Reference Control Points Research Articles

Calibration-Based ALE Model Order Reduction for Hyperbolic Problems with Self-Similar Travelling Discontinuities

We propose a novel Model Order Reduction framework that is able to handle solutions of hyperbolic problems characterized by multiple travelling discontinuities. By means of an optimization based approach, we introduce suitable calibration maps that allow us to transform the original solution manifold into a lower dimensional one. The novelty of the methodology is represented by the fact that the optimization process does not require the knowledge of the discontinuities location. The optimization can be carried out simply by choosing some reference control points, thus avoiding the use of some implicit shock tracking techniques, which would translate into an increased computational effort during the offline phase. In the online phase, we rely on a non-intrusive approach, where the coefficients of the projection of the reduced order solution onto the reduced space are recovered by means of an Artificial Neural Network. To validate the methodology, we present numerical results for the 1D Sod shock tube problem, for the 2D double Mach reflection problem, also in the parametric case, and for the triple point problem.

High-Accuracy Filtering of Forest Scenes Based on Full-Waveform LiDAR Data and Hyperspectral Images

Airborne light detection and ranging (LiDAR) technology has been widely utilized for collecting three-dimensional (3D) point cloud data on forest scenes, enabling the generation of high-accuracy digital elevation models (DEMs) for the efficient investigation and management of forest resources. Point cloud filtering serves as the crucial initial step in DEM generation, directly influencing the accuracy of the resulting DEM. However, forest filtering presents challenges in dealing with sparse point clouds and selecting appropriate initial ground points. The introduction of full-waveform LiDAR data offers a potential solution to the problem of sparse point clouds. Additionally, advancements in multi-source data integration and machine learning algorithms have created new avenues that can address the issue of initial ground point selection. To tackle these challenges, this paper proposes a novel filtering method for forest scenes utilizing full-waveform LiDAR data and hyperspectral image data. The proposed method consists of two main steps. Firstly, we employ the improved dynamic graph convolutional neural network (IDGCNN) to extract initial ground points. In this step, we utilize three types of low-correlation features: LiDAR features, waveform features, and spectral features. To enhance its accuracy and adaptability, a self-attention module was incorporated into the DGCNN algorithm. Comparative experiments were conducted to evaluate the effectiveness of the algorithm, demonstrating that the IDGCNN algorithm achieves the highest classification accuracy with an overall accuracy (OA) value of 99.38% and a kappa coefficient of 95.95%. The second-best performer was the RandLA-net algorithm, achieving an OA value of 98.73% and a kappa coefficient of 91.68%. The second step involves refining the initial ground points using the cloth simulation filter (CSF) algorithm. By employing the CSF algorithm, non-ground points present in the initial ground points are effectively filtered out. To validate the efficacy of the proposed filtering method, we generated a DEM with a resolution of 0.5 using the ground points extracted in the first step, the refined ground points obtained with the combination of the first and second steps, and the ground points obtained directly using the CSF algorithm. A comparative analysis with 23 reference control points revealed the effectiveness of our proposed method, as evidenced by the median error of 0.41 m, maximum error of 0.75 m, and average error of 0.33 m.

Geometric distortion assessment in 3T MR images used for treatment planning in cranial Stereotactic Radiosurgery and Radiotherapy.

Magnetic Resonance images (MRIs) are employed in brain Stereotactic Radiosurgery and Radiotherapy (SRS/SRT) for target and/or critical organ localization and delineation. However, MRIs are inherently distorted, which also impacts the accuracy of the Magnetic Resonance Imaging/Computed Tomography (MRI/CT) co-registration process. In this phantom-based study, geometric distortion is assessed in 3T T2-weighted images (T2WIs), while the efficacy of an MRI distortion correction technique is also evaluated. A homogeneous polymer gel-filled phantom was CT-imaged before being irradiated with 26 4-mm Gamma Knife shots at predefined locations (reference control points). The irradiated phantom was MRI-scanned at 3T, implementing a T2-weighted protocol suitable for SRS/SRT treatment planning. The centers of mass of all shots were identified in the 3D image space by implementing an iterative localization algorithm and served as the evaluated control points for MRI distortion detection. MRIs and CT images were spatially co-registered using a mutual information algorithm. The inverse transformation matrix was applied to the reference control points and compared with the corresponding MRI-identified ones to evaluate the overall spatial accuracy of the MRI/CT dataset. The mean image distortion correction technique was implemented, and resulting MRI-corrected control points were compared against the corresponding reference ones. For the scanning parameters used, increased MRI distortion (>1mm) was detected at areas distant from the MRI isocenter (>5cm), while median radial distortion was 0.76mm. Detected offsets were slightly higher for the MRI/CT dataset (0.92mm median distortion). The mean image distortion correction improves geometric accuracy, but residual distortion cannot be considered negligible (0.51mm median distortion). For all three datasets studied, a statistically significant positive correlation between detected spatial offsets and their distance from the MRI isocenter was revealed. This work contributes towards the wider adoption of 3T imaging in SRS/SRT treatment planning. The presented methodology can be employed in commissioning and quality assurance programmes of corresponding treatment workflows.

