Geometric Inconsistencies Research Articles

Author Correction: Harmonic errors of a 9.4 T all-REBCO NMR magnet affected by screening current and geometric inconsistency of coated conductors

Author Correction: Harmonic errors of a 9.4 T all-REBCO NMR magnet affected by screening current and geometric inconsistency of coated conductors

A unified feature-motion consistency framework for robust image matching

Establishing reliable feature matches between a pair of images in various scenarios is a long-standing open problem in photogrammetry. Attention-based detector-free matching with coarse-to-fine architecture has been a typical pipeline to build matches, but the cross-attention module with global receptive field may compromise the structural local consistency by introducing irrelevant regions (outliers). Motion field can maintain structural local consistency under the assumption that matches for adjacent features should be spatially proximate. However, motion field can only estimate local displacements between consecutive images and struggle with long-range displacements estimation in large-scale variation scenarios without spatial correlation priors. Moreover, large-scale variations may also disrupt the geometric consistency with the application of mutual nearest neighbor criterion in patch-level matching, making it difficult to recover accurate matches. In this paper, we propose a unified feature-motion consistency framework for robust image matching (MOMA), to maintain structural consistency at both global and local granularity in scale-discrepancy scenarios. MOMA devises a motion consistency-guided dependency range strategy (MDR) in cross attention, aggregating highly relevant regions within the motion consensus-restricted neighborhood to favor true matchable regions. Meanwhile, a unified framework with hierarchical attention structure is established to couple local motion field with global feature correspondence. The motion field provides local consistency constraints in feature aggregation, while feature correspondence provides spatial context prior to improve motion field estimation. To alleviate geometric inconsistency caused by hard nearest neighbor criterion, we propose an adaptive neighbor search (soft) strategy to address scale discrepancy. Extensive experiments on three datasets demonstrate that our method outperforms solid baselines, with AUC improvements of 4.73/4.02/3.34 in two-view pose estimation task at thresholds of 5°/10°/20° on Megadepth test, and 5.94% increase of accuracy at threshold of 1px in homography task on HPatches datasets. Furthermore, in the downstream tasks such as 3D mapping, the 3D models reconstructed using our method on the self-collected SYSU UAV datasets exhibit significant improvement in structural completeness and detail richness, manifesting its high applicability in wide downstream tasks. The code is publicly available at https://github.com/BunnyanChou/MOMA.

Harmonic errors of a 9.4 T all-REBCO NMR magnet affected by screening current and geometric inconsistency of coated conductors

REBa2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document}Cu3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_3$$\\end{document}O7-x\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{7-\ ext {x}}$$\\end{document} (REBCO, RE = rare earth)-coated conductor is a competitive option in terms of current-carrying capacity and high-stress durability in developing high-field magnets for nuclear magnetic resonance (NMR) research. Meanwhile, a technical challenge in utilizing a stand-alone REBCO NMR magnet is an unexpected difference in the field uniformity between the designed and measured values after being constructed and charged, i.e., harmonic errors. Bortot et al., and Li et al., reported analytic evidence of the related issue. However, sufficient research has not yet been conducted, so evidence should be supplemented further. Here we report harmonic errors due to screening current and inconsistent conductor thickness, confirmed by a 400 MHz 1H NMR magnet development project. The magnet was first charged up to its operating current, and then multiple overcharge-discharge cycles were applied, which was an empirically optimized operation protocol. A field mapping device obtained magnetic fields at designated locations in the room-temperature bore. The result showed over 100 ppm field uniformity difference between designed and measured values. A simulation model was developed considering screening current and inconsistent conductor thickness for reproducing the field distribution. Comparison of voltages and fields between simulation and measurement validated the model. Further analysis of the overcharge-discharge effect on harmonic errors demonstrated that even and odd-order harmonics are mainly attributed to screening current and geometric inconsistency while confirming the limitation of the screening current mitigation effect. Hence, we concluded that the desirable requirement of the sub-ppm level field uniformity generation might be barely possible with the current REBCO NMR magnet design approach.

