Spectral Information Divergence Research Articles

Early detection and lesion visualization of pear leaf anthracnose based on multi-source feature fusion of hyperspectral imaging.

Pear anthracnose, caused by Colletotrichum bacteria, is a severe infectious disease that significantly impacts the growth, development, and fruit yield of pear trees. Early detection of pear anthracnose before symptoms manifest is of great importance in preventing its spread and minimizing economic losses. This study utilized hyperspectral imaging (HSI) technology to investigate early detection of pear anthracnose through spectral features, vegetation indices (VIs), and texture features (TFs). Healthy and diseased pear leaves aged 1 to 5 days were selected as subjects for capturing hyperspectral images at various stages of health and disease. Characteristic wavelengths (OWs1 and OWs2) were extracted using the Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) algorithm. Significant VIs were identified using the Random Forest (RF) algorithm, while effective TFs were derived from the Gray Level Co-occurrence Matrix (GLCM). A classification model for pear leaf early anthracnose disease was constructed by integrating different features using three machine learning algorithms: Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN). The results showed that: the classification identification model constructed based on the feature fusion performed better than that of single feature, with the OWs2-VIs-TFs-BPNN model achieving a highest accuracy of 98.61% in detection and identification of pear leaf early anthracnose disease. Additionally, to intuitively and effectively monitor the progression and severity of anthracnose in pear leaves, the visualization of anthracnose lesions was achieved using Successive Maximum Angle Convex Cone (SMACC) and Spectral Information Divergence (SID) techniques. According to our research results, the fusion of multi-source features based on hyperspectral imaging can be a reliable method to detect early asymptomatic infection of pear leaf anthracnose, and provide scientific theoretical support for early warning and prevention of pear leaf diseases.

Hyperspectral endmember extraction using convexity based purity index

The endmember extraction is a challenging problem in spectral unmixing (SU) of a mixed pixel in hyperspectral imagery. There are plenty of attempts to solve the endmember extraction problem. Still, the pure pixel assumption-based algorithms have probably been used most in solving the endmember extraction of SU due to the light computational burden. These pure pixel assumption-based algorithms usually follow one of the criteria: (1) Maximum simplex volume or (2) Extreme projection on a subspace. We propose a novel integrated framework that uses both the criteria mentioned above and the proposed one is referred to as the Convexity-based Pure Index (CPI) algorithm. The CPI generates a fixed number of convex sets based on the number of available bands in the hyperspectral image. The algorithm defines the purity score based on the availability of pixels in the convex sets for the two-band data. The CPI has been compared with contemporary algorithms such as Automatic Target Generation Process (ATGP), Vertex Component Analysis (VCA), Pixel Purity Index (PPI), Successive Volume MAXimization (SVMAX), Alternating Volume MAXimization (AVMAX), TRIple-P: P-norm based Pure Pixel identification (TRIP), Successive Decoupled Volume Max–Min (SDVMM), Negative ABundance-Oriented (NABO), and Entropy-based Convex Set Optimization (ECSO). The metrics, Spectral Angle Distance (SAD) and Spectral Information Divergence (SID) used in the comparison were improved up to 5.9% and 9%, respectively. The CPI outperforms prevailing algorithms on real benchmark data and new AVIRIS-NG data. The robustness of the CPI is also tested for various noisy synthetic data. The efficacy of the proposed algorithm is also tested by using qualitative analysis by visualizing the spectra comparison, and abundance maps for all real data.

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%.

High-resolution cropland mapping in China’s Huang-Huai-Hai Plain: The coupling of machine learning methods and prior information

