Control Points In Images Research Articles

Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment

Structure-from-motion (SfM) algorithms greatly facilitate the production of detailed topographic models from photographs collected using unmanned aerial vehicles (UAVs). However, the survey quality achieved in published geomorphological studies is highly variable, and sufficient processing details are never provided to understand fully the causes of variability. To address this, we show how survey quality and consistency can be improved through a deeper consideration of the underlying photogrammetric methods. We demonstrate the sensitivity of digital elevation models (DEMs) to processing settings that have not been discussed in the geomorphological literature, yet are a critical part of survey georeferencing, and are responsible for balancing the contributions of tie and control points. We provide a Monte Carlo approach to enable geomorphologists to (1) carefully consider sources of survey error and hence increase the accuracy of SfM-based DEMs and (2) minimise the associated field effort by robust determination of suitable lower-density deployments of ground control. By identifying appropriate processing settings and highlighting photogrammetric issues such as over-parameterisation during camera self-calibration, processing artefacts are reduced and the spatial variability of error minimised.We demonstrate such DEM improvements with a commonly-used SfM-based software (PhotoScan), which we augment with semi-automated and automated identification of ground control points (GCPs) in images, and apply to two contrasting case studies — an erosion gully survey (Taroudant, Morocco) and an active landslide survey (Super-Sauze, France). In the gully survey, refined processing settings eliminated step-like artefacts of up to ~50mm in amplitude, and overall DEM variability with GCP selection improved from 37 to 16mm. In the much more challenging landslide case study, our processing halved planimetric error to ~0.1m, effectively doubling the frequency at which changes in landslide velocity could be detected. In both case studies, the Monte Carlo approach provided a robust demonstration that field effort could by substantially reduced by only deploying approximately half the number of GCPs, with minimal effect on the survey quality. To reduce processing artefacts and promote confidence in SfM-based geomorphological surveys, published results should include processing details which include the image residuals for both tie points and GCPs, and ensure that these are considered appropriately within the workflow.

Orbit Determination of the Dawn Spacecraft with Radiometric and Image Data

The orbit of the Dawn spacecraft around asteroid 4 Vesta has been reconstructed using radiometric measurements and image control points. Image control points are introduced as a new measurement type because they constrain the orbit in the along- and crosstrack directions, whereas radiometric measurements provide a stronger constraint in the radial direction. Image control points are available whenever two image footprints on the surface overlap. The measurement uncertainty of images is limited by pixel scale that, in the case of the Dawn mission at Vesta, is between 20 and . The obtained image constraint residual root mean square is approximately the pixel scale for all mission phases. Radiometric residual root mean square spanned from 0.07 to for Doppler and from 0.7 to 1.5 m for range measurements. Radiometric and image constraint residual root-mean-square values are comparable to those reported by the Dawn Science Team using independent software and processing methodology. Improvements as a result of including image control points are largest in the along- and crosstrack directions during the survey phase of the mission. Agreement between the current orbit reconstruction and the trajectory archived by the Dawn Gravity Science Team improves from several kilometers with radiometric measurements to hundreds of meters when image control points are included.

Locating Control Points In Aerial Images With A Multi‐Scale Approach Based On Terrestrial Image Patches

AbstractThe objective of the proposed approach is to locate and measure ground control points in aerial images when large image search spaces are defined due to the use of inaccurate initial exterior orientation parameters, such as those provided by consumer‐grade navigation systems. Vertical terrestrial image patches covering control point areas are generated and compared with aerial patches using feature‐ and area‐based matching algorithms to automatically determine their corresponding positions in aerial images with sub‐pixel precision. The approach is based on techniques for both image search space reduction and adaptive least squares matching. Experiments with real data were performed with bundle block triangulation and the results were analysed using control and check points in both object and image spaces. The proposed technique enabled a significant reduction in the search space within which it was feasible to successfully locate control points. Compared with manual measurements, the results obtained by the automatic technique were more accurate, achieving one‐fifth of the ground sample distance in planimetric check point discrepancies.

Research on 3D Tunnel Modeling Based on Close-Range Photogrammetry

Combined with the experimental tunnel actual environment to select the appropriate control points as the logo, layout tunnel three dimensional modeling control network. using high-precision total station TM30 to control, measure and acquire image control points coordinate. in the following field collection imaging principle, the actual target of image acquisition, 3D modeling of tunnel based on the software platform of Lensphoto, the research results show that, the tunnel homonymous control point coordinates measured by Lensphoto three-dimensional model and using high-precision total station TM30 observations are of basic agreement, the error in the centimeter level, verified the feasibility of using digital close range photogrammetry in actual measurement of tunnel engineering,has the long-term guiding significance to the tunnel three-dimensional digital information collection and safety production.

