Accuracy Assessment of Analytical Orientation Process in Close range Photogrammetry

Three-dimensional (3D) real scale models delivered from digital photogrammetric techniques have rapidly increased to meet the requirements of many applications in different fields of daily life. This paper deals with the establishment of a 3D real scale model from a block of images (18 images) that were captured by using Canon EOS 500D digital camera to cover a test field area consisting of 90 artificial target points, 25 of them are ground control points (GCPs) while the remains are checkpoints (CPs). The analytical photogrammetric processes including the calculation of interior orientation parameters (IOPs) of the camera during the camera calibration process, exterior orientation parameters (EOPs) of the camera in each capturing, and the object space (ground) coordinates of the model are calculated simultaneously based on collinearity equation using bundle block adjustment method (BBA). Assessment and validation of the accuracy of the results is an important task in this study that was implemented to determine and analyze the errors of 3D coordinates through linear regression analysis (LRA). Root mean square error (RMSE) is the statistical parameter that was used in the statistical analysis of results. The standard error is another statistical parameter which also used to evaluate the accuracy of locations and rotation angles (EOPs) of cameras. The total RMSE (RMSE)xyz of GCPs is ± 2.530 mm while the total RMSE (RMSExyz) of CPs is ± 2.740 mm. The overall accuracy of the work is 5.000 mm.

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.

Aiming to the difficult area of surveying and mapping, such as Antarctic, it is difficult to guarantee favourable observation condition for traditional mapping methods. Large-scale block adjustment (BA) without ground control point (GCP) is carried out by using domestic high-resolution satellite remote sensing imagery. The key technologies of BA model construction based on virtual control point (VCP), gross error detection and elimination, and robust and fast method of large-scale BA are studied in this paper. On this basis, the experimental analysis and validation of 13pairs (39 images) of ZY-3 satellite imagery in Antarctic are carried out. The results show that the sparse matrix technology can effectively reduce the memory requirement. The combined matrix block and GPU parallel technology can solve the problem of large-scale BA computational efficiency. In addition, after BA, the maximum residual is 3.920 pixel, the root mean square error (RMSE) is 0.169 pixel in the X (flight) direction, the maximum residual error is 5.933 pixel, and the RMSE is 0.191 pixel in the Y (scan) direction. The proposed method has certain accuracy and stability in large-scale BA without GCP using high-resolution satellite remote sensing imagery in Antarctica. The relative positioning accuracy can reach sub-pixel level, which can meet the requirements of cartographic splicing.

Georeferenced archival aerial images are key elements for the study of landscape evolution in the scope of territorial planning and management. The georeferencing process proceeds by applying to photographs advanced digital photogrammetric techniques integrated along with a set of ground truths termed ground control points (GCPs). At the end of that stage, the accuracy of the final orthomosaic is assessed by means of root mean square error (RMSE) computation. If the value of that index is deemed to be unsatisfactory, the process is re-run after increasing the GCP number. Unfortunately, the search for GCPs is a costly operation, even when it is visually carried out from recent digital images. Therefore, an open issue is that of achieving the desired accuracy of the orthomosaic with a minimal number of GCPs. The present paper proposes a geostatistically-based methodology that involves performing the spatialization of the GCP errors obtained from a first gross version of the georeferenced orthomosaic in order to produce an error map. Then, the placement of a small number of new GCPs within the sub-areas characterized by the highest local errors enables a finer georeferencing to be achieved. The proposed methodology was applied to 67 historical photographs taken on a geo-morphologically complex study area, located in Southern Italy, which covers a total surface of approximately 55,000 ha. The case study showed that 75 GCPs were sufficient to garner an orthomosaic with coordinate errors below the chosen threshold of 10 m. The study results were compared with similar works on georeferenced images and demonstrated better performance for achieving a final orthomosaic with the same RMSE at a lower information rate expressed in terms of nGCPs/km2.

KOMPSAT-2와 같은 고해상 위성영상은 대상영역의 3차원 위치결정을 위하여 RPC(Rational Polynomial Coefficient)가 포함된 자료를 제공한다. 그러나 RPC로 계산된 영상기하는 일정량의 편이(systematic errors)를 지니고 있는 상태이며, 이를 보정하기 위해서는 수 개 이상의 지상기준점(ground control point)이 필요하다. 이에 본 논문에서는 지상기준점 없이 입체영상(stereo pair)과 SRTM(Shuttle Radar Topography Mission) DEM(Digital Elevation Model) 사이의 대응점(tie point)만을 이용하여 자동으로 영상 기하를 보정하는 효과적인 방법을 제안하였다. 이러한 방법은 4가지 단계를 포함 한다: 1) 대응점 추출, 2) 대응점에 대한 지상좌표 결정, 3) SRTM DEM을 이용한 지상좌표의 보정, 4) RPC 보정 모델의 파라미터 결정. 우리는 KOMPSAT-2 입체영상을 이용하여 제안된 방법의 성과를 입증하였다. 검사점(check point)을 통해 계산된 RMSE(Root Mean Square Error)는 X와 Y, Z방향으로 각각 약 3.55 m, 9.70 m, 3.58 m를 나타냈다. 이는 SRTM DEM을 이용하여 RPC가 지닌 편이를 X, Y 및 Z 모든방향에 대하여 10 m이내의 정확도로 자동보정할 수 있다는 것을 의미한다. A high resolution satellite imagery such as KOMPSAT-2 includes a material containing rational polynomial coefficient (RPC) for three-dimensional geopositioning. However, image geometries which are calculated from the RPC must have inevitable systematic errors. Thus, it is necessary to correct systematic errors of the RPC using several ground control points (GCPs). In this paper, we propose an efficient method for automatic correction of image geometries using tie points of a stereo pair and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) without GCPs. This method includes four steps: 1) tie points extraction, 2) determination of the ground coordinates of the tie points, 3) refinement of the ground coordinates using SRTM DEM, and 4) RPC adjustment model parameter estimation. We validates the performance of the proposed method using KOMPSAT-2 stereo pair. The root mean square errors (RMSE) achieved from check points (CPs) were about 3.55 m, 9.70 m and 3.58 m in X, Y;and Z directions. This means that we can automatically correct the systematic error of RPC using SRTM DEM.