Numerous map products containing cropland data are currently available. However, due to their low temporal and spatial resolutions and limited accuracy, their practical applications are limited. Thus, research on the precise mapping of croplands with high temporal and spatial resolutions is still urgently needed. The Huang-Huai-Hai Plain, a critical grain production area in China, was used as the research area to produce high-resolution annual cropland maps from 2018 to 2022. Sentinel-2 imagery, digital elevation model (DEM) data, and China Agricultural Ecological Zoning (CAEZ) data were collected. Based on these data, the study area was stratified into different zones, and then the distinct characteristics and optimal timing for differentiating cropland from other land cover types within each zone were analyzed and used as prior information for guiding time-series image compositing. Furthermore, the spectral information divergence (SID) index was used to modify the existing sample migration technique to transfer training samples across years. Via the Google Earth Engine (GEE) platform, the random forest (RF) classification method and positive and unlabeled learning (PUL) method were combined to construct cropland maps from composited images and terrain information from DEMs. Compared with the seasonal or spectral-temporal metric (STM) image composition methods, the image composition method based on prior information significantly improved the distinction between cropland and other land cover types. Moreover, the newly proposed sample migration strategy substantially increased the accuracy of training samples transferred across years. Compared with existing cropland maps from products such as the ESA, ESRI, GlobeLand30, GLAD, and CLCD, the cropland maps generated in this study demonstrated superior accuracy, with average overall accuracy (OA) values from 88.81% to 91.66% for different zones, thus outperforming other products with OA values from 77.53% to 88.90%. This study supports the development of related agricultural and ecological research and provides a valuable reference for cropland mapping.

Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data

ABSTRACT Artificial intelligence (AI) began to make its way into geoinformation science several decades ago and since then has constantly brought forth new cutting-edge approaches for diverse geographic use cases. AI and deep learning methods have become essential approaches for land use and land cover (LULC) classifications, which are important in urban planning and regional management. While the extraction of LULC information from multispectral satellite images has been a well-studied part of past and present research, only a few studies emerged about the recovery of spectral properties from LULC information. Estimates of the spectral characteristics of LULC categories could enrich LULC forecasting models by providing necessary information to delineate vegetation indices or microclimatic parameters. We train two identical Conditional Generative Adversarial Networks (CGAN) to synthesize a multispectral Sentinel-2 image based on different combinations of open-source LULC data sets. Large-scale synthetic multispectral satellite images of the administrative region of Bonn and Rhein-Sieg in Germany are generated with a Euclidean distance-based patch-fusion method. The approach generated a realistic-looking satellite image without noticeable seams between patch borders. Based on several metrics, such as difference calculations, the spectral information divergence (SID), and the Fréchet inception distance (FID), we evaluate the resulting images. The models reach mean SIDs as low as 0.026 for urban fabrics and forests and FIDs below 90 for bands B2 and B5 showing that the CGAN is capable of synthesizing distinct synthetic features matching with features typical for respective LULC categories and manages to mimic multispectral signatures. The method used in this paper to generate large-scale synthetic multispectral satellite images can be used as an approach to support scenario-oriented sustainable urban planning.

Potentials of spaceborne imaging spectrometer PRISMA, ASTER, and ALOS-1 PALSAR datasets for mineral mapping and geological controls of mineral deposit in Jahazpur, Rajasthan, India

Palaeoproterozoic rocks of the Jahazpur region are well known for talc mineralization and its mining. The present study attempts to identify and map the talc and associated minerals and their structural control using multi-source remote sensing datasets. Remote Sensing studies were carried out using the relative band depth methods for the ASTER dataset for mapping talc and other associated minerals of the region. Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Minimum Noise Fraction (MNF) were applied to the PRISMA dataset to map the lithology of the region. Along with that, the Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) algorithms map the talc and clay minerals using the PRISMA dataset. ALOS – 1 PALSAR dataset had been utilized to delineate the various lineaments present in the region at different angles. The obtained mineral maps form the ASTER had been validated with results of the PRISMA classified map and showed a good number of similarities. The obtained results from remote sensing have been further validated with the help of field survey and XRD analysis which confirms the presence of talc and clay minerals in the region. The structural pattern of the lineament shows the similarity in the direction orientation with the Jahazpur Thrust. Dolomites and clay minerals were mapped around the talc mining region which suggests that the alteration of these minerals played an important role in the formation of talc. Clay minerals were also mapped there and formed due to the weathering of the granites of Berach and Jahazpur present in the region. Both talc and clay deposits are present at the higher lineament density zones which represents the structural control over the mineralization. Therefore, the mineral deposits of the Jahazpur region are mineralogical and structurally controlled.

