Composite Image Research Articles

Assessment of Minimally Invasive Surgical Techniques for Sagittal Craniosynostosis: A Multi-Center Time Series Study.

Minimally invasive strip craniectomy is a well-established treatment for sagittal craniosynostosis, however the temporality of change in head shape has not been assessed. In this study, we perform head shape analysis to compare time-series-based clinical outcomes between three different surgical techniques across three academic centers for the treatment of sagittal craniosynostosis. Retrospective, longitudinal 3D images were collected from patients who underwent surgery for the correction of sagittal craniosynostosis for up to 5 years post-operation. The surgical methods studied include spring-assisted craniectomy, narrow-strip craniectomy plus orthotic helmet therapy, and wide-strip craniectomy with biparietal and bitemporal barrel stave wedge osteotomies plus orthotic helmet therapy. Postoperative 3D images were binned into six age groups. Cranial index measurements were calculated on 3D images. 3D whole-head composite images were generated for each procedure to visually represent longitudinal outcomes. The median with interquartile range (IQR) CI measurements at 3-5 years postoperative follow up were 75.9 [73.1-78.6] for spring-assisted, 75.9 [75.4-78.5] for narrow-strip, and 79.4 [76.4-81.9] for wide-strip. The wide-strip CI was significanly different from the spring-assissted and narrow strip groups (p < 0.001). Concerning 3D analysis, spring-assisted patients showed normalization of frontal bossing and skull height compared to age-matched controls, while wide-strip patients showed greater correction of occipital bulleting. Narrow-strip patients had intermediate results between these outcomes. There was no statistically significant regression in longitudinal CI measurements across the three techniques aimed at treating sagittal craniosynostosis. Longitudinal comparison of 3D head shape outcome demonstrated satisfactory correction of scaphocephalic deformity across all three surgical groups.

Identifying fine-scale archaeological features using KH-9 HEXAGON mapping and panoramic camera images: evidence from Liangzhu Ancient City

ABSTRACT Historical fine-scale information of archaeological landscapes is crucial in archaeological investigations. However, documenting such information using satellite sensor data prior to 2000 remains a daunting challenge. Images from the declassified archives of KH-9 HEXAGON (KH-9) cameras, including the panoramic camera system (PCS) and mapping camera system (MCS), offer fine-scale information about archaeological sites. However, noise, contrast distortion and the availability of only a single panchromatic band can limit their potential, particularly for identifying features in subtropical climates within heterogeneous landscape types. This paper focuses on developing a novel multifaceted analytical framework with two components: image pre-processing and feature identification. The image pre-processing component is divided into two steps. First, a trained stationary wavelet transform (SWT) based on the normalized sill (NS) is developed to not only de-noises the image, but also preserve its original image characteristics. Then, the contrast of the de-noised images is optimized by the multi-resolution Top-hat (MTH) using multi-scale information. In the feature identification component, the MCS image is analysed using spatial colour composite write function memory (SCCWFM) and spatial novelty detection (SND). An ultra-fine spatial three-dimensional colour composite (UFSTCC) image and ultra-fine spatial digital surface model (UFSDSM) are produced to aid interpretation of the KH-9 PCS images. The proposed processing pipelines were tested on KH-9 MCS and PCS images of the World Heritage site at Liangzhu Ancient City (LAC) in China, which is characterized by a subtropical climate and a heterogeneous landscape types. The proposed pre-processing pipeline improved considerably the appearance of these images across the LAC landscape while maintaining the original image information. The developed digital analytical approaches for KH-9 PCS and MCS images facilitated straightforward identification of archaeological features in the LAC. The proposed framework has the potential to increase exploitation of the available KH-9 images in archaeological applications.

