review and compare the state-of-the-art target detection algorithms. We introduce a new target detection workflow incorporating a Minimum Noise Fraction (MNF) transform before target detection. Applying a MNF transform was found to improve the detection results in general, especially with the Orthogonal Subspace Projection detector. In this paper, we propose a new algorithm - Mixture Tuned Target-Constrained Interference-Minimized Filter (MTTCIMF). MTTCIMF uses the MNF transformed image as the input and combines the mixture tuned technique with the TCIMF target detector. By adding an additional infeasibility band, mixture tuned techniques improve the detection results with a reduced number of false alarms. A HyMap data set with ground truth is used in the comparative study. Quantitative and visual evaluation of different algorithms is given. A new quantitative metric is proposed to evaluate the visibility of targets in the detection results. Keywords: target detection, mixture tuned matched filter (MTMF), mixture tuned target-constrained interferenceminimized filter (MTTCIMF), minimum noise fraction (MNF), adaptive coherence estimator (ACE), orthogonal subspace projection (OSP), constrained energy minimization (CEM), target visibility

Characterization of ASTER spectral bands for mapping of alteration zones of volcanogenic massive sulphide deposits

Extracting surface water distribution with satellite imagery has been an important subject in remote sensing. Spectral indices of water only use information from a limited number of bands, thus they may have poor performance from pixels contaminated by ice/snow, clouds, etc. The detection algorithms using information from all spectral bands, such as constrained energy minimization (CEM), could avoid this problem to some extent. However, these are mostly designed for hyperspectral imagery, and may fail when applied to multispectral data. It has been proved that adding linearly irrelevant data to original data could improve the performance of CEM. In this study, two kinds of linearly irrelevant data are added for water extraction: the spectral indices and the spectral similarity metric data. CEM is designed for targets with low-probability distribution in an image, but water bodies do not always satisfy this condition. We thereby impose a sensible coefficient for each pixel to form the weighted autocorrelation matrix. In this study, the weight is based on the orthogonal subspace projection, so this new method is named Orthogonal subspace projection Weighted CEM (OWCEM). The newly launched Landsat 8 images over two lakes, the Hala Lake in China with ice/snow distributed in the north, and the Huron Lake in North America, a lake with a very large surface area, are selected to test the accuracy and robustness of our algorithm. The Kappa coefficient and the receiver operating characteristic (ROC) curve are calculated as an accuracy evaluation standard. For both lakes, our method can greatly suppress the background (including ice/snow and clouds) and extract the complete water surface with a high accuracy (Kappa coefficient > 0.96).

Landsat Thematic Mapper and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data were used for lithological mapping and chromite prospecting in Neyriz ophiolitic zone in the south of Iran. Image transformation techniques, namely decorrelation stretch, band ratio, principal component analysis (PCA) were applied to the datasets. The RGB decorrelated image of Landsat TM bands 7, 5 and 4, and the principal components images of ASTER bands in the visible near infrared (VNIR) and shortwave infrared (SWIR) portions demonstrated the occurrence of major lithological units in the study area. The developed band ratios on the VNIR-SWIR spectral bands of ASTER, including 5/3, 5/1, 7/5 discriminated all rock units distinctly and delineated the host rock of chromite mineralized zone along the transition zone in the study area. Image processing results were verified by laboratory analysis and fieldwork. Result of this investigation indicate that the integration of Landsat TM and ASTER datasets is one of the most effective tool for lithological mapping and chromite prospecting in mountainous and inaccessible regions such Iranian ophiolitic zones.

