False Color Research Articles

Deep Learning Based Panchromatic2RGB Image Generation from VHR Images

Image colorization is the process of obtaining colored images by assigning RGB color values to a grayscale or panchromatic image. This technique has an important place in the field of computer vision because colored images provide a better visual experience and are widely used in areas such as image recognition and object detection. It also has many practical applications such as coloring historical photographs, adding colors to be used in the analysis of medical images, and improving the analysis of satellite images. Colorization methods are divided into two main categories: brush coloring and sample-based coloring. Both methods have certain limitations. The performance of these methods depends on the selected reference images and may sometimes contain false colors or significant errors. While these methods require operator intervention or pre-defined rules, deep learning based methods are largely automated and uses neural networks to understand the global and local context of an image, leading to more realistic and contextually accurate colorizations. The presented study uses the Denoising Diffusion Null-Space Model (DDNM) architecture. DDNM is a method that aims to obtain more efficient and high-quality results compared to the coloring approaches available in literature. In the study, the weight data of the DDNM architecture was used to predict colored images from panchromatic images using the SpaceNet 6 open access dataset. The SpaceNet 6 dataset includes a combination of Capella Space 0.5m Synthetic Aperture Radar (SAR) imagery and Maxar's 0.5m electro-optical (EO) imagery. In order to assess the results, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) accuracy metrics are calculated.

Analysis of differential weathering patterns and causes on the top of hollow enemy towers’ facades of the Great Wall – a case study of section in Haigang District, Qinhuangdao City

ABSTRACT The hollow enemy towers are iconic structures of the Great Wall. During a survey of the Ming Great Wall in Haigang District of Qinhuangdao, researchers observed differential weathering phenomena in the facade tops of 21 out of 51 well-preserved hollow enemy towers. Initially, high-precision 3D data of enemy towers was acquired using close-range photogrammetry from drones. The 3D surface metrology analysis software Mountains was introduced, in which the particle analysis module can use false-color images to calibrate and measure depressions on the 3D model surface, thereby enabling the identification and quantitative analysis of weathering and erosion areas on the top of the enemy towers’ facades. The study revealed that the upper regions of facades with the drainage troughs exhibited more pronounced weathering erosion compared to the areas without drainage troughs. Upon conducting an analysis of the structural integrity of the top drainage components and evaluating the slope and surface of the drainage system, it was determined that the primary water collection zones were located near the drainage troughs at both ends of enemy tower tops. Differential weathering at the top of enemy towers’ facades is primarily influenced by the varying amount of water accumulation, which serves as the decisive factor.

Augmented reality for rhinoplasty: 3D scanning and projected AR for intraoperative planning validation.

Rhinoplasty is one of the major surgical procedures most popular and it is generally performed modelling the internal bones and cartilage using a closed approach to reduce the damage of soft tissue, whose final shape is determined by means of their new settlement over the internal remodelled rigid structures. An optimal planning, achievable thanks to advanced acquisition of 3D images and thanks to the virtual simulation of the intervention via specific software. Anyway, the final result depends also on factors that cannot be totally predicted regarding the settlement of soft tissues on the rigid structures, and a final objective check would be useful to eventually perform some adjustments before to conclude the intervention. The main idea of the present work is the using of 3D scan to acquire directly in the surgical room the final shape of the nose and to show the surgeon the differences respect to the planning in an intuitive way using augmented reality (AR) to show false colours directly over the patient face. This work motivates the selection of the devices integrated in our system, both from a technical and an ergonomic point of view, whose global error, evaluated on an anthropomorphic phantom, is lower than±1.2mm with a confidence interval of 95%, while the mean error in detecting depth thickness variations is 0.182mm.