Evaluation of Control Points’ Distribution on Distortions and Geometric Transformations for Aerial Images Rectification

This paper proposes a stereo‐image block‐adjustment algorithm without ground control points (GCPs) by adopting a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) as a height assistant. Using dense, evenly distributed tie points on images used in the block adjustment, the initial height values of tie points are derived directly from the SRTM DEM. In the subsequent block‐adjustment iterative computation, the height correction value for tie points is rigorously restricted by the SRTM DEM elevation. A total of 509 ZY‐3 satellite stereo‐images across 190 000 km2 of China's Hubei Province were used for this block‐adjustment experiment. The results show that the vertical root mean square errors (RMSEs) are improved from 7·2 to 2·3 m without GCPs, while the RMSEs in flat and mountainous terrain are improved to 1·5 and 2·8 m, respectively, fulfilling the vertical accuracy requirement of China's 1:25 000 scale mapping.

Geometric correction is used to correct the registration errors in remotely sensed images. These images are often compared to ground control points (GCPs) either by using an accurate map (image to map) or using another geo-referenced image (image to image) and then resampled. Accordingly, the exact locations and the appropriate pixel values can be calculated in more accurate, time-wise and effortless manner. In the traditional methods, the GCPs are manually selected and then the transformation models are applied which yield time consuming and less accurate processes. The objective of this work is to develop an automatic approach for image registration based on another geo-referenced image using five feature extraction models. They are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Discrete Wavelet Transforms (DWT), (SIFT & DWT), and (SURF & DWT). The GCPs were selected based on the least-squares adjustments as the basis for improving the spatial accuracy of all the linking points in both images. The obtained results showed that models have higher accuracy in image registration with Root Mean Square Error (RMSE) less than 0.5. The developed automated image registration method provides more accurate results and saves time, money and effort.

Background and purposeThe study evaluated the differences in leaf positioning deviations by the log files of three advanced accelerators with two delivery techniques, and established specific assessment parameters of leaf positioning deviations for different types of accelerators.MethodsA total of 420 treatment plans with 5 consecutive treatment log files were collected from the Trilogy, TrueBeam and Halcyon accelerators. Millennium MLC was equipped on the Trilogy and TrueBeam accelerators. A jawless design and dual-layer MLC were adopted on the Halcyon accelerator. 70 IMRT and 70 VMAT plans were selected randomly on each accelerator. The treatment sites of all plans included head and neck, chest, breast, pelvis and other sites. The parsing tasks for 2100 log files were proceeded by SunCheck software from Sun Nuclear Corporation. The maximum leaf root mean square (RMS) errors, 95th percentile errors and percentages of different leaf positioning errors were statistically analyzed. The correlations between these evaluation parameters and accelerator performance parameters (maximum leaf speed, mean leaf speed, gantry and arc angle) were analyzed.ResultsThe average maximum leaf RMS errors of the Trilogy in the IMRT and VMAT plans were 0.44 ± 0.09 mm and 0.79 ± 0.07 mm, respectively, which were higher than the TrueBeam's 0.03 ± 0.01 mm, 0.03 ± 0.01 mm and the Halcyon's 0.05 ± 0.01 mm, 0.07 ± 0.01 mm. Similar data results were shown in the 95th percentile error. The maximum leaf RMS errors were strongly correlated with the 95th percentile errors (Pearson index > 0.5). The leaf positioning deviations in VMAT were higher than those in IMRT for all accelerators. In TrueBeam and Halcyon, leaf position errors above 1 mm were not found in IMRT and VMAT plans. The main influencing factor of leaf positioning deviation was the leaf speed, which has no strong correlation with gantry and arc angles.ConclusionsCompared with the quality assurance guidelines, the MLC positioning deviations tolerances of the three accelerators should be tightened. For both IMRT and VMAT techniques, the 95th percentile error and the maximum RMS error are suggested to be tightened to 1.5 and 1 mm respectively for the Trilogy accelerator. In TrueBeam and Halcyon accelerators, the 95th percentile error and maximum RMS error of 1 and 0.5 mm, respectively, are considered appropriate.

