Publicly available spatial data as a source of coordinates for ground control points

The availability of high accuracy GCPs (ground control points) and DEMs (digital elevation models) becomes the key issue for successful implementation of an image orthorectification project. It is a very difficult task for collecting a large number of high quality GCPs by using traditional methods to meet all the requirements for digital photogrammetric and orthorectification process. Airborne light detection and ranging (LiDAR) - also referred to as airborne laser scanning (ALS), provides an alternative for high-density and high-accuracy three-dimensional terrain point data acquisition. One of the appealing features in the LiDAR output is the direct availability of three dimensional coordinates of points and intensity data in object space. With LiDAR data, high- accuracy and high-resolution intensity image, hillshade DSM (digital surface model) image, and DEM can be generated. Due to high planimetric accuracy characteristics of LiDAR data, ground truth can be extracted from these LiDAR-derived products (e.g., hillshade image and intensity image). This study investigated the feasibility of using LiDAR-derived hillshade DSM image and intensity image to extract ground truth for aerial image orthorectification. Two sets of GCPs were extracted from hillshade image and intensity image separately, and then were used as the inputs for aerial triangulation processing. LiDAR- derived DEM was then employed for differential rectification to produce the final orthoimage. The assessment of the planimetric accuracy of orthorectified images by using different set of GCPs was conducted by comparing the coordinates of some checking points from orthoimages and correspondent GPS surveyed coordinates.

Abstract. Digital Elevation Model (DEM) was generated from Advanced LandObservation Satellite - The Panchromatic Remote-Sensing Instrument for Stereo Mapping(ALOS PRISM) stereo data using image matching and collinear correlation based on LeicaPhotogrametry Suite (LPS) software. The process needs three dimension of Ground ControlPoint (GCP) or Control Point (CP) XYZ as input data for collinear correlation to determineexterior orientation coefficient. The main problem of the DEM generation is the difficultyto obtain the accurate field measurement GCP in many areas. Therefore, another alternativeCP sources are needed. The aim of this research was to study the possibility of (IKONOS)image and Shuttle Radar Topography Mission (SRTM) X-C band to be used as CPreference for ALOS PRISM DEM generation. The study area was Sragen and Bandungregion. The DEM of each study area was generated using 2 methods: generated using fieldmeasurement GCPs taken by differential GPS and generated using CPs from IKONOSimage (XY coordinat) and SRTM for (Z elevation). The generated DEMs were compared.The accuracy of both DEMs were evaluated using another field measurement GCPs. Theresult showed that the generated DEM using CPs from IKONOS and SRTM X-C hadrelatively same height pattern and height distribution along transect line with the DEMusing GCPs. The absolute accuracy of the DEM using CPs was about 60% - 80% lessaccuracy comparing to the DEM using GCPs. This research showed that IKONOS imageand SRTM X-C band can be considered as good alternative CP source to generate highaccuracy DEM from ALOS PRISM stereo data.