Refinement of Cropland Data Layer with Effective Confidence Layer Interval and Image Filtering

Various systems have been developed to process agricultural land data for better management of crop production. One such system is Cropland Data Layer (CDL), produced by the National Agricultural Statistics Service of the United States Department of Agriculture (USDA). The CDL has been widely used for training deep learning (DL) segmentation models. However, it contains various errors, such as salt-and-pepper noise, and must be refined before being used in DL training. In this study, we used two approaches to refine the CDL for DL segmentation of major crops from a time series of Sentinel-2 monthly composite images. Firstly, different confidence intervals of the confidence layer were used to refine the CDL. Secondly, several image filters were employed to improve data quality. The refined CDLs were then used as the ground-truth in DL segmentation training and evaluation. The results demonstrate that the CDL with +45% and +55% confidence intervals produced the best results, improving the accuracy of DL segmentation by approximately 1% compared to non-refined data. Additionally, filtering the CDL using the majority and expand–shrink filters yielded the best performance, enhancing the evaluation metrics by about 1.5%. The findings suggest that pre-filtering the CDL and selecting an effective confidence interval can significantly improve DL segmentation performance, contributing to more accurate and reliable agricultural monitoring.

Effect of high plant protein/peptide nutrition supplementation on knee osteoarthritis in older adults with sarcopenia: A randomized, double-blind, placebo-controlled trial

Background & AimsSkeletal muscle is an important contributor to joint health. Previous studies have shown that age-related muscle mass and strength loss are closely associated with the development of knee osteoarthritis. The objective of this study is to investigate whether a high plant protein/peptide nutrition supplementation can alleviate knee osteoarthritis by improving muscle mass and strength. MethodsThis randomized, double-blind, placebo-controlled trial that included participants aged 50–70 years diagnosed with knee osteoarthritis and sarcopenia was conducted in China from February 2022 to September 2022 (ChiCTR2200056415). Participants were randomized to receive a 12-week high plant protein/peptide nutrition supplementation or a placebo twice daily, with one serving each after breakfast and lunch, respectively. The primary outcome analyzed using intention-to-treat analysis was difference in Short Physical Performance Battery (SPPB) from baseline to week 12 between the two groups. The secondary outcomes included changes in muscle mass, strength, symptom and imaging of knee osteoarthritis, body composition, biochemical parameters, and health quality scores. ResultsAfter 12 weeks, a total of 124 participants (38.7% male) completed the trial and were included in the final analysis. Over the 12-week follow-up, the experimental group showed a significant improvement in the SPPB total score (1.03, 95% CI, 0.69 to 1.38, P < 0.0001) compared with the placebo group. Grip strength (2.83 kg, 95% CI, 2.13–3.53, P < 0.0001) and skeletal muscle mass index (0.66 kg/m2, 95% CI, 0.45–0.86, P < 0.0001) were also significantly increased in the experimental group compared with the placebo group. The mean change in Western Ontario and McMaster Universities Osteoarthritis Index total score was −3.95 points (95% CI, −5.02 to −2.89, P < 0.0001) in the experimental group and 0.23 points (95% CI, −0.17 to 0.63, P = 0.253) in the placebo group. Additionally, within the experimental group, nine participants experienced an improvement in osteophyte magnetic resonance imaging results, while no improvement was observed in the placebo group. The experimental group also exhibited significant improvements in health quality compared with the placebo group as assessed by Short Form 36, the World Health Organization Quality of Life Brief Scale, and the Chalder Fatigue Scale. No serious adverse event was reported during the trial. ConclusionOral supplementation with high levels of plant protein/peptides can alleviate symptoms of osteoarthritis in elderly individuals with minor and mild knee osteoarthritis and sarcopenia. The improvement may be attributed to the enhancement of muscle mass, strength, and physical performance.