Mapping of hydrothermal alterations related to gold mineralization within parts of the Malumfashi Schist Belt, North-Western Nigeria

The study area is located in the Kerman magmatic arc in southern Iran, which is known for its world-class porphyry-type deposits. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Land Imager (ALI) visible–near-infrared to shortwave-infrared bands were used to investigate the spectral discrimination of hydrothermally altered rocks based on their mineral assemblages. Band ratioing, directed principal component analysis (DPCA), and the Spectral Angle Mapper (SAM) were applied on ASTER and ALI data for separating ferric iron-poor from ferric iron-rich phyllic alteration zones. The individual principal component images through DPCA could detect specific alteration zones dominated by minerals such as iron oxides, sericite, kaolinite, chlorite, and epidote. The phyllic zone associated with copper mineralization is generally rich in iron oxide minerals at the surface, which can be especially detected by ALI. The altered areas were sampled and studied using X-ray diffraction analysis, spectral measurements, chemical analysis, and thin-section studies. The results of this analysis have shown that more than 90% of the known copper mineralization falls within the ASTER/ALI-mapped alteration areas. These data can be useful for mapping alteration minerals related to porphyry deposits in other regions with similar geological settings.

Alteration minerals and silicification are typically associated with a variety of ore mineralizations and could be detected using multispectral remote sensing sensors as indicators for mineral exploration. In this investigation, the Visible Near-Infra-Red (VNIR), Short-Wave Infra-Red (SWIR), and Thermal Infra-Red (TIR) bands of the ASTER satellite sensor derived layers were fused to detect alteration minerals and silicification in east the Kerdous inlier for cupriferous mineralization exploration. Several image processing techniques were executed in the present investigation, namely, Band Ratio (BR), Selective Principal Component Analysis (SPCA) and Constrained Energy Minimization (CEM) techniques. Initially, the BR and SPCA processing results revealed several alteration zones, including argillic, phyllic, dolomitization and silicification as well as iron oxides and hydroxides. Then, these zones were mapped at sub-pixel level using the CEM technique. Pyrophyllite, kaolinite, dolomite, illite, muscovite, montmorillonite, topaz and hematite were revealed displaying a significant distribution in relation with the eastern Amlen region lithological units and previously detected mineral potential zones using HyMap imaging spectroscopy. Mainly, a close spatial association between iron oxides and hydroxide minerals, argillic, and phyllic alteration was detected, as well as a strong silicification was detected around doleritic dykes unit in Jbel Lkest area. A weighted overlay approach was used in the integration of hydrothermal alteration minerals and silicification, which allowed the elaboration of a new mineral alteration map of study area with five alteration intensities. ASTER and the various employed processing techniques allowed a practical and cost effective mapping of alteration features, which corroborates well with field survey and X-ray diffraction analysis. Therefore, ASTER data and the employed processing techniques offers a practical approach for mineral prospection in comparable settings.

Abstract. Geothermal systems are essentially associated with hydrothermal alteration mineral assemblages such as iron oxide/hydroxide, clay, sulfate, carbonate and silicate groups. Blind and fossilized geothermal systems are not characterized by obvious surface manifestations like hot springs, geysers and fumaroles, therefore, they could not be easily identifiable using conventional techniques. In this investigation, the applicability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were evaluated in discriminating hydrothermal alteration minerals associated with geothermal systems as a proxy in identifying subtle Geothermal systems at Yankari Park in northeastern Nigeria. The area is characterized by a number of thermal springs such as Wikki and Mawulgo. Feature-oriented Principal Component selection (FPCS) was applied to ASTER data based on spectral characteristics of hydrothermal alteration minerals for a systematic and selective extraction of the information of interest. Application of FPCS analysis to bands 5, 6 and 8 and bands 1, 2, 3 and 4 datasets of ASTER was used for mapping clay and iron oxide/hydroxide minerals in the zones of Wikki and Mawulgo thermal springs in Yankari Park area. Field survey using GPS and laboratory analysis, including X-ray Diffractometer (XRD) and Analytical Spectral Devices (ASD) were carried out to verify the image processing results. The results indicate that ASTER dataset reliably and complementarily be used for reconnaissance stage of targeting subtle alteration mineral assemblages associated with geothermal systems.