MULTI-SCALE MATCHING FOR THE AUTOMATIC LOCATION OF CONTROL POINTS IN LARGE SCALE AERIAL IMAGES USING TERRESTRIAL SCENES

Abstract. A technique to automatically locate Ground Control Points (GCPs) in large aerial images is presented considering the availability of low accuracy direct georeferencing data. The approach is based on image chips of GCPs extracted from vertical terrestrial images. A strategy combining image matching techniques was implemented to select correct matches. These matches were used to define a 2D transformation with which the GCP is projected close to its correct position, reducing the search space in the aerial image. Area-based matching with some refinements is used to locate GCPs with sub-pixel precision. Experiments were performed with multi-scale images and assessed with a bundle block adjustment simulating an indirect sensor orientation. The accuracy analysis was accomplished based on discrepancies obtained from GCPs and check points. The results were better than interactive measurements and a planimetric accuracy of 1/5 of the Ground Sample Distance (GSD) for the check points was achieved.

Design of the Ground Control Point Image Databases

Ground control point data is necessary in the aerospace images geometric processing. This study proposed the design solutions for the ground control point image databases. At first, this study compared the storage management manners of the control point data. Then, it analyzed the control point image data features. Accordingly, it mentioned the database storage solutions. Next, this study discussed the image control point database query methods. Finally, it designed a ground control point image database management and application system. The experiments show that it is an effective method for the control point image database.

The 2008/09 surge of central Yulinchuan glacier, northern Tibetan Plateau, as monitored by remote sensing

AbstractThe 2008/09 surge of central Yulinchuan glacier (CYG) on the northwestern slope of Muztag mountain, Tibetan Plateau, is studied based on satellite remote sensing. The widely used cross-correlation feature-tracking method was used to collect satellite image control points and validate their geometric accuracies, as well as to derive glacier surface velocity. Changes in glacier length and area were also retrieved. Results show that the surge of CYG initiated in May 2008 and terminated in July 2009. Two diffluent glacier termini advanced 590 ± 26 m (5.1 ± 0.2% of the 2004 length) and 182 ± 26m (1.8 ± 0.3%), respectively, and glacier area increased by ∼1.41 ± 0.11 km2 (4.6 ± 0.4% of the 2004 area) during this period. The most significant surge period was October 2008 to March 2009, when most of the terminus advances and area increases occurred. The glacier surface drastically crevassed during this time, as much as 1657 ± 297 m of horizontal surface displacements were produced by surge ice, and maximum surface speed reached 13 ± 1.5 m d-1. Results of transverse and longitudinal velocity profiles revealed two surge waves during this surge of CYG.

Intelligent Sensor for Image Control Point of Eigenfaces for Face Recognition

Problem statement: The sensor for image control point in Face Recognition (FR) is one of the most active research areas in computer vision and pattern recognition. Its practical application includes forensic identification, access control and human computer interface. The task of a FR system is to compare an input face image against a database containing a set of face samples with known identity and identifying the subject to which the input face belongs. However, a straightforward implementation is difficult since faces exhibit significant variations in appearance due to acquisition, illuminations, pose and aging variations. This research contracted with several images combined through image registration offering the possibility of improving eigenface recognition. Sensor detection by head orientation for image control point of the training sets collected in a database was discussed. Approach: In fact, the aim of such a research consisted first, identification of the face recognition and the possibility of improving eigenface recognition. So the approach of eigenface focused on three fundamental points: generating eigenfaces, classification and identification and the method used image processing toolbox to perform the matrix calculations. Results: Observation showed that the performance of the proposed technique proved to be less affected by registration errors. Conclusion/Recommendations: We presented the intelligent sensor for face recognition using image control point of eigenfaces. It is important to note that many applications of face recognition do not require perfect identification, although most require a low false-positive rate. In searching a large database of faces, for example, it may be preferable to find a small set of likely matches to present to the user.