Classification Method of Composite Insulator Surface Image Based on GAN and CNN

Classification Method of Composite Insulator Surface Image Based on GAN and CNN

Calcination process of porous metal–organic frameworks derived from nickel sulfide composites for supercapacitor and computer vision for investigating the porosity-electrochemical correlation

The utilization of metal–organic framework nanostructured electrode materials in supercapacitors and sensor applications is achieved by various chemical methods. In this study, we create NiS and NiS@MOF-BDC by employing nickel precursors and benzene dicarboxylic acid (BDC) as chelating organic linkers through a thermal reduction procedure at a temperature of 400 °C to produce the composite. The composite heterostructure enhanced the conductivity, porous characteristics, and diverse potential morphological qualities. The production of composite electrodes demonstrates a specific capacity of 260F/g (104C/g) when subjected to a current density of 1A/g. Additionally, these electrodes exhibit exceptional cyclic stability, enduring 5000 cycles, when used with a 2 M KOH electrolyte. Moreover, the synthesized composite HR-TEM images were analyzed using computer vision and AI techniques for estimating the porosity and investigating the enhanced electrochemical correlation.

Geological controls of mineralization occurrences in the Egyptian Eastern Desert using advanced integration of remote sensing and magnetic data

This study presents a comprehensive analysis of mineralization exploration in the Egyptian Eastern Desert (ED), one of the most sought-after areas for those interested in mining industry, by integrating Landsat-9 images and geophysical magnetic data. Employing advanced techniques like Principal Component (PC) analysis, Minimum Noise Fraction (MNf) transform, and Band-Ratio (B-Ratio), the research focuses on mapping lithological units, hydrothermal alteration regions, and structural elements. Composite images derived from specific PC, and MNf bands, and B-Ratio exhibit superior lithological unit identification. The findings emphasize that there are significant variations in the types of rocks extend from the southern to the northern parts of the ED. Hydrothermal alteration mapping, guided by B-Ratio results, aids qualitative lithological discrimination. A novel false color composite image optimizes Landsat-9 B-Ratios, enhancing rock unit discrimination. Correlation analyses reveal associations between mineralization types and major lithological units, while exploration of the magnetic anomaly map highlights its role in correlating mineralization sites. Structural features, analyzed through Center for Exploration-Targeting Grid-Analysis (CET-GA) and Center for Exploration-Targeting Porphyry-Analysis (CET-GA) with Tilt Derivative of RTP (TDR) techniques, contribute to a robust association between regions with medium to high structural density and porphyry intrusions and mineralization. The study significantly supports the advanced exploration geoscience, providing insights into the geological structures and dynamics governing mineralization in the Egyptian ED.

CNN-based prediction of microstructure-derived random property fields of composite materials

The simulation of random spatial variation in the mechanical properties of composite materials using random fields derived from their microstructure can be computationally demanding, since it often requires numerical homogenization of a large number of stochastic volume elements (SVEs). These SVEs are usually extracted from an initial image of the composite microstructure by using a moving window technique and their homogenized properties constitute the discrete data points of the random field at the centroid of each SVE. By replacing the expensive and highly repetitive homogenization procedure with a convolutional neural network (CNN), it is possible to perform nearly instant computations of random property fields. In this work, a CNN is proposed, that takes as input an SVE image and returns its apparent mechanical properties, and can therefore be applied along with the moving window technique. Training is performed on randomly generated SVEs with varying volume fractions and inclusion positions, which are representative of the SVEs obtained during processing of the initial large composite image. It is shown that the proposed CNN can accurately predict the mechanical properties of SVEs and it is subsequently applied for the prediction of random property fields derived from computer simulated and real microstructure images. Results show that, with the proposed methodology, it is possible to make accurate predictions of random fields within a few seconds, a procedure which could potentially require hours of computation time with the finite element based approach.

SAC-GAN: Structure-Aware Image Composition.