The Urumieh-Dokhtar Magmatic Belt (UDMB) is the host of many Iranian Cu porphyry deposits. The southern part of this belt, in the Jebal-Barez area, has been less investigated due to the mountainous and rough topography as well as some security confrontations. Porphyry deposits are associated with hydrothermal alteration zones, which can be mapped using the remote sensing data such as Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER). The aim of this study was to identify and detect the alteration zones associated with porphyry systems using ASTER data in Jebal-Barez area, SE Iran. For this reason, seven scenes of ASTER level-1B data which cover the Jebal-Barez area, were pre-processed and then processed by band ratio, false color composite and Spectral Angle Mapper (SAM) methods. The result indicated the presence of extensive phyllic and argillic alteration zones. The argillic alteration is surrounded by border zones of propylitic alteration. Also, a massive granitoid intrusion in NW of the studied area was detected by SAM method and the color composite of 4, 6, and 8 bands of ASTER as a completely altered area (SF area) which strongly needs more detailed exploration and field inspections.

Kernel-based approaches have recently drawn considerable interests in hyperspectral image analysis due to its ability in expanding features to a higher dimensional space via a nonlinear mapping function. Many well-known detection and classification techniques such as Orthogonal Subspace Projection (OSP), RX algorithm, linear discriminant analysis, Principal Components Analysis (PCA), Independent Component Analysis (ICA), have been extended to the corresponding kernel versions. Interestingly, a target detection method, called Constrained Energy Minimization (CEM) which has been also widely used in hyperspectral target detection has not been extended to its kernel version. This paper investigates a kernel-based CEM, called Kernel CEM (K-CEM) which employs various kernels to expand the original data space to a higher dimensional feature space that CEM can be operated on. Experiments are conducted to perform a comparative analysis and study between CEM and K-CEM. The results do not show K-CEM provided significant improvement over CEM in detecting hyperspectral targets but does show significant improvement in detecting targets in multispectral imagery which provides limited spectral information for the CEM to work well.

The realtime implementation is discussed for several detection and classification techniques: Orthogonal Subspace Projection (OSP), Filter Vector Algorithm (FVA), Generalized Likelihood Ratio Test (GLRT), RX algorithm, Constrained Energy Minimization (CEM), Target Constrained Interference Minimization Filter (TCIMF), and Constrained Linear Discriminant Analysis (CLDA). Two data dimensionality limitations are met in realtime processing. One is the number of classes to be classified cannot be larger than data dimensionality, i.e., the number of spectral bands (for some techniques), and the other is the number of independent pixel vectors used for processing must be larger than the number of bands for a data sample correlation or covariance matrix with full rank (for some techniques). In this paper, we present methods to take care of these two limitations: the former is solved by generating artificial band images to expand the data dimensionality, while the latter is solved by using a positive definite correlation matrix as initial matrix. Experiments using hyperspectral data and multispectral data demonstrate the effectiveness of these methods.

Object detection from hyperspectral images (HSIs) is an important issue but encounters a critical challenge that results in poor detection due to the variation of the detection object spectrum. Especially when the detection object area is large and widely distributed in HSIs, such spectral variability becomes more serious. Spectral variability can make false detection and leak detection in object detection very serious. The constrained energy minimization (CEM) algorithm is a classical object detection algorithm that only needs the object prior spectrum to achieve object detection, but the spectral variability will have a detrimental effect on the detection results of the CEM algorithm. To address the above problems, we propose a multiobject subspace projection sample weighted CEM (MSPSW-CEM) algorithm. The proposed method has the following capabilities: (1) it constructs object subspaces and detectors using multiple prior spectra of the detection object under spectral variability conditions and (2) it utilizes the subspace projection theory to weight the pixel spectra, so that the detector can better suppress the background information and highlight the object information. Extensive experiments were carried out on two sets of real-world HSIs, and it was found that MSPSW-CEM generally showed a better detection performance than other object detection methods.