AIRBORNE LASER SCANNING STRIP ADJUSTMENT AND AUTOMATION OF TIE SURFACE MEASUREMENT

Airborne laser scanning of the earth surface and other objects on top it yields measurements of unstructured point clouds in a strip wise manner. Often multiple length strips with a small overlap are observed, sometimes augmented by a few cross strips for validation purposes. Due to inaccurate calibration of the entire measurement system and due to the limited accuracy of direct geo-referencing (i.e., the exterior orientation determination) with GPS and IMU, including systematic errors, adjacent strips may have discrepancies in their overlap. For removing these discrepancies strip adjustment algorithms require quantification on these offsets at various locations within the overlapping zones. Different methods of strip adjustment are reviewed, followed by the presentation of a general method for determining the discrepancies automatically. This method the core of the paper is based on segmenting the point cloud in the overlap. In the examples, mean offsets between neighboring strips in the order of a few centimeters are reconstructed. The offsets also show substantial variation along the strip. The method developed for discrepancy determination can be applied to height or full 3D strip adjustment and for approaches using the original measurements, the coordinates of the measured points, or only the offsets between surfaces. An example of strip adjustment using discrepancy observations with the method presented and a discussion of the results conclude this paper

3D Building Change Detection on the basis of Airborne Laser Scanning Data

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.The paper presents the possibility to use airborne laser scanning (ALS) for building change detection. For the analysis data was gathered during two campaigns: 2003 and 2011. As research area we chose a test site covering 0.72 km², representing different kind of land cover classes: water, buildings, vegetation, bare-ground, and other artificial and temporary objects. The extraction and classification of the objects was performed in 3D in order to preserve all information contained in the data, i.e., the original point cloud. The first, results of our study present the advantages and new possibilities in buildings change detection in 3D, which were not possible in the analysis based on satellite and aerial images only

Investigation on the automatic geo-referencing of archaeological UAV photographs by correlation with pre-existing ortho-photos

We present a method for the automatic geo-referencing of archaeological photographs captured aboard unmanned aerial vehicles (UAVs), termed UPs. We do so by help of pre-existing ortho-photo maps (OPMs) and digital surface models (DSMs). Typically, these pre-existing data sets are based on data that were captured at a widely different point in time. This renders the detection (and hence the matching) of homologous feature points in the UPs and OPMs infeasible mainly due to temporal variations of vegetation and illumination. Facing this difficulty, we opt for the normalized cross correlation coefficient of perspectively transformed image patches as the measure of image similarity. Applying a threshold to this measure, we detect candidates for homologous image points, resulting in a distinctive, but computationally intensive method. In order to lower computation times, we reduce the dimensionality and extents of the search space by making use of a priori knowledge of the data sets. By assigning terrain heights interpolated in the DSM to the image points found in the OPM, we generate control points. We introduce respective observations into a bundle block, from which gross errors i.e. false matches are eliminated during its robust adjustment. A test of our approach on a UAV image data set demonstrates its potential and raises hope to successfully process large image archives

Accuracy analysis of direct georeferenced UAV images utilising low-cost navigation sensors