The choice of spectral similarity algorithms influences suspected soil sample provenance

Similarity algorithms are commonly used in soil forensic applications to help identify similar samples from an existing reference library as possible source locations of unknown target samples. These algorithms are well-suited to compare soil spectra. However, different similarity algorithms may lead to different clusters of similar samples, and thus different strengths of evidence in forensic investigations. To quantify this, we conducted a study to evaluate the influence of seven similarity algorithms on soil provenance, using as a sample set a soil spectral library consisting of 280 soil profiles from Anhui Province, China. This library includes three spatial scales of datasets: provincial (DSp), county (DSc) and field (DSf). A set of ten samples covering a wide range of spectra variations were selected from the DSf dataset as the “unknown” samples, with the remaining being used as the reference samples. This study aimed to: (1) evaluate how several commonly-used similarity algorithms, namely Euclidean distance (ED), Mahalanobis distance (MD), Spectral angle mapper (SAM), and Spectral information divergence (SID), as well as variants of several of these measured in standardized principal component space computed from the spectra (ED_PCA, MD_PCA and SAM_PCA), influence the identification of the matched similar samples; (2) determine the overlap in sample selection between different similarity algorithms; (3) propose best practices for similarity algorithms applied to soil forensic analysis using spectroscopy. The use of different similarity algorithms did influence the selection of most similar samples. The similarity algorithms calculated in PC space (ED_PCA, MD_PCA and SAM_PCA) performed slightly better than their counterparts calculated in spectral space. Due to the availability of a detailed spectral library, regardless of the different similarity algorithms used, the matched most similar samples were all located close to the unknowns, mostly within 3 km, with one exception. That is, the varied choices of different similarity algorithms hardly influenced the conclusion of soil provenance in this case. In general, MD_PCA, SAM and ED were the best similarity algorithms overall. However, since there was no single best algorithms for all cases, we recommend the joint use of MD_PCA, SAM and ED as an ensemble. Indications of possible sample provenance from these similarity measured can be useful evidence to complement evidence from other methods in a forensic investigation.

Spectrum recovery from multispectral ocean color data using system spectral shape factor

Data from ocean color monitoring sensors at different spectral channels are available for remote sensing of radiation as seen in the given spectral windows, which is used for deriving information on various atmospheric parameters. However, recent studies have demonstrated the potential of hyperspectral (HS) data over multispectral ocean color (MSOC) data in accurately estimating phytoplankton concentration and in monitoring the coastal dynamics. We propose system spectral shape factor (SSSF)-based approach to recover the embedded HS top-of-atmosphere (TOA) radiance (TOARAD) from the MSOC data. SSSF is defined as convolution of normalized input spectrum and sensor spectral response function (SRF). The advantage of SSSF is that it decouples magnitude and spectral shape part of sensor output and enables recovery of TOARAD. To test this method, the airborne visible/infrared imaging spectrometer-next generation data are used to simulate inputs to MSOC. SRF of ocean color monitor simulated MSOC. SSSF of TOARAD is estimated using SSSF of model-based path radiance spectrum of the pixel, which is similar in spectral shape. Methodology, developed using data from five stations, is validated with data from other five stations. The procedure is successfully repeated using SRFs of sea-viewing wide-field-of-view sensor. The recovered HS data are found to be consistent with the original spectra with very small deviations in spectral angle map (<0.012 rad) spectral information divergence (<5.8 × 10 − 5), mean percentage relative error (MPRE) of TOARAD (<0.7 % ), and MPRE of TOA water leaving radiance (<5.8 % ). This approach possibly opens up research for application of HS analysis on MSOC recovered spectra and for optimization of sensor configurations.

Comparison of Information Criteria for Detection of Useful Signals in Noisy Environments.

This paper considers the appearance of indications of useful acoustic signals in the signal/noise mixture. Various information characteristics (information entropy, Jensen-Shannon divergence, spectral information divergence and statistical complexity) are investigated in the context of solving this problem. Both time and frequency domains are studied for the calculation of information entropy. The effectiveness of statistical complexity is shown in comparison with other information metrics for different signal-to-noise ratios. Two different approaches for statistical complexity calculations are also compared. In addition, analytical formulas for complexity and disequilibrium are obtained using entropy variation in the case of signal spectral distribution. The connection between the statistical complexity criterion and the Neyman-Pearson approach for hypothesis testing is discussed. The effectiveness of the proposed approach is shown for different types of acoustic signals and noise models, including colored noises, and different signal-to-noise ratios, especially when the estimation of additional noise characteristics is impossible.