Lithological Mapping of the Neoproterozoic Basement Rocks in the Area East El-Sibai Core Complex (SCC), Central Eastern Desert of Egypt, Based on Remote Sensing Data and Field Observations

Spectral data are an active tool for mineral exploration, lithological, and structural mapping by spectral classification; due to its accuracy, ease of use, and low cost, especially in inaccessible areas. This study aims to integrate Landsat-8/OLI and ASTER images with extensive field survey and petrographical analysis to discriminate the Neoproterozoic basement rock units in the area east Gabal El-Sibai. In image enhancement techniques, we used the Correlation Coefficient and Optimum Index Factor for choosing the best-colored images to discriminate various rock units based on spectral signature curves. The exposed rock units consist of granitic rocks of variable compositions intruded directly into ophiolitic and island arc associations. The different rock units and structural elements in this study were mapped and discriminated using False Color Composites images (7-6-1 of Landsat-8 and 9-4-3 of ASTER), Minimum Noise Fraction (6-5-1), Principal Component Analysis (4-1-2 for Landsat-8 and 3-1-4 for ASTER), two newly proposed ratios images (3/4, 6/7, 5/6 for Landsat-8 and 4/1, 3/1, 8/9 for ASTER), with the Maximum Likelihood Classification and Support Vector Machine Classification. In conclusion; the overall accuracy and the Kappa coefficient show a good agreement with the Principal Component Analysis and Band Ratio results in the current geological map. Therefore, the suggested methodology shows that optical data have a strong potential for discriminating between lithological units. This approach provides accurate information to geologists, geophysical engineers, and land-use land-cover decision-makers. It can also improve and enhance the accuracy of geological maps in the Nubian Shield in Egypt and other arid environments worldwide.

Potential of model-based polarimetric decomposition extended with multi-frequency and multi-incidence PolSAR observations

ABSTRACT This letter attempts to extend the model-based polarimetric decomposition (PD) theorems with multiple polarimetric synthetic aperture radar (PolSAR) observations. To consider both the surface and dihedral scattering depolarization effects, the X-Bragg model, the extended double Fresnel scattering model, and the step-wise volume scattering model are introduced in the model-based PD. The proposed methodology is explored by exploiting Airborne Synthetic Aperture Radar (AIRSAR) C-band and L-band data from southern Oklahoma, United States of America, and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric data from Winnipeg, Canada, with two flight lines of 31605-03 and 31606-03 in different incidence angles. The potential and superiority of the proposed decomposition method are examined by illustrating the false color composition images, and the power ratio statistics of surface, dihedral, and volume scattering components over selected bare soil, forest, grass, and urban patches.

Application of PlanetScope Imagery for Flood Mapping: A Case Study in South Chickamauga Creek, Chattanooga, Tennessee

Floods stand out as one of the most expensive natural calamities, causing harm to both lives and properties for millions of people globally. The increasing frequency and intensity of flooding underscores the need for accurate and timely flood mapping methodologies to enhance disaster preparedness and response. Earth observation data obtained through satellites offer comprehensive and recurring perspectives of areas that may be prone to flooding. This paper shows the suitability of high-resolution PlanetScope imagery as an efficient and accessible approach for flood mapping through a case study in South Chickamauga Creek (SCC), Chattanooga, Tennessee, focusing on a significant flooding event in 2020. The extent of the flood water was delineated and mapped using image classification and density slicing of Normalized Difference Water Index (NDWI). The obtained results indicate that PlanetScope imagery performed well in flood mapping for a narrow creek like SCC, achieving an overall accuracy of more than 90% and a Kappa coefficient of over 0.80. The findings of this research contribute to a better understanding of the flood event in Chattanooga and demonstrate that PlanetScope imagery can be utilized as a very useful resource for accurate and timely flood mapping of streams with narrow widths.

Multi-Analytical Characterization of Illuminated Choirbooks from the Royal Audience of Quito

Choirbooks are historical heritage manuscripts used for the performance of vocal music in religious ceremonies in colonial times. This study aimed to understand the characteristics of choirbook manuscripts produced in the Real Audiencia de Quito during the 17th century. The methodology combined non-invasive techniques, such as infrared false-color imaging (IRFC) and X-ray fluorescence (XRF), together with spot analysis by scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) and Fourier transform infrared spectroscopy with attenuated total reflection (FTIR-ATR). The analytical results revealed the use of pumice, chalk and lime carbonate as support materials in the manufacturing process and surface treatment of the parchment. In the illuminations, three pictorial techniques based on protein, polysaccharide and lipid binders were recognized, establishing that the pigments used with greater regularity in the illuminations were vermilion, minium, verdigris, orpiment, azurite, and indigo, preferably in a pure state. Materials used less regularly were also identified, such as yellow ochre, saffron, smalt, red ochre, and bone black, among others. Regarding the vulnerability of the pictorial materials, it was determined that, although most of the pigments exhibit chemical stability, they present some vulnerabilities associated with their intrinsic composition and the medium that contains them.