Natural fibre pultruded profiles: Illustration of optimisation processes to develop high-performance biocomposites for architectural and structural applications

The selection of materials in the construction industry plays a pivotal role in advancing sustainability goals. Traditional materials derived from natural resources face inherent constraints linked to geographic limitation, growth time, and geometric inconsistency and therefore recent attention has shifted towards developing novel bio-based materials. Composites, offering varying properties and geometries, are becoming increasingly popular for customising materials for specific applications. Pultrusion, a technology for manufacturing linear fibre-reinforced composites, is a well-established and reliable method. This study delves into optimising pultrusion technology, which traditionally relies on synthetic fibres, by exploring the potential of natural alternatives, specifically hemp bast fibres. Additionally, it presents a customised formulation based on a plant-based resin and additives. This formulation is tailored for pultrusion to produce high-performance biocomposites for use as load-bearing components in structural applications, with an initial focus on bending structures. The study elaborates on the material composition and performance of these newly developed natural fibre pultruded profiles, showcasing their mechanical capabilities through rigorous experimentation and testing. The results demonstrate the material's mechanical capabilities showcasing a flexural strength of 260 MPa with a bending modulus of 21 GPa and a bending radius reaching 0.5 m. While this study focuses on the material formulation tested on laboratory-scale pultrusion, the findings will be later applied in an upscaled production at an industrial level, aiming to enhance overall sustainability in the construction industry.

Reevaluating Cosmic Origins: A Critical Analysis of Relic Radiation and Cosmometric Assumptions in Big Bang Cosmology

This article presents a critical examination of two key aspects of Big Bang cosmology: the nature and observability of cosmic microwave background (CMB) radiation and geometric inconsistencies or “cosmometric contradictions” within established approaches to cosmology. By dissecting the conventional interpretation of the CMB as relic radiation from a primordial fireball, we uncover that the standard model’s reliance on a spatially homogeneous fluid to represent radiation and matter underestimates the propagation of radiation and leads to the conclusion that the observability of the CMB contradicts its purported origins. Treating free radiation as if it expanded with the universe while disregarding that it escapes from its source at the speed of light is termed here as the “relic radiation blunder”. Furthermore, we delve into the cosmometric contradiction, highlighting how the calculated distances in standard cosmology imply a universe that exceeds the spatial limitations of its own foundational premise. This makes it evident that standard cosmology is not a unitary theory but involves models with conflicting spatial extensions. The analysis suggests a need for a revised understanding of redshift phenomena and cosmic expansion. It advocates for a reassessment of the underpinnings of cosmology, urging the scientific community to consider alternative frameworks that reconcile these critical inconsistencies. This work challenges the inviolable core of established cosmological theory, calling for a paradigm shift to foster foundational advances in our understanding of the universe.

Volume Conservation Constrained Multi-Material Reconstruction for Inconsistent Spectral CT Imaging

Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, the difficulty of decomposition also increases due to the nonlinearity nature of the measurements and ill condition of the problem, especially in the case of geometric inconsistency, which typically leads to low image qualities. Therefore, it is a crucial issue for inconsistent spectral CT imaging to improve the accuracy of material decomposition while suppressing the noise. This paper depends on a statistical reconstruction model with different priors to propose one-step multi-material algorithms. In these approaches, the gradient sparisty-based and convolutional neural network based methods are designed for the case of the consistent numbers of material and energies. And volume conservation constraint are further developed while the two numbers are not equal. An efficient Newton descent method is adopted based on the simple surrogate function. For simulation experiments with different noise levels, the largest peak signal-to-noise ratio (PSNR) obtained by the proposed method approximately increases by 20.924 dB and 18.283 dB compared with those of other algorithms. Magnified areas of real data also further demonstrated that the proposed methods has a better ability to suppress noise. Numerical experiments verify that the proposed method efficiently reconstructed the material maps, and reduced noise compared with the state-of-the-art methods.