Efficacy of AVIRIS-NG data for species-specific recognition towards a comparative analysis by hyperspectral classifiers

Due to increasing anthropogenic pressure on the forest ecosystem, many plant species face a threat, and many of them are rapidly getting extinct. Proper identification of tree species and understanding their geographical distribution are prerequisites towards its conservation and developing management plans. Species mapping and distribution have typically had restrict due to limitations in spectral information. Recently, hyperspectral imageries have solved this problem, but more satisfactory resolution and exact identification by different methods are still in discussion. Here, we used airborne AVIRIS-NG hyperspectral data to identify-five tree species, namely Butea monosperma, Diospyros melanoxylon, Tectona grandis, Terminalia bellirica, and Terminalia tomentosa. In this study, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) classifiers identify these tree species using spectral signatures with 92.44% and 74.55% overall accuracy; and 0.89 and 0.67 kappa coefficient, respectively. A comparative analysis is executed for both applied species identification algorithms, which shows importance of used identification techniques for a specific dataset and a relationship was also stabilized between overall accuracy of tree species and number of ground samples.

SI2FM: SID Isolation Double Forest Model for Hyperspectral Anomaly Detection

Hyperspectral image (HSI) anomaly detection (HSI-AD) has become a hot issue in hyperspectral information processing as a method for detecting undesired targets without a priori information against unknown background and target information, which can be better adapted to the needs of practical applications. However, the demanding detection environment with no prior and small targets, as well as the large data and high redundancy of HSI itself, make the study of HSI-AD very challenging. First, we propose an HSI-AD method based on the nonsubsampled shearlet transform (NSST) domain spectral information divergence isolation double forest (SI2FM) in this paper. Further, the method excavates the intrinsic deep correlation properties between NSST subband coefficients of HSI in two ways to provide synergistic constraints and guidance on the prediction of abnormal target coefficients. On the one hand, with the “difference band” as a guide, the global isolation forest and local isolation forest models are constructed based on the spectral information divergence (SID) attribute values of the difference band and the low-frequency and high-frequency subbands, and the anomaly scores are determined by evaluating the path lengths of the isolation binary tree nodes in the forest model to obtain a progressively optimized anomaly detection map. On the other hand, based on the relationship of NSST high-frequency subband coefficients of spatial-spectral dimensions, the three-dimensional forest structure is constructed to realize the co-optimization of multiple anomaly detection maps obtained from the isolation forest. Finally, the guidance of the difference band suppresses the background noise and anomaly interference to a certain extent, enhancing the separability of target and background. The two-branch collaborative optimization based on the NSST subband coefficient correlation mining of HSI enables the prediction of anomaly sample coefficients to be gradually improved from multiple perspectives, which effectively improves the accuracy of anomaly detection. The effectiveness of the algorithm is verified by comparing real hyperspectral datasets captured in four different scenes with eleven typical anomaly detection algorithms currently available.

A Review on different ML Techniques used for Disease Detection in Sugarcane Crop

Latest improvements in precision agriculture through machine learning, deep learning, remote sensing has helped to come up with different methods to detect crop diseases. One of the main reasons for yield loss of a crop is non detection of disease early in time. This paper reviews the various methods and techniques that can be used to detect diseases in sugarcane crop. Firstly, we provide a review on the different types of input data w.r.t imagery -RGB, multispectral and hyperspectral. Then we highlight the different techniques applied for disease detection-machine learning, deep learning, transfer learning and spectral information divergence. We also give an overview of the results achieved by using the different techniques.

Adaptive Reference-Related Graph Embedding for Hyperspectral Anomaly Detection

Graph embedding provides an effective way to reveal intrinsic feature of high-dimensional data on the foundation of preserving topological properties. Under the framework of graph embedding, the hyperspectral image can be represented by a weighted graph, where pixels and similarities among them are treated as vertices and edge weights, respectively. In this article, an adaptive reference-related graph embedding (ARGE) method is proposed to efficaciously obtain the low-dimensional feature and improve computational efficiency. The ARGE method is composed of two primary processes. The key to connecting these two processes is reference vertices set, which is the abstraction of graph topological features. Firstly, the reference vertices are adaptively selected through a three-step adaptive reference set selection (ARSS) algorithm. Secondly, the original high-dimensional graph is embedded as a low-dimensional graph through preserving the reference-related structure. Specifically, the pairwise similarities between vertices and reference vertices are preserved in embedding space. In addition, a new hybrid dissimilarity measure of Rao distance and spectral information divergence (RD-SID) is designed to depict the spectral difference between pixels. To evaluate the effectiveness of the proposed method, the obtained low-dimensional feature is fed into the anomaly detector to detect anomalous pixels. The experimental results on five real and one synthetic hyperspectral datasets demonstrate the superiority of the proposed ARGE method over the compared feature extraction methods.