This report is dealing with the digital surface model generation from VHR stereo satellite data with the focus on building height and shape extraction. The report provides a theoretical insight into orthorectification methods based on either empirical or rigorous, physical models and the theoretical aspects of digital surface model extraction. The requirement and ideal distribution of ground control points is discussed. In the final part of the report the results of four software packages, ENVI, PCI Geomatica, RSG and Leica Photogrammetric Suite, tested for urban DSM generation, are presented and described.\nThe orthorectification accuracy analyses were done using QuickBird and IKONOS data. The geometrical accuracies of the tested GCP based orthorectification methods have shown that a rigorous physical model gives the best horizontal accuracies and has proven to be stable, whereas RF based models tend to be instable.\nFor the digital surface model accuracy analyses stereo IKONOS data were mainly used. Two datasets were used to perform the tests. One study area is situated in Nairobi where a variety of building types are present, from high-rise buildings to small illegal shacks. The second study area is in Graz, which was mainly chosen because a very detailed reference surface model was available.\nThe five evaluation tests for the Nairobi test area have shown that all tested software packages created DSMs that performed well in at least one of the tests. They all have advantages and disadvantages. Height accuracy as well as clear building shape extraction is of great importance for the use of DSMs in information extraction for settlement analysis and mapping. The highlighted tests are representing these criteria best. Judging them it can be concluded that overall the PCI and RSG software performed best. They should be favoured for DSM extraction. \nThe quantitative accuracy assessment for the test area of Graz has shown that the best vertical estimation results were achieved with the software packages of LPS and PCI followed by RSG. The vertical MAE for built-up and impervious areas was 2.20m for PCI, 2.28m for LPS and 2.55m for RSG respectively. The RMSE was 3.05m, 2.96m and 3.25m respectively. However, the qualitative, visual DSM evaluation has not confirmed the quantitative results. LPS with the best quantitative accuracy created fuzzy building outlines and contains low details in areas with smaller objects. PCI and RSG both produced DSMs with clear building outlines. They both are able to extract high details in areas with small buildings. Besides achieving the largest error in the quantitative analysis due to an erroneous mountain in the North of the test area ENVI also had problems in extracting correct multi-storey buildings outlines in the denser city area. It achieved good visual results with high details for rather small buildings.\nTwo problems have to be addressed to extract building heights from stereo satellite data. First, the object height information has to be derived from the generated DSM. Two methodologies were presented to derive the object height layer: an indirect and a direct methodology. Second, the building outlines have to be delineated and extracted. A possible approach was proposed based on watershed segmentation.\nThe first results of the two tested methodologies are promising. A mean absolute error of 4.53m and 5.97m respectively was achieved when comparing them with reference building heights. Medium-height buildings were estimated well with an approximate error of one floor. Tall buildings are estimated with larger errors of two or more floors. \nAdditionally, a building outline extraction approach based on watershed segmentation and preliminary results were presented. The methodology successfully detected most buildings. However, problems occur where buildings have complicated outlines.

Unmanned Aerial Vehicle (UAV) data acquisition is more flexible compared with the more complex traditional airborne data acquisition. This advantage puts UAV platforms in a position as an alternative acquisition method in many applications including Large Scale Topographical Mapping (LSTM). LSTM, i.e. larger or equal than 1:10.000 map scale, is one of a number of prominent priority tasks to be solved in an accelerated way especially in third world developing countries such as Indonesia. <br><br> As one component of fundamental geospatial data sets, large scale topographical maps are mandatory in order to enable detailed spatial planning. However, the accuracy of the products derived from the UAV data are normally not sufficient for LSTM as it needs robust georeferencing, which requires additional costly efforts such as the incorporation of sophisticated GPS Inertial Navigation System (INS) or Inertial Measurement Unit (IMU) on the platform and/or Ground Control Point (GCP) data on the ground. To reduce the costs and the weight on the UAV alternative solutions have to be found. <br><br> This paper outlines a direct georeferencing method of UAV data by providing image orientation parameters derived from simple building structures and presents results of an investigation on the achievable results in a LSTM application. In this case, the image orientation determination has been performed through sequential images without any input from INS/IMU equipment. The simple building structures play a significant role in such a way that geometrical characteristics have been considered. Some instances are the orthogonality of the building’s wall/rooftop and the local knowledge of the building orientation in the field. In addition, we want to include the Structure from Motion (SfM) approach in order to reduce the number of required GCPs especially for the absolute orientation purpose. <br><br> The SfM technique applied to the UAV data and simple building structures additionally presents an effective tool for the LSTM application at low cost. Our results show that image orientation calculations from building structure essentially improve the accuracy of direct georeferencing procedure adjusted also by the GCPs. To gain three dimensional (3D) point clouds in local coordinate system, an extraction procedure has been performed by using Agisoft Photo Scan. Subsequently, a Digital Surface Model (DSM) generated from the acquired data is the main output for LSTM that has to be assessed using standard field and conventional mapping workflows. For an appraisal, our DSM is compared directly with a similar DSM obtained by conventional airborne data acquisition using Leica RCD-30 metric camera as well as Trimble Phase One (P65+) camera. The comparison reveals that our approach can achieve meter level accuracy both in planimetric and vertical dimensions.