DyLFG: A Dynamic Network Learning Framework Based on Geometry.

Dynamic network representation learning has recently attracted increasing attention because real-world networks evolve over time, that is nodes and edges join or leave the networks over time. Different from static networks, the representation learning of dynamic networks should not only consider how to capture the structural information of network snapshots, but also consider how to capture the temporal dynamic information of network structure evolution from the network snapshot sequence. From the existing work on dynamic network representation, there are two main problems: (1) A significant number of methods target dynamic networks, which only allow nodes to increase over time, not decrease, which reduces the applicability of such methods to real-world networks. (2) At present, most network-embedding methods, especially dynamic network representation learning approaches, use Euclidean embedding space. However, the network itself is geometrically non-Euclidean, which leads to geometric inconsistencies between the embedded space and the underlying space of the network, which can affect the performance of the model. In order to solve the above two problems, we propose a geometry-based dynamic network learning framework, namely DyLFG. Our proposed framework targets dynamic networks, which allow nodes and edges to join or exit the network over time. In order to extract the structural information of network snapshots, we designed a new hyperbolic geometry processing layer, which is different from the previous literature. In order to deal with the temporal dynamics of the network snapshot sequence, we propose a gated recurrent unit (GRU) module based on Ricci curvature, that is the RGRU. In the proposed framework, we used a temporal attention layer and the RGRU to evolve the neural network weight matrix to capture temporal dynamics in the network snapshot sequence. The experimental results showed that our model outperformed the baseline approaches on the baseline datasets.

On the coincidence of optimal completions for small pairwise comparison matrices with missing entries

AbstractIncomplete pairwise comparison matrices contain some missing judgements. A natural approach to estimate these values is provided by minimising a reasonable measure of inconsistency after unknown entries are replaced by variables. Two widely used inconsistency indices for this purpose are Saaty’s inconsistency index and the geometric inconsistency index, which are closely related to the eigenvector and the logarithmic least squares priority deriving methods, respectively. The two measures are proven to imply the same optimal filling for incomplete pairwise comparison matrices up to order four but not necessarily for order at least five.

Fusion of aerial, MMS and backpack images and point clouds for optimized 3D mapping in urban areas

Photorealistic 3D models are important data sources for digital twin cities and smart city applications. These models are usually generated from data collected by aerial or ground-based platforms (e.g., mobile mapping systems (MMSs) and backpack systems) separately. Aerial and ground-based platforms capture data from overhead and ground surfaces, respectively, offering complementary information for better 3D mapping in urban areas. Particularly, backpack mapping systems have gained popularity for 3D mapping in urban areas in recent years, as they offer more flexibility to reach regions (e.g., narrow alleys and pedestrian routes) inaccessible by vehicle-based MMSs. However, integration of aerial and ground data for 3D mapping suffers from difficulties such as tie-point matching among images from different platforms with large differences in perspective, coverage, and scale. Optimal fusion of the results from different platforms is also challenging. Therefore, this paper presents a novel method for the fusion of aerial, MMS, and backpack images and point clouds for optimized 3D mapping in urban areas. A geometric-aware model for feature matching is developed based on the SuperGlue algorithm to obtain sufficient tie-points between aerial and ground images, which facilitates the integrated bundle adjustment of images to reduce their geometric inconsistencies and the subsequent dense image matching to generate 3D point clouds from different image sources. After that, a graph-based method considering both geometric and texture traits is developed for the optimal fusion of point clouds from different sources to generate 3D mesh models of better quality. Experiments conducted on a challenging dataset in Hong Kong demonstrated that the geometric-aware model could obtain sufficient accurately matched tie-points among the aerial, MMS, and backpack images, which enabled the integrated bundle adjustment of the three image datasets to generate properly aligned point clouds. Compared with the results obtained from state-of-the-art commercial software, the 3D mesh models generated from the proposed point cloud fusion method exhibited better quality in terms of completeness, consistency, and level of detail.