We introduce an end-to-end learning framework for image-to-image composition, aiming to plausibly compose an object represented as a cropped patch from an object image into a background scene image. As our approach emphasizes more on semantic and structural coherence of the composed images, rather than their pixel-level RGB accuracies, we tailor the input and output of our network with structure-aware features and design our network losses accordingly, with ground truth established in a self-supervised setting through the object cropping. Specifically, our network takes the semantic layout features from the input scene image, features encoded from the edges and silhouette in the input object patch, as well as a latent code as inputs, and generates a 2D spatial affine transform defining the translation and scaling of the object patch. The learned parameters are further fed into a differentiable spatial transformer network to transform the object patch into the target image, where our model is trained adversarially using an affine transform discriminator and a layout discriminator. We evaluate our network, coined SAC-GAN, for various image composition scenarios in terms of quality, composability, and generalizability of the composite images. Comparisons are made to state-of-the-art alternatives, including Instance Insertion, ST-GAN, CompGAN and PlaceNet, confirming superiority of our method.

A quest for precipitation attractors in weather radar archives

Abstract. Archives of composite weather radar images represent an invaluable resource to study the predictability of precipitation. In this paper, we compare two distinct approaches to construct empirical low-dimensional attractors from radar precipitation fields. In the first approach, the phase space variables of the attractor are defined using the domain-scale statistics of precipitation fields, such as the mean precipitation, fraction of rain, and spatial and temporal correlations. The second type of attractor considers the spatial distribution of precipitation and is built by principal component analysis (PCA). For both attractors, we investigate the density of trajectories in phase space, growth of errors from analogue states, and fractal properties. To represent different scales and climatic and orographic conditions, the analyses are done using multi-year radar archives over the continental United States (≈4000×4000 km2, 21 years) and the Swiss Alpine region (≈500×500 km2, 6 years).

Анализ индикаторов идентификации состояния сельскохозяйственных угодий Волго-Ахтубинской поймы на основе материалов дистанционного зондирования Земли и полевых исследований

Constant monitoring of agriculture within dry-steppe, semi-desert- and intra- zonal terrains is very important. The author analyzes indicators of agricultural fields’ state to automate Southern Russia agro-landscapes mapping processes. Its results and analysis of the spectral indices values on irrigated fields, different in spatial location, operating conditions and use (totally 10 classes are identified), within semi-desert, dry-steppe- and floodplain intra-zonal landscapes of the mentioned area. The obtained results of the spectral indices distribution analysis within agricultural fields indicate that the main classes of condition have certain features in the minimum and maximum values for the growing season, and their fixation can be carried out on the basis of composite images. The most significant indicators of condition identification for farmlands were detected

Fat suppression using frequency-sweep RF saturation and iterative reconstruction.

To introduce an alternative idea for fat suppression that is suited both for low-field applications where conventional fat-suppression approaches become ineffective due to narrow spectral separation and for applications with strong B0 homogeneities. Separation of fat and water is achieved by sweeping the frequency of RF saturation pulses during continuous radial acquisition and calculating frequency-resolved images using regularized iterative reconstruction. Voxel-wise signal-response curves are extracted that reflect tissue's response to RF saturation at different frequencies and allow the classification into fat or water. This information is then utilized to generate water-only composite images. The principle is demonstrated in free-breathing abdominal and neck examinations using stack-of-stars 3D balanced SSFP (bSSFP) and gradient-recalled echo (GRE) sequences at 0.55 and 3T. Moreover, a potential extension toward quantitative fat/water separation is described. Experiments with a proton density fat fraction (PDFF) phantom validated the reliability of fat/water separation using signal-response curves. As demonstrated for abdominal imaging at 0.55T, the approach resulted in more uniform fat suppression without loss of water signal and in improved CSF-to-fat signal ratio. Moreover, the approach provided consistent fat suppression in 3T neck exams where conventional spectrally-selective fat saturation failed due to strong local B0 inhomogeneities. The feasibility of simultaneous fat/water quantification has been demonstrated in a PDFF phantom. The proposed principle achieves reliable fat suppression in low-field applications and adapts to high-field applications with strong B0 inhomogeneity. Moreover, the principle potentially provides a basis for developing an alternative approach for PDFF quantification.