In this study, Digital Elevation Models (DEMs) were generated from ASTER stereo images for a study area enclosing the city of Ankara and covering an area of 3600 km/sup 2/. Majority of the area was covered by large scale orthophotos, which were used as the main source to collect the Ground Control Points (GCPs). For those areas that were not covered by the orthophotos the GCPs were collected through a differential GPS. The bundle adjustment process and the generation of DEMs were carried out using PCI Geomatica image analysis software. In order to find the best GCP combination, DEMs were generated using different number of GCPs (8, 16, 24 and 32). DEM accuracies were assessed based on different parameters including the check points, slopes and land cover types. The accuracies of the resulting DEMs revealed that 16 GCPs were good compromise to produce the most accurate DEM. It was found that the post processing and blunder removal were crucial stages that affect the overall DEM accuracy up to 38%. There was a strong linear relationship between the accuracy of DEMs and the terrain slopes. For urban and forested areas, the accuracy in elevation was found to be 8.03 m and 12.69 m, respectively. The overall accuracy computed for 2,171,664 points produced 10.92 m elevation accuracy.

ABSTRACTPhotogrammetry based on high-resolution satellite image can acquire geospatial information within a large area rapidly and timely, but its geopositioning accuracy is highly dependent on ground control points. Under the background of global mapping, a public digital elevation model (DEM) assisted Chinese satellite image geopositioning scheme was proposed to realize satellite photogrammetry without ground control points. To make full use of public DEM advantages of consistent and high accuracy, public DEM was regarded as reference data and matched with the DEM extracted from image, and then the determined transformation parameters were applied to correct direct georeferencing results in object space. A fast least Z-difference method combined with least trimmed squares estimator was proposed to achieve DEM matching, which can not only automatically select corresponding point determination model, but also self-adaptively detect and eliminate difference between DEMs. Multi-groups of comparative experiments using Mapping Satellite-1 and ZiYuan-3 surveying satellite images were designed. Experimental results show that the DEM assisted geopositioning scheme exploits advantages of reference DEM, which can greatly improve the accuracy of photogrammetry without ground control points to a relatively high level. The geopositioning accuracy of image is largely determined by, but not confined to, the accuracy of reference DEM, but it is slightly affected by the resolution of reference DEM. If target DEM resolution is relatively high, the geopositioning accuracy of a single image assisted by Shuttle Radar Topography Mission (SRTM) DEM can satisfy the accuracy requirements of 1:50,000 scale mapping perfectly. This scheme also has good robustness and high computational efficiency.

Terrain survey with traditional photogrammetry is often difficult in western China, such as Qingzang tableland at an average height of 5000 m above sea level and the southwest China area with cloudy weather. To resolve western terrain mapping, the first Chinese single-pass airborne Interferometric Synthetic Aperture Radar (InSAR) system was successfully developed by the Institute of Electronics, Chinese Academy of Sciences (IECAS) in 2004. The main objective of this article is to examine and evaluate the performance of the airborne SAR system through interferometric processing and error analysis. First, the article describes how high-precision digital elevation models (DEMs) are derived from the airborne dual-antenna (single-pass) InSAR data. In order to improve the precision, the antenna eccentricity correction and parameter calibration with the least square method (LSM) are proposed. Based on the airborne dual-antenna InSAR bore-sight model, this article summarizes the primary factors that influence the accuracy of DEMs in data processing, and analyses the errors induced by these factors. Then, the global positioning system (GPS)/inertial measurement unit (IMU) data, acquired and stored by the position and orientation system (POS), is used for analysing the quantitative relationships among the platform height, baseline length, baseline angle, look angle and DEM error. The experimental data used are airborne dual-antenna X-band InSAR data, and the measured ground control points (GCPs) are used to validate the accuracy of the DEM. The evaluation results in terms of the standard deviation (SD) and the average mean error (AME) are derived by comparing the reconstructed InSAR DEM with the reference GCPs. The AMEs of the X-direction, the Y-direction and the height are up to 2.078, 9.149 and 1.763 m, respectively. The SDs of the X-direction, the Y-direction and the height are up to ±1.379, ±0.764 and ±1.086 m, respectively. These results agree with the previously calculated quantitative errors. The error value of the Y-direction seems too large, a possible result of system errors. In general, the airborne dual-antenna InSAR system initially meets the requirements of 1:50 000 terrain mapping in western China.