Wavelet-based image registration technique for high-resolution remote sensing images

Image registration is the process of geometrically aligning one image to another image of the same scene taken from different viewpoints at different times or by different sensors. It is an important image processing procedure in remote sensing and has been studied by remote sensing image processing professionals for several decades. Nevertheless, it is still difficult to find an accurate, robust, and automatic image registration method, and most existing image registration methods are designed for a particular application. High-resolution remote sensing images have made it more convenient for professionals to study the Earth; however, they also create new challenges when traditional processing methods are used. In terms of image registration, a number of problems exist in the registration of high-resolution images: (1) the increased relief displacements, introduced by increasing the spatial resolution and lowering the altitude of the sensors, cause obvious geometric distortion in local areas where elevation variation exists; (2) precisely locating control points in high-resolution images is not as simple as in moderate-resolution images; (3) a large number of control points are required for a precise registration, which is a tedious and time-consuming process; and (4) high data volume often affects the processing speed in the image registration. Thus, the demand for an image registration approach that can reduce the above problems is growing. This study proposes a new image registration technique, which is based on the combination of feature-based matching (FBM) and area-based matching (ABM). A wavelet-based feature extraction technique and a normalized cross-correlation matching and relaxation-based image matching techniques are employed in this new method. Two pairs of data sets, one pair of IKONOS panchromatic images from different times and the other pair of images consisting of an IKONOS panchromatic image and a QuickBird multispectral image, are used to evaluate the proposed image registration algorithm. The experimental results show that the proposed algorithm can select sufficient control points semi-automatically to reduce the local distortions caused by local height variation, resulting in improved image registration results.

ポリゴンの図心を基準点に用いた高精度幾何補正

Recently, high resolution satellite imageries (about 1 meter spatial resolution) are used in many fields. Generally, they are expected to be overlaid with GIS data and updating existing map. It is important issue for remote sensing to detect land-use/land-cover changes using high resolution satellite imageries. Therefore, the accuracy of geometric correction should be reduced to sub-pixel error. It is necessary to prepare accurate control points to correct geometrically. The control points are Ground Control Point (GCP) collected from the ground surface and corresponded Image Control Point (ICP) from selected imagery. Nowadays, the defined centroids of circles on the ground are used as accurate control points. However, it is difficult to prepare circle type GCPs. The existing types could be evaluated at first part of this study. Continuously, accurate geometric correction could be established using centroids of the polygons as control points. The result showed 0.54m pixel error by applying proposed methodology. It showed that the accuracy of proposed method is approximately same as circle type centroid method. Therefore centroid of rectangular type and polygon type were efficient for the geometric correction of high resolution imageries.

Performance Measure as Feedback Variable in Image Processing

This paper extends the view of image processing performance measure presenting the use of this measure as an actual value in a feedback structure. The idea behind is that the control loop, which is built in that way, drives the actual feedback value to a given set point. Since the performance measure depends explicitly on the application, the inclusion of feedback structures and choice of appropriate feedback variables are presented on example of optical character recognition in industrial application. Metrics for quantification of performance at different image processing levels are discussed. The issues that those metrics should address from both image processing and control point of view are considered. The performance measures of individual processing algorithms that form a character recognition system are determined with respect to the overall system performance.

Invariant models for ground control points in high resolution images

This paper addresses the problem of automatic identification of ground control points (GCPs) in high resolution satellite digital images. The method is based on characteristics of GCPs and their neighbourhood, invariant with respect to rotation and contrast change. The characterization algorithm, which leads to a feature space with a fixed number of dimensions a critical advantage of the method is based on the autocorrelation function of an azimuthal projection around the GCP. Such features are first extracted from a set of reference images and stored in a database, together with the GCP geographical co-ordinates, and used in Euclidean minimum distance classifier, looking for matches with pixels of the image being geo-referenced. Practical issues lead to the necessity to filter out potential wrong identifications, through a consistency filter based on geometrical patterns made of ambiguous GCPs. The performance of the method is studied, demonstrating its insensitivity with respect to rotation and variations of contrast, its discrimination power, and its sensitivity to determine the absolute position of a GCP to within a fraction of pixel.

Automatic selection of control-points from shadow structures†

Abstract The purpose of this paper is to describe a procedure for the automatic selection of control-points in remote-sensing images of high-relief terrains for alignment with a reference map. This problem has been found to be of strategic importance whenever remote sensing images have to be integrated in a Geographic Information System (GIS) and processed in real time. The procedure described here is based on the recognition of shadow structures in the satellite image and on their comparison with the computer-generated shadows obtained from the Digital Terrain Model (DTM) of the region. The procedure was developed for a Landsat TM image of the Aurina Valley (in the Pusteresi Alps) with the DTM obtained from an IGM (Istituto Geografico Militare) 1:25000 reference map, but with minor changes it can be extended to other remote-sensing images. Comparison of the shadow structures is performed by similarity evaluation of a simplified model of their shapes described by means of inertia ellipses. Each pair of sh...