IVIU-Net: Implicit Variable Iterative Unrolling Network for Hyperspectral Sparse Unmixing

At present, an emerging technique called the algorithm unrolling approach has attracted wide attention, because it is capable of developing efficient and interpretable layers to eliminate the black-box nature of deep learning (DL). In this paper, inspired by the sparse unmixing model, we propose a model-driven DL approach, namely, an implicit variable iterative unrolling network (IVIU-Net). First of all, the unmixing performance and adaptive ability of the model are enhanced by introducing learnable parameters into the sparse unmixing algorithm. Then, a specific spatial convolution module is integrated into the network to promote the smoothness of the latent abundance map. Finally, a comprehensive loss function with three terms such as average spectral angle distance, HSIs reconstruction error, and spectral information divergence, is presented to train the IVIU-Net in an unsupervised way. Compared to the unmixing results of most existing data-driven DL algorithms, our network has significant advantages in two folds: 1) it is able to achieve better stability instead of relying heavily on the endmember initialization results; 2) it has better interpretability and robustness in the unmixing procedure. Experimental results on synthetic and real data show that the proposed network outperforms the state-of-the-art in terms of better convergence, faster unmixing speed as well as better accuracy.

Crop-specific hyperspectral band selection method using limited ground-truth data

ABSTRACT Hyperspectral imaging plays a significant role in crop classification and aims to separate various crop pixels from the imagery. It aids the government in deciding agricultural policies. However, high spectral dimensions in hyperspectral data require high computing power and time. This paper presents a new band selection method based on spectral information divergence and correlation (), which selects optimum bands to classify the crops. The requires only single ground-truth pixels having the least spectral information divergence value to select the bands. This method requires not only minimum ground truth data but also gives reduced computational complexity. We have evaluated the proposed method on three hyperspectral datasets, AVIRIS-NG, Indian Pines and Salinas. We have used overall accuracy and kappa coefficient as performance parameters from the support vector machine and k-nearest neighbours classifiers. The experimental findings reveal that the proposed band selection method achieves maximum overall accuracy of about 84.79% for Indian Pines and 93.08% for Salinas dataset. The proposed methodology exhibits an improvement in overall accuracy when the number of selected bands ranges from 35 to 50 when compared with the other competitive band selection approaches.

Toward Robust River Plastic Detection: Combining Lab and Field‐Based Hyperspectral Imagery

AbstractPlastic pollution in aquatic ecosystems has increased dramatically in the last five decades, with strong impacts on human and aquatic life. Recent studies endorse the need for innovative approaches to monitor the presence, abundance, and types of plastic in these ecosystems. One approach gaining rapid traction is the use of multi‐ and hyperspectral cameras. However, most experiments using this approach were in controlled environments, making findings challenging to apply in natural environments. We present a method linking lab‐ and field‐based identification of macroplastics using hyperspectral data (1,150–1,675 nm). Experiments using riverbank‐harvested macroplastics were set up in a laboratory environment, and on the banks of the Rhine River. Representative pixel selections of eleven lab‐based images (n = 786,264 pixels) and two field‐based images (n = 40,289 pixels) were used to analyze the differences between these environments. Next, classifier algorithms such as support vector machines (SVM), spectral angle mapper (SAM) and spectral information divergence (SID) were applied, because of their robustness to varying light conditions and high accuracies in mapping spectral similarities. Our results showed that SAM classifiers are most robust in separating plastic pixels from background elements. By applying lab‐based data for plastic detection in field‐based images, user accuracies for plastics to up to 93.6% (n = 8,370 plastic pixels) were attained. This study provides key fundamental insights in linking lab‐based data to plastic detection in the field. With this paper we aim to contribute to the development of future spectral missions to detect and monitor plastic pollution in aquatic ecosystems.