A Vision-based navigation system for landing procedure

An autonomous vision-based landing system was designed and its performance is analysed and measured by an UAS. The system equipment is based on a single camera to determine its position and attitude with respect to a well-defined landing pattern. The developed procedure is based on photogrammetric Space Resection Solution, which provides the position and camera attitude reckoning starting from at least three, not aligned, reference control points whose image coordinates may be measured in the image camera frame. Five circular coloured targets were placed on a specific landing pattern, their 2D image frame coordinates was extracted through a particular algorithm. The aim of this work is to compute UAS precise position and attitude from single image, in order to have a good approach to landing field. This procedure can be used in addition or for replacement of GPS tracking and can be applied when the landing field is movable or located on a moving platform, the UAS will follow the landing pattern until the landing phase will be closed.

The application of civic technologies in a field survey of landslides

AbstractSlope failures are financially devastating natural hazards that contribute to land degradation in many areas throughout the world. The adaptation of civic technologies (Google Tango) in a field survey of landslides was examined. Data acquired from different resources and processed using different technologies were merged into a single model to concurrently demonstrate the interoperability and scalability of these data and the model quality. Reference control points were established using a survey‐grade Topcon Hiper SK global navigation satellite system receiver and a Topcon GPT 9003 M total station. An aerial survey was performed in an area of approximately 30,000 m2 using airborne laser scanning (9 points/m2) and aerial photogrammetry using a remotely piloted aircraft system (500 points/m2). The models suffered from data gaps in less visible areas, and micro‐scale landforms reflecting landslide activity were poorly visible. The missing details were supplied using data obtained from close‐range photogrammetry (9,132 m2; 92,300 points/m2) and a Lenovo Phab 2 Pro running Google Tango, which acquired detailed point clouds in near real‐time conditions (1,847 m2; 109,000 points/m2). Scans using the phablet provided point clouds with homogeneously dispersed data gaps, but the spatial accuracy was lower. However, the ergonomics of its field use and its low cost made it competitive with other technologies. The results confirmed that models based on point clouds acquired using different technologies allow the identification and measurement of micro‐scale landforms that may indicate landslide activity.

Possibilities of Use of the UAVs of the Average Price Segment for Mapping of Agricultural Resources

Purpose. Development of methodical approaches and recommendations for the preparation and implementation of various tasks for obtaining data using UAV, to assess the possibilities and spatial accuracy of the data obtained from the mid-price drones with the purpose of expediency of its use. Methods. Field geodetic surveys, UAV launches, cameral processing of the obtained data with use of the computer equipment. Results. Field geodetic works have been performed for fixing of checkpoints. Some methodological approaches for preparatory pre-flight and flight work have been formulated. The features of conducting flight missions are described. The aerial photography of the explored polygon is represented by various models of drones at different heights. Orthophotomaps and digital relief models have been obtained with using the software product «AgiSoft PhotoScan». There is a discrepancy in the given overlay of photos and actual. For analysis, data with actual overlapping of photographs >70% was used. Received DEM and orthophotomaps were compared with the reference control points. An orthoplane is required to visualize the terrain, DEM - allows you to determine the value of each pixel in height (H). As expected, not all control points coincided with their image on orthophotomapsUsing the «ArcGIS» software system, an analysis of the data has been made, the spatial accuracy of the data obtained is calculated. Conclusions. It has been established that the use of certain models of UAVs at certain heights used in the researches is possible for obtaining scale maps of scale 1: 5000 - 1: 1000. As for the data corresponding to the accuracy of scale 1: 25000, the receipt and use of such data is ineffective.

Reliable Subpixel Ground Control Point Estimation Algorithm Using Vector Roads

This letter proposes a novel algorithm for reliable and accurate ground control point (GCP) estimation, which uses vector road network data as a reference. Roads are detected in satellite images, and road patches are used as a template, which is matched against the vector road data by using a nonlinear criterion based on the distance transform. The proposed approach is compared with the standard correlation-based template matching in terms of the reliability and accuracy of localization on a test set of five RapidEye images with 380 reference control points. The experiments demonstrate superior reliability with respect to the size of the search window and the variable image acquisition conditions. While the subpixel accuracy is achieved by the cross-correlation method over the search area up to 20% of the template size, the proposed method achieves comparable accuracy over a search area that is ten times larger than the template image.

UAV image registration based on the weighted total-least-squares

In optical image registration,the polynomial regression model generally supposes that the reference control points( RCPs) used as the coefficient matrix is error- free. However,the actual RCPs often inevitably contain errors and RCPs residual errors between different images are not the same. The general least squares method( LS) only considers the error in the observation vector whereas the total least squares method( TLS) takes the errors of both the observation vector and the coefficient matrix into account and assumes that they have the same residual error. In view of this situation,this paper introduces a more reasonable weighted total least squares method( WTLS) for polynomial regression coefficients estimation. Experiments show that the WTLS can estimate the parameters better and significantly improve the image registration accuracy.