ASTER remote sensing data for detecting porphyry copper type alteration patterns in West Zafarghand Cu±Mo deposit, Iran

ABSTRACT Porphyry deposits are typically associated with specific hydrothermal alteration zones. The identification of hydrothermal alteration patterns in porphyry deposits using remote sensing imagery is a cost-effective tool to identify zones of high potential zones. The West Zafarghand porphyry is located in the northeast of Isfahan, central part of the Cenozoic Urumieh-Dokhtar magmatic arc. Lack of interpretation of the spatial pattern of hydrothermal alteration zones and their relationships to ore mineralisation in the study area has led us to use ASTER remote sensing data to detect alteration patterns. Delineating the extent of hydrothermal alteration zones and high-grade mineralised lithological units is a particular goal of this study. We used various image processing techniques such as False Color Composite, Band Ratio, Principal Component Analysis, Minimum Noise Fraction and Spectral Angle Mapper. Remote sensing results, field observations, petrographic surveys and XRD revealed argillic, phyllic and propylitic hydrothermal alteration zones in the central part of the study area. An overall accuracy of 90% and a kappa coefficient of 0.84 were achieved in this analysis. The spatial distribution of the NE-SW trending phyllic alteration zone discovered in this study could be considered an area of high potential for a comprehensive exploration campaign at this deposit.

Natural Cover suppression patterns used in PRODES Caatinga mapping

Abstract. Since 2022, we have mapped native vegetation removal for all Brazilian biomes within the scope of the PRODES Project. In this study, our main objective was to present interpretation criteria for feature detection used to identify natural vegetation removal throughout the Caatinga. To this end, we created a catalog of typical patterns that can be used for supervised and unsupervised deforestation mapping. We used PRODES data from 2023 year's PRODES Caatinga based on the composition of top of atmosphere (TOA) reflectance images from MSI/Sentinel-2 satellite. The original reflectance data was used to generate false color images in the following combination: band 8 (Red), band 11 (Green) and band 4 (Blue). This image was then visually classified into different natural vegetation removal classes. These are good examples of what PRODES interpreters have encountered throughout this diverse region and can help any future mapping efforts of the Caatinga.

Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.

The purpose of this study is to investigate the impact of using morphological information in classifying suspicious breast lesions. The widespread use of deep transfer learning can significantly improve the performance of themammogram based CADx schemes.However, digital mammograms are grayscale images, while deep learning models are typically optimized using the natural images containing three channels. Thus, it is needed to convert the grayscale mammograms into three channel images for the input of deep transfer models. This study aims to developa novel pseudo color image generation methodwhich utilizesthe mass contour information to enhance the classification performance.Accordingly, a total of 830 breast cancer cases were retrospectively collected, which contains 310 benign and 520 malignant cases, respectively. For each case, a total of four regions of interest (ROI) are collected from the grayscale images captured for both the CC and MLO views of the two breasts. Meanwhile, a total of seven pseudo color image sets are generated as the input of the deep learning models, which arecreated through a combination of the original grayscale image, a histogram equalized image, a bilaterally filtered image, and a segmented mass. Accordingly, the output features from four identical pre-trained deep learning models are concatenated and then processed by a support vector machine-based classifier to generate the final benign/malignant labels. The performance of each image set was evaluated and compared. The results demonstrate that the pseudo color sets containing the manually segmented mass performed significantly better than all other pseudo color sets, which achieved an AUC (area under the ROC curve) up to 0.889 ± 0.012 and an overall accuracy up to 0.816 ± 0.020, respectively. At the same time, the performance improvement is also dependent on the accuracy of the mass segmentation. The results of this study support our hypothesis that adding accurately segmented mass contours can provide complementary information, thereby enhancing the performance of the deep transfer model in classifying suspicious breast lesions.