In this study, the geometric accuracy comparison of aerial photos and WorldView-2 satellite stereo image data is evaluated with the different number and the distribution of the ground control points (GCPs) on the basis of large scale map production. Also, the current situation of rivalry between airborne and satelliteborne imagery was mentioned. The geometric accuracy of Microsoft UltraCam X 45 cm ground sampling distance (GSD) aerial imagery and WorldView-2 data both with and without GCPs are also separately analyzed. The aerial photos without any GCP by only using global navigation satellite system (GNSS) and inertial measurement unit (IMU) data with tie points give an accuracy of ±1.17 m in planimetry and ±0.71 m in vertical that means nearly two times better accuracy than the rational polynomial coefficient (RPC) of stereo WorldView-2. Using one GCP affects the accuracies of aerial photos and WorldView-2 in different ways. While this situation distorts the aerial photo block, it corrects the shift effect of RPC in WorldView-2 and increases the accuracy. By using four or more GCPs, ½ pixel (∼0.23 m) accuracy in aerial photos and 1 pixel (∼0.50 m) accuracy in WorldView-2 can be achieved in horizontal. In vertical, aerial photos have 1 pixel (∼0.55 m) and WorldView-2 has 1.5 pixels (∼0.85 m) accuracy. These results show that Worldview-2 imagery can be used in the production of class I 1:5000 scale maps according to the ASPRS Accuracy Standards for Digital Geospatial Data in terms of geometric accuracy. It is concluded that the rivalry between aerial and satellite imagery will continue for some time in the future.

The purpose of this research was to analyze the quality and characteristics of four digital elevation models (DEMs) generated using in-track Korea Multi-Purpose Satellite (KOMPSAT)-3 stereo images. The sensor modeling methods were based on ground control points (GCPs), the initial rational polynomial coefficients (RPCs), relative adjustment, and the automatic bias-compensation method. The GCPs and check points (CPs) were extracted from the 0.25 m aerial orthoimage and the 5 m DEM provided by the National Geographic Information Institute (NGII). The DEMs had the same resolution as the reference DEM (5 m) and comparative analysis was carried out. The results indicate that when relative adjustment was applied alone (DEM 3), the percentage of matched points with a correlation of 0.8 or more was improved by at least 17% compared to the case where only initial RPCs were used (DEM 2). Although the absolute horizontal position error of DEM 3 could not be eliminated, the relative elevation error at the same position was reduced significantly. Therefore, if the relative positions of DEMs produced at different times can be corrected, they can be used for the detection of changes in altitude. When applying the automatic bias-compensation method (DEM 4) without GCPs, the percentage of matched points with a correlation of 0.8 or more was 70.1%. When GCPs were used (DEM 1), the value was 70.2%, i.e., almost identical to that of DEM 4. The mean difference in resolution among DEMs 1 and 4 was -1.8 ± 3.4 m (median, -1.0 m). The results show that DEMs of sufficient quality can be generated without GCPs. Furthermore, although discrepancies among the DEMs were noted in forest and shadow areas, it is possible to produce a 5~10 m resolution DEM by using additional image processing techniques, such as shadow removal.