Cartographic heritage: Toward unconventional methods for quantitative analysis of pre-geodetic maps

Abstract Historical cartography all over the world is a fundamental part of Cultural Heritage, and it needs to be preserved from damage of its analogical support due to ageing. Regeneration of ancient cartography in digital form is an interesting way not only to preserve historical cartographic documents as Cultural Heritage, but also to allow new chances of understanding and using the historical information they record. In such a way, modern digital techniques, in particular study of map deformations and map georeferencing, help in metric analysis of ancient cartography, and at last they appear to be useful for researchers to derive historical information for their studies, for example related to urban development or to geomorphological and environmental topics. The present research would give an example of the usefulness of the digital regeneration of ancient cartography, but also an example of possible difficulties in correctly interpreting information preserved in historical cartography, especially the pre-geodetic one. The study subject consists in three contemporaneous pre-geodetic maps (late 16th century) from the ancient Po river delta area (Italy), by means of which a geometrically correct representation of those parts of the landscape, not preserved today because of sea erosion, was tried. In fact, standard georeferencing methods, that use reference control points to compare historical cartography with the present one, in this specific case demonstrated to be not successful in describing the real location of disappeared landscape details with an adequate level of accuracy. For these reasons, in order to define which map among the others was the most faithful to the contemporaneous physical reality, a compound methodology, consisting of a three-step analytical process, is here applied to the three samples. Starting from measurement of sighting angles and distances applied to a number of landmarks, a splitting of the old maps in sub-areas, probably corresponding to the set of original surveyed zones, was performed. In the area of main concern, the use of absolute measurements was avoided in order to check the level of inner congruence of the representation. Finally, a new specific error index, that can also be applied to maps lacking an explicit graphical scale, is proposed to evaluate the map truthfulness degree. The proposed method can be applied to other similar examples from ancient cartography.

Scaled total-least-squares-based registration for optical remote sensing imagery

In optical image registration, the reference control points (RCPs) used as explanatory variables in the polynomial regression model are generally assumed to be error free. However, this most frequently used assumption is often invalid in practice because RCPs always contain errors. In this situation, the extensively applied estimator, the ordinary least squares (LS) estimator, is biased and incapable of handling the errors in RCPs. Therefore, it is necessary to develop new feasible methods to address such a problem. This paper discusses the scaled total least squares (STLS) estimator, which is a generalization of the LS estimator in optical remote sensing image registration. The basic principle and the computational method of the STLS estimator and the relationship among the LS, total least squares (TLS) and STLS estimators are presented. Simulation experiments and real remotely sensed image experiments are carried out to compare LS and STLS approaches and systematically analyze the effect of the number and accuracy of RCPs on the performances in registration. The results show that the STLS estimator is more effective in estimating the model parameters than the LS estimator. Using this estimator based on the error-in-variables model, more accurate registration results can be obtained. Furthermore, the STLS estimator has superior overall performance in the estimation and correction of measurement errors in RCPs, which is beneficial to the study of error propagation in remote sensing data. The larger the RCP number and error, the more obvious are these advantages of the presented estimator.

Modelling for registration of remotely sensed imagery when reference control points contain error

Reference control points (RCPs) used in establishing the regression model in the registration or geometric correction of remote sensing images are generally assumed to be “perfect”. That is, the RCPs, as explanatory variables in the regression equation, are accurate and the coordinates of their locations have no errors. Thus ordinary least squares (OLS) estimator has been applied extensively to the registration or geometric correction of remotely sensed data. However, this assumption is often invalid in practice because RCPs always contain errors. Moreover, the errors are actually one of the main sources which lower the accuracy of geometric correction of an uncorrected image. Under this situation, the OLS estimator is biased. It cannot handle explanatory variables with errors and cannot propagate appropriately errors from the RCPs to the corrected image. Therefore, it is essential to develop new feasible methods to overcome such a problem. This paper introduces a consistent adjusted least squares (CALS) estimator and proposes relaxed consistent adjusted least squares (RCALS) estimator, with the latter being more general and flexible, for geometric correction or registration. These estimators have good capability in correcting errors contained in the RCPs, and in propagating appropriately errors of the RCPs to the corrected image with and without prior information. The objective of the CALS and proposed RCALS estimators is to improve the accuracy of measurement value by weakening the measurement errors. The conceptual arguments are substantiated by a real remotely sensed data. Compared to the OLS estimator, the CALS and RCALS estimators give a superior overall performance in estimating the regression coefficients and variance of measurement errors.

Recovering Filter-Based Microarray Data for Pathways Analysis Using a Multipoint Alignment Strategy

The use of commercial microarrays is rapidly becoming the method of choice for profiling gene expression and assessing various disease states. Research Genetics has provided a series of biological and software tools to the research community for these analyses. The fidelity of data analysis using these tools is dependent on a series of well-defined reference control points in the array. During the course of our investigations, it became apparent that in some instances the reference control points that are required for analysis became lost in background noise. This effectively halted the analysis and the recovery of any information contained within that experiment. To recover this data and to increase analytical veracity, the simple strategy of superimposing a template of reference control points onto the experimental array was developed. The utility of this tool is established in this communication.