Virtual cleaning of sooty mural hyperspectral images using the LIME model and improved dark channel prior

Murals, as important carriers of cultural heritage and historical records, showcase artistic, aesthetic, social, and political significance. In ancient times, religious activities such as burning incense and candles in temples led to many murals being polluted by soot, causing them to darken, lose details, and, in severe cases, completely blacken. As a result, the development of efficient virtual cleaning methods has become a key strategy for addressing this issue. In this study, we use synthetic true colour and false colour images in different bands of the hyperspectral spectrum, and use a guided filter fusion technique to fuse these two images into a new image of the sooty mural. Through analyzing the histograms and colour distribution scatterplots of the synthetic sooty mural images, we observed significant similarities to low-luminance images. To enhance the synthesized murals, we applied the LIME model. In addition, comparisons of the histograms and colour distribution scatterplots of the enhanced sooty mural images with those of haze images revealed notable similarities. Therefore, we applied the dark channel prior algorithm to remove soot from the mural images. Considering that soot particles are larger than haze particles, we introduced guided filtering to refine the transmission map and created a nonlinear transformation function to enhance its details. In terms of both visual perception and quantitative analysis, the proposed method significantly outperforms previous methods in the virtual cleaning of sooty murals. This technology can not only restore the colours and details of murals but also provide new clues for subsequent mural studies, allowing people to once again appreciate the true beauty of the murals.

Conspiratorial Narratives and Ideological Constructs in the Russia–Ukraine Conflict: From the New World Order to the Golden Billion Theories

This article explores the pervasive influence of conspiracy theories, specifically the New World Order (NWO) and Golden Billion theories, within the context of the ongoing conflict between Russia and Ukraine. These theories form key narrative frameworks in Russian state media and global conspiracy communities, shaping perceptions of geopolitical events. This study dissects four pivotal episodes within the Russia–Ukraine conflict to illustrate how conspiracy theories shape public perception and policy direction, further entrenching ideological divides. In the first episode of the 2022 full-scale invasion, narratives of the Golden Billion were utilised to justify the attack, presenting Russia as a bastion against the Western elite’s plans to dominate the global economy and resources. The second episode examines the attack on Mariupol in 2022, framed by Russian propaganda as a necessary act to thwart the supposed expansion of NATO and the EU, underpinned by the NWO agenda aiming to dilute Russian influence in Eastern Europe. The third episode analyses the Nord Stream pipeline sabotage in 2022, interpreted by some conspiracy theorists as an act by the NWO to destabilise Europe’s energy security, thus consolidating control over energy routes and resources. The fourth episode delves into the 2024 Moscow terrorist attacks, which were seen by some as either a false flag operation conducted by Western powers or as a legitimate repercussion of Western encroachment orchestrated to weaken Russia’s resolve and international standing. Each episode is contextualised within a broader conspiratorial framework, highlighting the dualistic nature of the NWO and Golden Billion theories that paint the conflict not merely as territorial disputes but as a clash between fundamentally opposing worldviews and global orders. This narrative analysis not only underscores the role of conspiracy theories in shaping geopolitical discourse but also demonstrates their utility in mobilising domestic support, framing international criticism, and justifying military actions. Our findings suggest that these conspiratorial narratives provide a resilient, albeit misleading, lens through which supporters of the Kremlin’s policies can rationalise the war, attributing complex sociopolitical dynamics to the malevolent machinations of a global elite. This study contributes to understanding how modern conflicts are interpreted through ancient conspiratorial lenses, impacting national and international policy and public opinion.