Development of MR fluid assisted novel vibration damping fixture for milling machine

Chatter occurs in all metal cutting process and it is an important factor that causes uneven tool wear and reduced tool life resulting in poor surface finish of work piece. It even causes geometrical inconsistencies and poor surface finish. It can lead to higher costs, delayed deliveries and even loss of orders due to poor quality of the machined component. To reduce and control the tool vibration in metal cutting it is necessitating that to develop a clamping method to improve the vibration damping capability and improves the machining conditions. To cater this need design and development of Magnetorheological (MR) fluid assisted novel vibration damping fixture designed. The application of MR damper assisted fixture in milling machine is considered to be a solution for better vibration control and surface finish. From the experimental investigation it was evident that MR damper performance depends on parameters like suspended fluid, density, reaction time, particle size, maximum yield stress. The viscosity of the MR fluid depends on the intensity of magnetic field applied. Coil orientation, number of turns, current, gap between cylinder and piston are important parameters to be consider while designing a MR Damper. From the experimental results it was witnessed that the magnetorheological assisted fixture reduces the vibration during machining significantly and improves the surface roughness and machining performance.

Carbonaceous aerosols remote sensing from geostationary satellite observation, Part I: Algorithm development using critical reflectance

Current aerosol remote sensing without using multi-view satellite sensors still fail to retrieve its components directly, such as black carbon (BC) and organic carbon (OC) simultaneously due to the limitations on available observations. In this study, A new general carbonaceous aerosol retrieval strategy for geostationary single-view satellite observations is implemented based on a method of critical reflectance, to retrieve the parameters of BC and extra OC concentration from measured radiance without prior quantification of Aerosol Optical Thickness (AOT). The method is based on measuring the change in upward reflectance between clear and hazy days over a varying surface reflectance, with the aim of finding a balance between brightening due to scattering and darkening due to absorption. Since aerosols in the proposed algorithm are considered as a mixture of BC, OC and clustered shell aerosols, this balance can be used to retrieve concentration parameters of these components via radiative transfer models. Sensitivity tests indicate that different scenarios of BC and OC volume fractions can introduce certain sensitivity pattern on critical reflectance. The retrieval is achieved by minimizing the differences between measured critical reflectance and a set of look-up tables (LUTs). Random sampling consensus (RANSAC) is also used in the strategy to reduce the influence of clouds and anomalous pixels on the retrievals. Since critical reflectance should be calculated by comparing geometrically consistent clear and hazy images, the algorithm is more suitable for geostationary satellites with constant viewing geometries. An initial validation and application applied to Himawari images over the North China Plain (NCP) shows that the carbonaceous aerosol retrievals are highly consistent with the expectations in terms of spatial and temporal patterns. The retrievals of BC and OC concentration follow the daily fluctuations of the Aethalometers observations and feature small differences in mean values throughout the month. Additionally, the AOTs at 0.55 μm can be also satisfactorily reproduced based on the carbonaceous aerosol retrievals, resulting in a mean absolute error of 0.089 and a correlation coefficient of 0.870. The main errors in the method arise from shell model assumptions, RANSAC fitting bias, inconsistent clear reference AOT in LUT, and geometric inconsistency between the clear reference and hazy images. This work indicates that the BC and OC concentration in high resolution can be acquired through the geostationary single-view remote sensing for the further air quality and climate studies.