A Type of Scale-Oriented Terrain Pattern Derived from Normalized Topographic Relief Layers and Its Interpretation

Topographic scale characteristics contain valuable information for interpreting landform structures, which is crucial for understanding the spatial differentiation of landforms across large areas. However, the absence of parameters that specifically describe the topographic scale characteristics hinders the quantitative representation of regional topography from the perspective of spatial scales. In this study, false-color composite images were generated using normalized topographic relief data, showing a type of scale-oriented terrain pattern. Subsequent analysis indicated a direct correlation between the luminance of the patterns and the normalized topographic relief. Additionally, a linear correlation exists between the color of the patterns and the change rate in normalized topographic relief. Based on the analysis results, the issue of characterizing topographic scale effects was transformed into a problem of interpreting terrain patterns. The introduction of two parameters, flux and curl of topographic field, allowed for the interpretation of the terrain patterns. The assessment indicated that the calculated values of topographic field flux are equivalent to the luminance of the terrain patterns and the variations in the topographic field curl correspond with the spatial differentiation of colors in the terrain patterns. This study introduced a new approach to analyzing topographic scale characteristics, providing a pathway for quantitatively describing scale effects and automatically classifying landforms at a regional scale. Through exploratory analysis on artificially constructed simple DEMs and verification in four typical geomorphological regions of real terrain, it was shown that the terrain pattern method has better intuitiveness than the scale signature approach. It can reflect the scale characteristics of terrain in continuous space. Compared to the MTPCC image, the terrain parameters derived from the terrain pattern method further quantitatively describe the scale effects of the terrain.

PUSHKIN IN A. MAKHOV’S NEW EMBLEMATICA

The article is based on the material of the recently published A. Makhov’s Emblematica: Microcosm. Pushkin is included in the “personal pantheon” of A. Makhov, being a link between the main research worlds of the scholar: emblem studies, the history of Russian and European poetics, the history of European Romanticism, etc. By Pushkin’s “presence”, we mean not only mentions of Pushkin’s name and direct quotations from his writings, but also more or less easily readable convergences at the topic level. Makhov’s new book is devoted to man as a subject of European emblems of the 16th and 17th centuries; an important place in it is occupied by the study of the human body as the language of emblems and the source of signs, the application of the main four combinatorial operations in the creation of emblematic composite images. When interpreting European emblems, associations with Pushkin’s work constantly arise — for example, the figure of the conciliatio of the stingy rich and poor, expressed in the topos “rich is poor”, allows a Russian reader to see a parallel with Pushkin’s “The Miserly Knight”; the topos of “vain flight from love, the incurable love disease” — with his lyrics. The method of reading “through the prism of Pushkin” used in the article allows us to look at the rich material of the monograph at least in two aspects: to shed new light on Pushkin’s connection with European emblems and to incorporate his work even more deeply into the European cultural tradition; to see how the Pushkin principle can serve as a kind of core, an immanent dominant in interpretations of European cultural realities by such a multi-world scholar as A. Makhov.

Mapping the recovery of Mountain Ash (Eucalyptus regnans) and Alpine Ash (E. delegatensis) using satellite remote sensing and a machine learning classifier

This research presents a random forest classification approach to map the response of the obligate-seeder Eucalyptus species, Mountain Ash (Eucalyptus regnans) and Alpine Ash (E. delegatensis), to disturbance from timber harvesting in the Victorian Central Highlands in south-eastern Australia. A Sentinel-2 MultiSpectral Instrument (MSI) composite image was classified and analysed using a random forest algorithm trained using field data collected within fifty-three sites. Training and validation datasets were produced by randomly sub setting using a 70:30 split. Validation was performed by producing a confusion matrix using the points which were excluded from model training. The random forest model demonstrated strong performance at distinguishing Eucalyptus regrowth from the dominant understory species, Silver Wattle (Acacia dealbata), achieving an F1-score of 97.3% and true skill statistic of 96.4%.This study showcases the operational insights that satellite remote sensing data and machine learning can provide for regional-scale monitoring and management of E. regnans and E. delegatensis dominant ecosystems following disturbance. Due to the high conservation value of these communities, and their sensitivity to frequent high intensity disturbance and low precipitation during regeneration, this research seeks to provide a means to assess the condition of regenerating forest and in doing so enhance our understanding of these ecologically significant ecosystems in response to changing environmental conditions.