Advanced exploration of rare metal mineralization through integrated remote sensing and geophysical analysis of structurally-controlled hydrothermal alterations

Fusing multi-source (remote sensing and geophysical) data and diverse approaches validation in targeting hydrothermal alteration and structural anomalies enhances the potential for accurately detecting and characterizing mineralization zones. Sentinel 2 data and ASTER were processed for lithological and hydrothermal alteration mapping in the rare metal-rich Umm Naggat area (Egypt). Different image processing techniques were implemented, including false color composites, minimum noise fraction, band rationing, band math, mineral indices, relative absorption band depth, and constrained energy minimization. The rare metal-bearing Umm Naggat younger granite (NYG) pluton was lithologically discriminated and intra-differentiated to mafic-rich biotite granites, mafic-poor alkali feldspar granites, and albitized granites. Extensive hydrothermal alterations, such as albitization, ferrugination, propylitization, argillization, and phyllitization, overprint the NYG pluton. Normalized standard deviation, automatic lineament extractions, and trend analysis highlighted the key structural directions (NW, NNW, NNE, and NE) and distinguished the NYG pluton as a moderate to high structural density zone. The high structural density and intensive alteration zones are spatially associated and more localized within the NYG pluton than the surrounding rocks. Spatial overlay analysis confirmed that the hydrothermal alterations and fluid circulation systems are structurally-controlled. Furthermore, the hydrothermal alteration mapping and structural analysis outcomes were verified by combining fieldwork, slab polishing, petrographic investigations, and mineral chemistry through semi-quantitative scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) and quantitative electron probe microanalysis (EPMA) analysis. As a result, the hydrothermal genesis of rare metal-bearing minerals (Nb-rutile, Nb-ilmenite, and columbite) close to or incorporated within alteration minerals (chlorite, muscovite, and hematite) is confirmed from the alteration zones (propylitic, phyllic, and ferruginated). In addition, biotite muscovitization and chloritization significantly contribute to the secondary rare metal enrichment. The current study emphasizes the extensive distribution of secondary rare metal-bearing minerals within the entire NYG pluton (not only limited to the northern albitized granite as depicted by previous studies), which might shed light on these hydrothermally-altered younger granites as a new potential source for Nb and Ta in Egypt.

Reflectance spectrometry and multispectral data for mapping fractures and hydrothermal alterations in the northern edge of the eastern High Atlas, Morocco

The Marginal Folds, in the north-western axis of Moroccan eastern High Atlas, are mainly composed of the Infra-Cenomanian terrains. These latter contain continental detrital deposits (red beds) and are characterized by a network of fractures. These marginal folds host significant concentrations of metallic minerals, such as Cu, Pb, Ag, and Zn, particularly at the Merija (Cu) and Bou Sellam (Pb-Zn) sites.Several techniques were applied to the Landsat-8 Oli images to improve the clarity and visibility of the linear features. Radiometric and atmospheric corrections, color compositions, directional filters, and principal component analysis were adopted. The lineaments resulting from automatic extraction, as well as their statistical analysis were validated by visual interpretation and field investigations. Statistical analysis revealed three dominants fracture systems: N-S, NE-SW, and E-W; the first two systems show a high density in the study area.The mineral alteration of the outcropping rocks is well described by ASTER multispectral imaging techniques. In this work, the effectiveness of this techniques for mineral alteration mapping was demonstrated on the Merija area. Several occurrences of mineral alteration were highlighted, using a combination of false color and band ratios, the principal component analysis (PCA), and the band ratios. This research was completed by reflectance spectrometry, covering the visible, near-infrared (VNIR), and short-wave infrared (SWIR) domains, to identify mineral alteration.Hydrothermal alteration in the Merija region includes clay, phyllite, carbonate, and phyllosilicate alteration, such as kaolinite, illite, montmorillonite, palygorskite, and vermiculite. Regarding to oxidation, as evidenced by the presence of goethite, jarosite, and hematite, it is widespread in most of the analyzed samples, resulting from the alteration of pyrite, followed by jarosite and goethite.

Insurrectionary Anarchism in Poland: The Case of the People's Liberation Front

The regime change in 1989 transformed nearly all elements of life in Poland, and the anarchist movement was not an exception. This article focuses on the Peoples’ Liberation Front, the first post-war Polish anarchist group to use violence as its modus operandi. Their actions were rejected en masse by the rest of the Polish anarchist movement, and in particular by the Anarchist Federation, which was establishing itself as the dominant organisation within the Polish anarchist movement. The functioning of the Peoples’ Liberation Front was and still is submerged in controversy. One allegation is that it was a false flag operation sponsored by the newly established civil counterintelligence service. Another view is that it was defined by a series of actions undertaken by youth fascinated by violence.