A method for automatically identifying vacant area in the current urban environment based on open source data

Urban development and effective land use planning are critical factors in creating sustainable and functional cities. Identifying vacant areas with development potential plays a crucial role in optimizing urban resources and contributes to making informed decisions for new projects and urban transformations. This paper presents a method for identifying vacant territories in the current urban environment to accommodate different infrastructures. The method uses modern technology and open-source data to identify vacant area that can become the object of urban planning and new construction projects. Taking into account such factors as accessibility, zoning rules and urban planning restrictions, the method offers a systematic approach to the assessment of urban development potential. The article describes the main steps of the method, including site selection, extracting sites and functional zones from public data sources, checking for compliance with urban planning regulations and restrictions, and cleaning up the data to eliminate geometric inconsistencies and shallow areas. Through detailed explanations, examples, and recommendations, this paper aims to provide a comprehensive understanding of the method and its application in urban planning practice. The results of this study have important implications for sustainable urban development by enabling informed decision-making that meets the needs and aspirations of communities, contributing to economic growth and improving the overall quality of life. Ultimately, the method developed to identify vacant spaces offers a valuable tool for urban planners and decision-makers seeking to optimize land use and create dynamic and sustainable cities.

Geometry-Contrastive Transformer for Generalized 3D Pose Transfer

We present a customized 3D mesh Transformer model for the pose transfer task. As the 3D pose transfer essentially is a deformation procedure dependent on the given meshes, the intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. Specifically, we propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies across the given meshes. Moreover, locally, a simple yet efficient central geodesic contrastive loss is further proposed to improve the regional geometric-inconsistency learning. At last, we present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task towards unknown spaces. The massive experimental results prove the efficacy of our approach by showing state-of-the-art quantitative performances on SMPL-NPT, FAUST and our new proposed dataset SMG-3D datasets, as well as promising qualitative results on MG-cloth and SMAL datasets. It's demonstrated that our method can achieve robust 3D pose transfer and be generalized to challenging meshes from unknown spaces on cross-dataset tasks. The code and dataset are made available. Code is available: https://github.com/mikecheninoulu/CGT.

High-Precision Registration of Lunar Global Mapping Products Based on Spherical Triangular Mesh

Lunar global mapping products provide a solid data foundation for lunar scientific research and exploration. As the widespread geometric inconsistencies among multi-source mapping products seriously affect the synergistic use of the products, high-precision registration of multiple lunar global products is critical, and it is highly challenging due to large coverage and complex local geometric inconsistencies. In this research, we propose a spherical triangular-mesh-based method for high-precision registration of lunar global mapping products, which involves four steps: data preprocessing, feature point extraction and matching, spherical Delaunay triangulation, and geometric correction with spherical barycentric coordinates. This global registration method avoids map projection distortions by using spherical coordinates directly, and achieves high precision by confining the geometric models to spherical triangular facets. Experiments are conducted using two groups of different types of mapping products to verify the proposed method quantitatively and qualitatively. The results show that the geometric inconsistencies are reduced from hundreds of pixels to sub-pixel level globally after the registration, demonstrating the effectiveness of the proposed method.

Improving predictive CTV segmentation on CT and CBCT for cervical cancer by diffeomorphic registration of a prior.

Automatic cervix-uterus segmentation of the clinical target volume (CTV) on CT and cone-beam CT (CBCT) scans is challenged by the limited visibility and the non-anatomical definition of certain border regions. We study the potential performance gain of convolutional neural networks by regulating the segmentation predictions as diffeomorphic deformations of a segmentation prior. We introduce a 3D convolutional neural network that segments the target scan by joint voxel-wise classification and the registration of a given prior. We compare this network to two other 3D baseline models: One treating segmentation as a classification problem (segmentation-only), the other as a registration problem (deformation-only). For reference and to highlight the benefits of a 3D model, these models are also benchmarked against a 2D segmentation model. Network performances are reported for CT and CBCT segmentation of the cervix-uterus CTV. We train the networks on the data of 84 patients. The prior is provided by the CTV segmentation of a planning CT. Repeat CT or CBCT scans constitute the target scans to be segmented. All 3D models outperformed the 2D segmentation model. For CT segmentation, combining classification and registration in the proposed joint model proved beneficial, achieving a Dice score of 0.87 and a mean squared error (MSE) of the surface distance below 1.7mm. No such synergy was observed for CBCT segmentation, for which the joint and the deformation-only model performed similarly, achieving a Dice score of about 0.80 and an MSE surface distance of 2.5mm. However, the segmentation-only model performed notably worse in this low contrast regime. Visual inspection revealed that this performance drop translated into geometric inconsistencies between the prior and target segmentation. Such inconsistencies were not observed for the deformation-based models. Constraining the solution space of admissible segmentation predictions to those reachable by a diffeomorphic deformation of the prior proved beneficial as it improved geometric consistency. Especially for CBCT, with its poor soft-tissue contrast, this type of regularization becomes important as shown by quantitative and qualitative evaluation.