Unmanned aerial vehicles (UAVs), also known as unmanned airborne systems (UAS) or remotely piloted airborne systems (RPAS), are an established platform for close range airborne photogrammetry. Compared to manned platforms, the acquisition of local remote sensing data by UAVs is a convenient and very flexible option. For the application in photogrammetry UAVs are typically equipped with an autopilot and a lightweight digital camera. The autopilot includes several navigation sensors, which might allow an automated waypoint flight and offer a systematic data acquisition of the object resp. scene of interest. Assuming a sufficient overlap between the captured images, the position (3 coordinates: x, y, z) and the orientation (3 angles: roll, pitch, yaw) of the images can be estimated within a bundle block adjustment. Subsequently, coordinates of observed points that appear in at least two images, can be determined by measuring their image coordinates or a dense surface model can be generated from all acquired images by automated image matching. For the bundle block adjustment approximate values of the position and the orientation of the images are needed. To gather this information, several methods exist. We introduce in this contribution one of them: the direct georeferencing of images by using the navigation sensors (mainly GNSS and INS) of a low-cost on-board autopilot. Beside automated flights, the autopilot offers the possibility to record the position and the orientation of the platform during the flight. These values don’t correspond directly to those of the images. To compute the position and the orientation of the images two requirements must be fulfilled. First the misalignment angles and the positional differences between the camera and the autopilot must be determined (mounting calibration). Second the synchronization between the camera and the autopilot has to be established. Due to the limited accuracy of the navigation sensors, a small number of ground control points should be used to improve the estimated values, especially to decrease the amount of systematic errors. For the bundle block adjustment the calibration of the camera and their temporal stability must be determined additionally. This contribution presents next to the theory a practical study on the accuracy analysis of direct georeferenced UAV imagery by low-cost navigation sensors. The analysis was carried out within the research project ARAP (automated (ortho)rectification of archaeological aerial photographs). The utilized UAS consists of the airplane “MAJA”, manufactured by “Bormatec” (length: 1.2 m, wingspan: 2.2 m) equipped with the autopilot “ArduPilot Mega 2.5”. For image acquisition the camera “Ricoh GR Digital IV” is utilised. The autopilot includes a GNSS receiver capable of DGPS (EGNOS), an inertial measurement system (INS), a barometer, and a magnetometer. In the study the achieved accuracies for the estimated position and orientation of the images are presented. The paper concludes with a summary of the remaining error sources and their possible corrections by applying further improvements on the utilised equipment and the direct georeferencing process

Automated archiving of archaeological aerial images

The main purpose of any aerial photo archive is to allow quick access to images based on content and location. Therefore, next to a description of technical parameters and depicted content, georeferencing of every image is of vital importance. This can be done either by identifying the main photographed object (georeferencing of the image content) or by mapping the center point and/or the outline of the image footprint. The paper proposes a new image archiving workflow. The new pipeline is based on the parameters that are logged by a commercial, but cost-effective GNSS/IMU solution and processed with in-house-developed software. Together, these components allow one to automatically geolocate and rectify the (oblique) aerial images (by a simple planar rectification using the exterior orientation parameters) and to retrieve their footprints with reasonable accuracy, which is automatically stored as a vector file. The data of three test flights were used to determine the accuracy of the device, which turned out to be better than 1° for roll and pitch (mean between 0.0 and 0.21 with a standard deviation of 0.17–0.46) and better than 2.5° for yaw angles (mean between 0.0 and −0.14 with a standard deviation of 0.58–0.94). This turned out to be sufficient to enable a fast and almost automatic GIS-based archiving of all of the imagery

LEAST SQUARES MATCHING FOR COMPARISON OF DIGITAL TERRAIN MODELS AND ITS APPLICATION POTENTIAL FOR THE BRAZILIAN MODELS AND THE SRTM MODEL

Digital Terrain Models are being used for planning and hydrological applications, but also for visualization and many other tasks. For all applications, it is necessary to know the model quality, because it has an impact on the quality of the decisions that are drawn from the terrain model applications. In this paper we present a method that is suitable for comparing two terrain models to each other. Vertical, but also horizontal displacement of terrain features can be found automatically, which are systematic errors and are in the main focus of this paper. However, random errors can be quantified, too. This method allows establishing a vector field of differences between two models, measuring the deviation from one to the other. These deviations are a measure of quality of one model against the other. Emphasis will be put on comparing terrain model from NASAs Shuttle Radar Topographic Mission to terrain models of known quality in Brazil

Zvonko Biljecki, doktor tehničkih znanosti

U radu je dan životopis i prikaz doktorskog rada Zvonka Biljeckog, doktora tehničkih znanosti