Spectrometric and remote sensing investigations of some granitic rocks in the Egyptian north Eastern Desert: Insights on environmental radiogenic heat production

Granitic rocks dominate the Neoproterozoic outcrops in the northern Egyptian Eastern Desert, prominently featuring two main categories: Arc Granitoids (AG) and late-to post-collision granites (LPCG). The AG range from granodiorite and tonalite to quartz-diorite. In contrast, LPCG comprise syenogranite, monzogranite, and alkali-feldspar granite. This study leverages Landsat-8 remote sensing data to effectively discriminate between these rock types using several advanced image processing techniques. False color composite and decorrelation stretch methods highlighted geological and structural features, revealing distinct spectral signatures for each rock type. Principal Component Analysis and band rationing further refined distinguishing various varieties within the LPCG and detailed mapping. Supervised classification using the Support Vector Machine method yielded precise delineation of rock units. The investigated granitic rocks exhibited estimated radiogenic heat production values ranging from 6.02 to 1.41 μW/m3, surpassing the average values observed in the Earth's crust. The reason behind these noteworthy surpassing values of radiogenic heat production can be directly attributed to the relatively high Gamma-ray measurements in the LPCG outcrops. Gamma-ray spectrometric analysis indicated varying distributions of radioelements, particularly between AG and LPCG. The equivalent uranium (eU) concentrations range from 2.8 to 7 ppm in AG, while LPCG exhibited broader variability from 5.1 to 34 ppm. The equivalent thorium (eTh) values range from 16.1 to 34.1 ppm, with an overall average of 23 ppm. Conversely, within the ferruginated-silicified domains, the LPCG display slightly elevated levels of eU, reaching 31.4, 35.5, and 27.8 ppm for the monzogranites, syenogranites, and alkali-feldspar granites, respectively. These elevated levels suggest the potential for iron oxy-hydroxide minerals to adsorb uranium within alteration zones. Additionally, radioactive minerals such as zircon, columbite, uranothorite, allanite, euxenite, and samarskite contribute to the observed spot anomalies.

Assessment of Dataset Scalability for Classification of Black Sigatoka in Banana Crops Using UAV-Based Multispectral Images and Deep Learning Techniques

This paper presents an evaluation of different convolutional neural network (CNN) architectures using false-colour images obtained by multispectral sensors on drones for the detection of Black Sigatoka in banana crops. The objective is to use drones to improve the accuracy and efficiency of Black Sigatoka detection to reduce its impact on banana production and improve the sustainable management of banana crops, one of the most produced, traded, and important fruits for food security consumed worldwide. This study aims to improve the precision and accuracy in analysing the images and detecting the presence of the disease using deep learning algorithms. Moreover, we are using drones, multispectral images, and different CNNs, supported by transfer learning, to enhance and scale up the current approach using RGB images obtained by conventional cameras and even smartphone cameras, available in open datasets. The innovation of this study, compared to existing technologies for disease detection in crops, lies in the advantages offered by using drones for image acquisition of crops, in this case, constructing and testing our own datasets, which allows us to save time and resources in the identification of crop diseases in a highly scalable manner. The CNNs used are a type of artificial neural network widely utilised for machine training; they contain several specialised layers interconnected with each other in which the initial layers can detect lines and curves, and gradually become specialised until reaching deeper layers that recognise complex shapes. We use multispectral sensors to create false-colour images around the red colour spectra to distinguish infected leaves. Relevant results of this study include the construction of a dataset with 505 original drone images. By subdividing and converting them into false-colour images using the UAV’s multispectral sensors, we obtained 2706 objects of diseased leaves, 3102 objects of healthy leaves, and an additional 1192 objects of non-leaves to train classification algorithms. Additionally, 3640 labels of Black Sigatoka were generated by phytopathology experts, ideal for training algorithms to detect this disease in banana crops. In classification, we achieved a performance of 86.5% using false-colour images with red, red edge, and near-infrared composition through MobileNetV2 for three classes (healthy leaves, diseased leaves, and non-leaf extras). We obtained better results in identifying Black Sigatoka disease in banana crops using the classification approach with MobileNetV2 as well as our own datasets.