Look Here: Learning Geometrically Consistent Refinement of Inverse-Depth Images for 3D Reconstruction

Building good 3D maps is a challenging and expensive task, which requires high-quality sensors and careful, time-consuming scanning. We seek to reduce the cost of building good reconstructions by correcting views of existing low-quality ones in a post-hoc fashion using learnt priors over surfaces and appearance. We train a convolutional neural network model to predict the difference in inverse-depth from varying viewpoints of two meshes — one of low-quality that we wish to correct, and one of high-quality that we use as a reference. Our full model runs at 11.3[Formula: see text]Hz when aggregating four input views. In contrast to previous work, we pay attention to the problem of excessive smoothing in corrected meshes. We address this with a suitable network architecture, and introduce a loss-weighting mechanism that emphasizes edges in the prediction. Furthermore, smooth predictions result in geometrical inconsistencies. To deal with this issue, we present a loss function which penalizes re-projection differences that are not due to occlusions. Future applications of this work will incorporate semantic scene understanding in a multi-task learning setting. We explore the efficacy of the proposed system in terms of gross error correction and generalization capability by showing its performance in practice on a subset of the Kitti Odometry dataset, complete with a component-wise ablation study. We evaluate correctness and completeness measures of surface reconstruction across viewpoints and show that the proposed system is introspective in regions lacking sufficient high-quality supervision — indeed, models trained with geometric consistency loss create a lot more surface in areas that were not supervised, in one case filling in 67.97% or 8010[Formula: see text]m2 of an unlabeled input region. Finally, we assess the practical applicability of our method at large-scale by experiments over the full scope of the Kitti Odometry dataset. Broadly, as a measure of effectiveness, our model reduces gross errors by 45.3–77.5%, up to five times more than previous work. We also assess the practical applicability of our method to 3D reconstruction at large scales and find that compared to the baseline our model shows better stability in correctness when improving completeness of surfaces, and is effective in reducing median total error by up to 21.8[Formula: see text]cm.

In-silico study of accuracy and precision of left-ventricular strain quantification from 3D tagged MRI.

Cardiac Magnetic Resonance Imaging (MRI) allows quantifying myocardial tissue deformation and strain based on the tagging principle. In this work, we investigate accuracy and precision of strain quantification from synthetic 3D tagged MRI using equilibrated warping. To this end, synthetic biomechanical left-ventricular tagged MRI data with varying tag distance, spatial resolution and signal-to-noise ratio (SNR) were generated and processed to quantify errors in radial, circumferential and longitudinal strains relative to ground truth. Results reveal that radial strain is more sensitive to image resolution and noise than the other strain components. The study also shows robustness of quantifying circumferential and longitudinal strain in the presence of geometrical inconsistencies of 3D tagged data. In conclusion, our study points to the need for higher-resolution 3D tagged MRI than currently available in practice in order to achieve sufficient accuracy of radial strain quantification.