Digital Terrain Models (DTMs) are used as basic data in many engineering projects. In addition to classical terrestrial techniques, space and satellite techniques and Unmanned Aerial Vehicles (UAVs) are coomonly used in the production of the DTMs. In the production of the DTM with the help of the UAVs, measurements can be made easily where people can access difficultly and large areas can be mapped in a short time. However, one of the most fundamental problems in the UAVs is to obtain the most accurate DTM by choosing the homogeneously spread ground control points (GCPs) number. In this study, the effect of flight altitude and the density of GCPs on position accuracy were investigated in produce of the DTM. For this purpose, 56 points were established at approximately 40 m intervals and images from 80, 100, 120 m flight altitude were taken in the test area. The rapid static Global Navigation Satellite Systems (GNSS) method was used to obtain the coordinates of the points with high accuracy. Then, the homogeneously spread 5, 10, 15 points were chosen as GCPs, respectively. The images were evaluated in Pix4d Mapper software with 9 different combinations and DTMs were produced. Outliers of the coordinates obtained from the models were detected by Bland-Altman Plot. In order to determine the geometric accuracy of the produced models, the coordinates of the test points obtained from the models and the results of rapid static GNSS measurements were compared with the statistical methods and the obtained results were interpreted.

ABSTRACTThe overfitting phenomenon and rational polynomial coefficients (RPCs) biases are two crucial issues that degrade the accuracy of geospatial products derived from high-resolution satellite images. The overfitting phenomenon is caused by both a large number of RPCs and strong correlations among them. The RPC biases arise from uncertainties in the global positioning system receivers and inertial measurement units. In this article, an innovative framework based on the genetic algorithm (GA) and the least squares (LS) algorithm, called GALS, is proposed to overcome these problems simultaneously. In this method, the GA is applied to select the optimum RPCs, while the LS algorithm is used to estimate the values of the optimally selected RPCs. The GALS method requires various sets of well-distributed ground control points (GCPs). To tackle the problem of GCP collection, we generated a large number of digital elevation model (DEM)-derived GCPs (DEMGCPs), using a global DEM (GDEM) and vendor-provided RPCs, refined by only one GCP. To evaluate the performance of this framework, four IRS-P5 data sets were used. The GALS is compared to two competing methods, L1-norm-regularized LS and ridge estimation by considering two scenarios using 50 GCPs and the DEMGCPs. The results demonstrate the superiority of GALS in both scenarios. Furthermore, GALS using DEMGCPs led to far more accurate and stable results when compared to GALS using GCPs. Compared to the vendor-provided RPCs, the results of the GALS using DEMGCPs also indicate a major improvement, single-pixel or subpixel accuracy with around 15 RPCs, and only 1 GCP, in both accuracy and reliability of georeferencing for all IRS-P5 data sets.

Orthophotos (or orthoimages if in digital form) have long been recognised as a supplement or alternative to standard maps. The increasing applications of orthoimages require efforts to ensure the accuracy of produced orthoimages. As digital photogrammetry technology has reached a stage of relative maturity and stability, the availability of high quality ground control points (GCPs) and digital elevation models (DEMs) becomes the central issue for successfully implementing an image orthorectification project. Concerns with the impacts of the quality of GCPs and DEMs on the quality of orthoimages inspire researchers to look for more reliable approaches to acquire high quality GCPs and DEMs for orthorectification. Light Detection and Ranging (LiDAR), an emerging technology, offers capability of capturing high density three dimensional points and generating high accuracy DEMs in a fast and cost-effective way. Nowadays, highly developed computer technologies enable rapid processing of huge volumes of LiDAR data. This leads to a great potential to use LiDAR data to get high quality GCPs and DEMs to improve the accuracy of orthoimages. This paper presents methods for utilizing LiDAR intensity images to collect high accuracy ground coordinates of GCPs and for utilizing LiDAR data to generate a high quality DEM for digital photogrammetry and orthorectification processes. A comparative analysis is also presented to assess the performance of proposed methods. The results demonstrated the feasibility of using LiDAR intensity image-based GCPs and the LiDAR-derived DEM to produce high quality orthoimages.