Antarctic ice surface properties inferred from Ka and Ku band altimeter waveforms

Altimetry missions designed for ocean monitoring can also evaluate the Antarctic ice sheet’s mass balance by measuring surface height. However, the returned radar echoes (waveforms) have complex shapes influenced by various snow and ice surface properties at multiple scales. Radar altimeters, operating in different modes and frequencies, interact with these surface properties in diverse ways. In this study, we developed a method to assess Antarctic surface properties using data from SARAL/AltiKa and Sentinel-3A & 3B altimeters for June 2021 and January 2023, respectively. We normalized and averaged the observed waveforms in 5 × 5 km2 grid cells. These were then used to create false-color images, with each grid cell’s average power and time gate represented as different ‘bands’. False-color images were analyzed using Principal Component Analysis (PCA) and ISODATA unsupervised classification to identify regions of Antarctica with similar surface properties. We achieved a percent variance of 73% and 84% for the first three Principal Components (PCs), and 96% and 98% for the first ten PCs with the Sentinel-3 and SARAL/AltiKa datasets, respectively. Finally, we analyzed the Ka and Ku band altimeter waveform shapes for each identified class to better understand snow and ice sensitivity to waveforms.

Remote sensing and aeromagnetic mapping for unveiling mineralization potential: Nuqrah Area, Saudi Arabia

Lately, Saudi Arabia has been developing its mineral exploration. However, comprehensive studies of the collected data are not accessible. Thus, the purpose of this research is to identify and map the hydrothermal alteration zones and structural lineaments that regulate the mineral occurrences in the Nuqrah region of the Kingdom of Saudi Arabia using remote sensing and aeromagnetic data. To achieve the desired goal of the study, ASTER remote sensing data were employed, and they were processed in several ways, including principal component analysis, band ratio, and false color composites to reveal the zones of alteration and structure lineaments. In addition, aeromagnetic data was employed to map the lineaments controlling the mineralization. These datasets were integrated using GIS tools to generate a new mineralization potential map of the Nuqrah area, which was classified into three classes: low, moderate, and high probability mineralization. The results showed thirteen intriguing anomalies (high potential mineralization) dispersed over the research area to be prospected. Additionally, techniques such as residual, regional, first vertical derivative, and tilt derivative were utilized to detect the potential mineral-related geologic structures. The results were validated by plotting known mineralization sites on our maps. Six significant faulting trends have been found, according to the lineament map and rose diagrams from remote sensing: NE–SW, WNW–ESE, N–S, ENE–WSW, NNE–SSW, NW–SE, and E–W. The research region is most affected by the NW–SE, ENE–WSW, E–W, and N–S trends, which are organized in decreasing order of magnitude, according to the rose diagram of the aeromagnetic maps. The applied approach can be employed to map potential mineral deposits in Saudi Arabia and similar zones around the globe.

Exploring Factors Affecting the Performance of Neural Network Algorithm for Detecting Clouds, Snow, and Lakes in Sentinel-2 Images

Detecting clouds, snow, and lakes in remote sensing images is vital due to their propensity to obscure underlying surface information and hinder data extraction. In this study, we utilize Sentinel-2 images to implement a two-stage random forest (RF) algorithm for image labeling and delve into the factors influencing neural network performance across six aspects: model architecture, encoder, learning rate adjustment strategy, loss function, input image size, and different band combinations. Our findings indicate the Feature Pyramid Network (FPN) achieved the highest MIoU of 87.14%. The multi-head self-attention mechanism was less effective compared to convolutional methods for feature extraction with small datasets. Incorporating residual connections into convolutional blocks notably enhanced performance. Additionally, employing false-color images (bands 12-3-2) yielded a 4.86% improvement in MIoU compared to true-color images (bands 4-3-2). Notably, variations in model architecture, encoder structure, and input band combination had a substantial impact on performance, with parameter variations resulting in MIoU differences exceeding 5%. These results provide a reference for high-precision segmentation of clouds, snow, and lakes and offer valuable insights for applying deep learning techniques to the high-precision extraction of information from remote sensing images, thereby advancing research in deep neural networks for semantic segmentation.