Modeling of the stress-deformed state of a capacitive apparatus with geometric deviations when mounting on a foundation

The reliable and trouble-free operation of the capacitive equipment of hazardous production facilities is significantly influenced by the quality of the installation performed before commissioning. So, during the installation of capacitive equipment, various geometric inconsistencies and deviations may occur, which are established by the current regulatory and technical documents for the installation of technological equipment. If unacceptable deviations and inconsistencies established by regulatory and technical documents are found, they must be fully eliminated. However, in certain cases, the regulatory and technical documentation allows operation with various insignificant geometric deviations and inconsistencies that do not go beyond the established limits. This may relate to deviations from the design horizontal position of the apparatus when installed on a foundation. It should be noted that during operation, under the influence of increased pressure, temperature and other loads, an apparatus with deviations of this type can undergo a completely different stress-deformed state compared to an apparatus without geometric deviations, which is not taken into account in expert diagnostics, strength analysis and assessment of residual resource. The solution to this issue can be approached using software systems that allow building 3D models of the objects under consideration with the introduction of all the necessary parameters into these models and getting a real picture of the distribution of zones of increased stress. In this work, the stress-deformed state of the process vessel is simulated due to the impact of working loads and different directions of deviation from the horizontal design position of the body when the apparatus is mounted on the foundation.

Sub-pixel registration of multi-resolution imagery by correlation matching of the bathymetry-related features.

Multispectral imaging plays a significant role in coastal mapping and monitoring applications. For tasks involving the integration of multiple overlapped images, precise co-registration of the multisource satellite images is a crucial preliminary step. However, due to the limited terrestrial area and insufficient landscape features, the traditional methods become less efficient or even invalid in offshore island environments. This study addresses the problem by exploring the feasibility of using bathymetry information for geometric registration of satellite imagery. Instead of using the ground control points (GCPs) or extracting the tie points from the landscape features, the band ratio values are extracted from the multispectral images and are subsequently matched between different images through a correlation-based similarity measure. By searching the optimum correlation within the positioning uncertainty radius, the translation between two satellite images is estimated. Thus, the geometric inconsistency between the multispectral images of different sources and resolutions is effectively reduced. This result is obtained by using the ample bathymetry features without the aid of the GCPs and the in-situ bathymetry data. The experimental results using GeoEye-1, Sentinel-2, and Landsat-8 images at Ganquan Island show that for an island setting with a limited terrestrial area, the developed method achieves sub-pixel registration accuracy (less than 2 m) in planimetry. The effect of the nonlinearity and outliers are accounted for using the Spearman correlation measure. The improvement in image alignment enables the integration of multispectral images of different sources and resolutions for producing an accurate and consistent interpretation for coastal comparative and synergistic applications.

An oblique projection modification technique (OPMT) for fast multispectral CT reconstruction

In x-ray multispectral (or photon-counting) computed tomography (MCT), the object of interest is scanned under multiple x-ray spectra, and it can acquire more information about the scanned object than conventional CT, in which only one x-ray spectrum is used. The obtained polychromatic projections are utilized to perform material-selective and energy-selective image reconstruction. Compared with the conventional single spectral CT, MCT has a superior material distinguishability. Therefore, it has wide potential applications in both medical and industrial areas. However, the nonlinearity and ill condition of the MCT problem make it difficult to get high-quality and fast convergence of images for existing MCT reconstruction algorithms. In this paper, we proposed an iterative reconstruction algorithm based on an oblique projection modification technique (OPMT) for fast basis material decomposition of MCT. In the case of geometric inconsistency, along the current x-ray path, the oblique projection modification direction not only relates to the polychromatic projection equation of the known spectrum, but it also comprehensively refers to the polychromatic projection equation information of the unknown spectra. Moreover, the ray-by-ray correction makes it applicable to geometrically consistent projection data. One feature of the proposed algorithm is its fast convergence speed. The OPMT considers the information from multiple polychromatic projection equations, which greatly speeds up the convergence of MCT reconstructed images. Another feature of the proposed algorithm is its high flexibility. The ray-by-ray correction will be suitable for any common MCT scanning mode. The proposed algorithm is validated with numerical experiments from both simulated and real data. Compared with the ASD-NC-POCS and E-ART algorithms, the proposed algorithm achieved high-quality reconstructed images while accelerating the convergence speed of them.