U-Net Convolutional Neural Network for Mapping Natural Vegetation and Forest Types from Landsat Imagery in Southeastern Australia.

Accurate and comparable annual mapping is critical to understanding changing vegetation distribution and informing land use planning and management. A U-Net convolutional neural network (CNN) model was used to map natural vegetation and forest types based on annual Landsat geomedian reflectance composite images for a 500 km × 500 km study area in southeastern Australia. The CNN was developed using 2018 imagery. Label data were a ten-class natural vegetation and forest classification (i.e., Acacia, Callitris, Casuarina, Eucalyptus, Grassland, Mangrove, Melaleuca, Plantation, Rainforest and Non-Forest) derived by combining current best-available regional-scale maps of Australian forest types, natural vegetation and land use. The best CNN generated using six Landsat geomedian bands as input produced better results than a pixel-based random forest algorithm, with higher overall accuracy (OA) and weighted mean F1 score for all vegetation classes (93 vs. 87% in both cases) and a higher Kappa score (86 vs. 74%). The trained CNN was used to generate annual vegetation maps for 2000-2019 and evaluated for an independent test area of 100 km × 100 km using statistics describing accuracy regarding the label data and temporal stability. Seventy-six percent of pixels did not change over the 20 years (2000-2019), and year-on-year results were highly correlated (94-97% OA). The accuracy of the CNN model was further verified for the study area using 3456 independent vegetation survey plots where the species of interest had ≥ 50% crown cover. The CNN showed an 81% OA compared with the plot data. The model accuracy was also higher than the label data (76%), which suggests that imperfect training data may not be a major obstacle to CNN-based mapping. Applying the CNN to other regions would help to test the spatial transferability of these techniques and whether they can support the automated production of accurate and comparable annual maps of natural vegetation and forest types required for national reporting.

Using Google Earth Engine to Assess the Current State of Thermokarst Terrain on Arga Island (the Lena Delta)

The mapping of thermokarst landscapes and the assessment of their conditions are becoming increasingly important in light of a rising global temperature. Land cover maps provide a basis for quantifying changes in landscapes and identifying areas that are vulnerable to permafrost degradation. The study is devoted to assessing the current state of thermokarst terrain on Arga Island. We applied a random forests algorithm using the capabilities of the Google Earth Engine cloud platform for the supervised classification of the composite image. The analyzed composite consists of a Sentinel-2 image and a set of calculated indices. The study found that thermokarst-affected terrains occupy 35% of the total area, and stable terrains cover 29% at the time of image acquisition. The classifier has also mapped water bodies, slopes, and blowouts. The accuracy assessment revealed that the overall accuracy for all the different land cover classes was 98.34%. A set of other accuracy metrics also demonstrated a high level of performance. This study presents significant findings for assessing landscape changes in a region with unique environmental features. It also provides a potential basis for future interdisciplinary research and for predicting future thermokarst landscape changes in the Lena Delta area.

A Multi-Shot Approach for Spatial Resolution Improvement of Multispectral Images from an MSFA Sensor.