This study evaluates a methodology for using IKONOS stereo imagery to determine the height and position of buildings in dense residential areas. The method was tested on three selected sites in an area of 8.5 km long by 7 km wide and covered by two overlapping (97% overlap) IKONOS images. The images were oriented using rational function models in addition to ground control points. Buildings were identified using an algorithm that utilized the Digital Surface Model (DSM) extracted from the images in addition to the image spectral properties. A digital terrain model was used with the DSM created from the IKONOS stereo imagery to compute building heights. Positional accuracy and building heights were evaluated using corner coordinates extracted from topographic maps and surveyed building heights. The results showed that the average building detection percentage for the test area was 82.6% with an average missing factor of 0.16. When the image rational polynomial coefficients were used to build the image model, results showed a horizontal accuracy of 2.42 and 2.39 m Root Mean Square Error (RMSE) for the easting and northing coordinates, respectively. When ground control points were used, the results improved to the sub-meter level. Differences between building heights extracted from the image model and the corresponding heights obtained through traditional ground surveying had a RMSE of 1.05 m.

OPINION article Front. Physiol., 22 January 2014Sec. Integrative Physiology Volume 4 - 2013 | https://doi.org/10.3389/fphys.2013.00403

The important preprocessing techniques for remote sensing data and geometrical alteration is the geometric correction. In this paper ,it covers two models which are used in to three dimensions the physical model and the projective Transformation for SPOT 2 to determine the geometric correction of study region of Mosul city. The ground control points are located on SPOT 2 satellite image. These models are producing the X residual, Y residual and root mean square error (RMSE) and then comparing the effects of two models. The X residual for SPOT 2 physical model is lower than X residual for projective transformation at 0.1420, while the Y residual for physical model has higher comparing with Y residual for projective transformation at 0.1143and Total RMSE for SPOT 2 physical model is lower than Total RMSE for projective Transformation at 0.1823. The physical model is superior to Projective model, so that, it is highly recommended to be used for very precise application and not to be replaced by these non-physical models. For the physical model, it is clearly that, the altitude of the Ground Control Points (or Check Points) does not effect on its individual RMSE, when using Projective model the RMSE for high altitude was high ,where at low altitude it was low too(extreme relationship). DOI:

The launch of the very high resolution (VHR) sensor satellites has paved the way for further exploitation of the capabilities of stereo satellite imagery for many applications and most importantly in the extraction of topographical details. Advances in digital technologies in the photogrammetric technique provide more accurate and up to date topographic representation, while traditional methods of topographic mapping are time-consuming and require large technical support. This paper explores the methods of production and an analysis of the capabilities of the high-resolution stereo imaging system for the extraction of features (topographic map) and generation of digital elevation model (DEM). This study uses high-resolution GeoEye-1 stereo pair images. For qualitative assessment, topographic features were extracted by digitizing the georeferenced stereo pair images and overlapped to form a DEM and orthophoto within the Summit Evolution software. Georeferencing was carried out using the existing Ground Control Points (GCPs) and Check Points (CPs) established using Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) technique. For quantitative assessment, a total of 7 existing GCPs were considered suitable for use and 5 CPs were established as a check for accuracy of the output. The accuracy of the CPs was evaluated by computing Root Mean Square Error (RMSE). The average horizontal RMSE for the left image and right image were 1.17150098m and 0.819334672m respectively and the average vertical RMSE was 1.605898565m. Additionally, the standard deviation for elevation data from generated DEM using photogrammetric technique and existing elevation data of RTK-GNSS technique were computed, which resulted in 15.91286667 and 16.59529228 respectively. The accuracy was evaluated by comparing the results of the data extracted using stereo satellite images and those extracted from ground measurement techniques (Total station and RTK-GNSS). The comparative analysis showed that stereo pair images provide a viable alternative to other ground mapping techniques providing results within the acceptable range. Based on error estimation and analysis, it concludes that, if strict photogrammetric processing model and ground control points are employed, high-resolution satellite imagery can be used for the accurate generation and update of the topographic maps, DEM and digital orthophotos, which can be further be used as a reference for comparative analysis applications.

Crop height estimation based on UAV images: Methods, errors, and strategies

This study assessed the performance of recently released 3 arc second SRTM DEM version 4.1 by CSI-CGIAR and 1 arc second ASTER GDEM version 1 and version 2 by METI-NASA in comparison with ground control points from 1:50000 digital line graphs for the coastal zone of Shandong Province, Easter China. The vertical accuracy of SRTM DEM is 13.74 m root mean square error (RMSE), and GDEM version 1 reaches 24.11 m RMSE. Version 2 of ASTER GDEM shows better performance than version 1 and SRTM DEM with a RMSE of 12.12 m. A strong correlation of the magnitude of elevation error with slope and elevation is identified, with lager error magnitudes in the steeper slopes and higher elevations. Taking into account slope and elevation has the potential to considerably improve the accuracy of the SRTM DEM and GDEM version 1 products. However, this improvement for GDEM version 2 can be negligible due to their limited explanatory power for the DEM elevation errors.