Detection of DLBCL by pixel purity index and iterative linearly constrained minimum variance into hyperspectral imaging analysis.

It is unclear whether a hyperspectral imaging-based approach can facilitate the diagnosis of diffuse large B-cell lymphoma (DLBCL), and further investigation is required. In this study, the pixel purity index (PPI) coupled with iterative linearly constrained minimum variance (ILCMV) was used to bridge this gap. We retrospectively reviewed 22 pathological DLBCL specimens. Ten normal lymph node specimens were used as controls. PPI endmember extraction was performed to identify seed-training samples. ILCMV was then used to classify cell regions. The 3D receiver operating characteristic (ROC) showed that the spectral information divergence possessed superior ability to distinguish between normal and abnormal lymphoid cells owing to its stronger background suppression compared with the spectral angle mapper and mean square error methods. An automated cell hyperspectral image classification approach that combined the PPI and ILCMV was used to improve DLBCL diagnosis. This strategy intelligently resolved critical problems arising in unsupervised classification.

Evaluation of effectiveness of supervised classification algorithms in land cover classification using ASTER images-A case study from the Mankweng (Turfloop) Area and its environs, Limpopo Province, South Africa

The production of land cover maps using supervised classification algorithms is one of the most common applications of remote sensing. In this study, the effectiveness of supervised classification algorithms in land cover classification using ASTER data was evaluated in the Mankweng Area and its environs. The false colour composite image generated from combination of band 1, 2 and 3 in red, green and blue, respectively, was used to generate training classes for six land cover types (waterbody, forest, vegetation, Duiwelskloof leucogranite, Turfloop granite and built-up land). These were used to construct land cover maps using eight supervised classification algorithms: Maximum Likelihood, Minimum Distance, Support Vector Machine, Mahalanobis Distance, Parallelepiped, Neural Network, Spectral Angle Mapper and Spectral Information Divergence. To evaluate the effectiveness of the algorithms, the land cover maps were subjected to accuracy assessment to determine precision of the algorithms in accurately classifying the land cover types and level of confidence that can be attributed to the land cover maps. Most algorithms poorly performed in classifying spatially overlapping land cover types without abrupt boundaries. This indicates that the environmental conditions and distribution of land cover types can affect the performance of certain classification algorithms, and thus need to be considered prior to selection of algorithms. However, Support Vector Machine and Minimum Distance proved to be the two most effective algorithms as they provided better producer’s and user’s accuracy in the range of 80-100% for all land cover types, which represent good classification.

Mapping of gold-related alteration minerals and linear structures using ASTER data in the Giyani Greenstone Belt, South Africa

This paper focuses on the mapping of alteration minerals and linear structures associated with gold mineralization in the Giyani Greenstone Belt (GGB). Spectral Information Divergence (SID) and Linear Spectral Unmixing (LSU) classification algorithms were applied to the ASTER data using image extracted endmember spectra which match the spectral profiles of predominant alteration minerals (biotite and calcite) related to gold mineralization in the GGB. Directional convolution filters were also applied to the ASTER data to extract structural lineaments that may be related to gold mineralization. In addition, Fry analysis of known gold occurrences in the GGB was conducted to determine the major distribution patterns along which gold mineralization have occurred. LSU and SID results show several pixels classified as alteration minerals corresponding to known gold occurrences. SID results also show minerals overlapping into the gneissic rock, which is a rare host of gold mineralization in the study area. On the other hand, LSU results show high abundances of calcite and biotite generally confined within the mafic-ultramafic greenstone rocks, which have been hypothesized as the source of gold-mineralized fluids in the GGB. In general, LSU show distinct broad regions classified as alteration minerals corresponding to known gold occurrences that have been previously reported to host these alteration minerals. Directional convolution filters enabled the extraction of predominately NE-SW oriented linear structures, including the major shear zone which is associated with gold mineralization in the GGB. Fry analysis revealed three major distribution patterns: N-S, NE-SW, ENE-WSW along which gold mineralization have occurred. These patterns correspond to major lineaments associated with gold mineralization reported in the previous structural studies of the GGB.

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.