Using LIDAR-based DEM to orthorectify Ikonos panchromatic images

High-resolution Digital Surface Models (DSMs) from unmanned aerial vehicles (UAVs) imagery with accuracy better than 10 cm open new possibilities in geosciences and engineering. The accuracy of such DSMs depends on the number and distribution of ground control points (GCPs). Placing and measuring GCPs are often the most time-consuming on-site tasks in a UAV project. Safety or accessibility concerns may impede their proper placement, so either costlier techniques must be used, or a less accurate DSM is obtained. Photogrammetric blocks flown by drones with on-board receivers capable of RTK (real-time kinematic) positioning do not need GCPs, as camera stations at exposure time can be determined with cm-level accuracy, and used to georeference the block and control its deformations. This paper presents an experimental investigation on the repeatability of DSM generation from several blocks acquired with a RTK-enabled drone, where differential corrections were sent from a local master station or a network of Continuously Operating Reference Stations (CORS). Four different flights for each RTK mode were executed over a test field, according to the same flight plan. DSM generation was performed with three block control configurations: GCP only, camera stations only, and with camera stations and one GCP. The results show that irrespective of the RTK mode, the first and third configurations provide the best DSM inner consistency. The average range of the elevation discrepancies among the DSMs in such cases is about 6 cm (2.5 GSD, ground sampling density) for a 10-cm resolution DSM. Using camera stations only, the average range is almost twice as large (4.7 GSD). The average DSM accuracy, which was verified on checkpoints, turned out to be about 2.1 GSD with the first and third configurations, and 3.7 GSD with camera stations only.

Abstract. Unmanned Aerial Vehicle (UAV) data acquisition is more flexible compared with the more complex traditional airborne data acquisition. This advantage puts UAV platforms in a position as an alternative acquisition method in many applications including Large Scale Topographical Mapping (LSTM). LSTM, i.e. larger or equal than 1:10.000 map scale, is one of a number of prominent priority tasks to be solved in an accelerated way especially in third world developing countries such as Indonesia. As one component of fundamental geospatial data sets, large scale topographical maps are mandatory in order to enable detailed spatial planning. However, the accuracy of the products derived from the UAV data are normally not sufficient for LSTM as it needs robust georeferencing, which requires additional costly efforts such as the incorporation of sophisticated GPS Inertial Navigation System (INS) or Inertial Measurement Unit (IMU) on the platform and/or Ground Control Point (GCP) data on the ground. To reduce the costs and the weight on the UAV alternative solutions have to be found. This paper outlines a direct georeferencing method of UAV data by providing image orientation parameters derived from simple building structures and presents results of an investigation on the achievable results in a LSTM application. In this case, the image orientation determination has been performed through sequential images without any input from INS/IMU equipment. The simple building structures play a significant role in such a way that geometrical characteristics have been considered. Some instances are the orthogonality of the building’s wall/rooftop and the local knowledge of the building orientation in the field. In addition, we want to include the Structure from Motion (SfM) approach in order to reduce the number of required GCPs especially for the absolute orientation purpose. The SfM technique applied to the UAV data and simple building structures additionally presents an effective tool for the LSTM application at low cost. Our results show that image orientation calculations from building structure essentially improve the accuracy of direct georeferencing procedure adjusted also by the GCPs. To gain three dimensional (3D) point clouds in local coordinate system, an extraction procedure has been performed by using Agisoft Photo Scan. Subsequently, a Digital Surface Model (DSM) generated from the acquired data is the main output for LSTM that has to be assessed using standard field and conventional mapping workflows. For an appraisal, our DSM is compared directly with a similar DSM obtained by conventional airborne data acquisition using Leica RCD-30 metric camera as well as Trimble Phase One (P65+) camera. The comparison reveals that our approach can achieve meter level accuracy both in planimetric and vertical dimensions.