Multispectral imaging technology has advanced significantly in recent years, allowing single-sensor cameras with multispectral filter arrays to be used in new scene acquisition applications. Our camera, developed as part of the European CAVIAR project, uses an eight-band MSFA to produce mosaic images that can be decomposed into eight sparse images. These sparse images contain only pixels with similar spectral properties and null pixels. A demosaicing process is then applied to obtain fully defined images. However, this process faces several challenges in rendering fine details, abrupt transitions, and textured regions due to the large number of null pixels in the sparse images. Therefore, we propose a sparse image composition method to overcome these challenges by reducing the number of null pixels in the sparse images. To achieve this, we increase the number of snapshots by simultaneously introducing a spatial displacement of the sensor by one to three pixels on the horizontal and/or vertical axes. The set of snapshots acquired provides a multitude of mosaics representing the same scene with a redistribution of pixels. The sparse images from the different mosaics are added together to get new composite sparse images in which the number of null pixels is reduced. A bilinear demosaicing approach is applied to the composite sparse images to obtain fully defined images. Experimental results on images projected onto the response of our MSFA filter show that our composition method significantly improves image spatial resolution and minimizes reconstruction errors while preserving spectral fidelity.

Integrative mapping of radioactive and alteration zones in Um Had plutons, Egypt: Using Landsat 9, ASTER imagery, and airborne geophysical data

The study aims to assess the effectiveness of using ASTER, Landsat 9, and airborne geophysical data to generate maps depicting hydrothermally altered regions and their correlation with radioactive-mineralized zones in Egypt's Um Had area. Various image processing methods were applied, including color composite images, band ratios, selective principal component analyses, and lineament extraction with enhancement procedures. The ASTER ratios identified the three types of hydrothermal alterations; phyllic, argillic, and propylitic, with other significant alteration zones related to mineralization. Airborne gamma-ray spectrometry contour maps displayed marked varied levels of total count (T.C), eU, eTh, and K, ranging from 1.2 to 21.2 Ur, 0.5–12.34 ppm, 1.45–28 ppm, and 0.13–3.65 %, respectively. The highest anomalies of these radioelements concentration have coincided with the alkali feldspar granite and along their intrusive contact zone with the Hammamat Group sedimentary rocks. Higher anomalies are well recorded in the center and eastern regions of Um Had's, according to the radioelements composite image. The lowest concentrations of these radioelements are associated with gneiss, ophiolitic mélange, metavolcanic, Hammamat Group sedimentary rocks, Taref Formation, and Wadi sediments. To mitigate local magnetic effects, the total aeromagnetic data was reduced to the north magnetic pole (RTP). Power spectrum analysis of the RTP data identified distinct magnetic wavelengths for regional-residual components. In order to identify near-surface magnetic lineaments, such as contacts, shear zones, faults, and dykes, advanced algorithms were applied to the RTP data. The lineaments derived from ASTER and airborne magnetic data revealed dominant fault systems characterized by E–W, NE, NNW, NW and N–S trends, governing the structural framework of the study area. Depth levels of geological contacts and faults that represented pathways for altered and mineralized zones reached more than 1200 m (Euler deconvolution). These findings highlight the consistent results obtained when combining ASTER data with airborne survey data, allowing for the identification of hydrothermally altered zones and primary conductive zones.

Automatic image alignment and stitching for ultrasound-based robotic inspection of complex geometry components

Robotic inspection of complex geometry components using immersion ultrasonic techniques is relevant for many ultrasonic Non-Destructive Testing (NDT) applications in different industries. To image large component, ultrasonic probe is manipulated around the component immersed in water using a robotic guided system to create a tiled image of the entire component. Due to the mechanical vibrations of the high-speed robotic arm, the probe position coordinates provided by the robotic system are not precise enough to ensure an accurate reconstruction (stitching) of a composite image from all individual images. In this work, we propose a stitching method specifically designed to create a single 2D image of the surface of an inspected component from a stack of Total Focusing Method (TFM) images. This stitching method uses Iterative Closest Point (ICP) registration to estimate a 2D rigid transformation between two consecutive 2D images, by maximizing the overlap between the surface geometries extracted from each image. The capabilities of this imaging technique are illustrated by various simulated and experimental results carried out in a water tank. Significant improvements in surface image quality, leading to accurate surface reconstruction, are shown for vertical vibrations with displacements of more than two operating wavelengths and for rotational vibrations with deviation angles of less than 1°. The results also show that the resolving power of the ICP algorithm decreases for strong rotational vibrations.