Airborne imaging for heritage documentation using the Fotokite tethered flying camera

Since the beginning of aerial photography, researchers used all kinds of devices (from pigeons, kites, poles, and balloons to rockets) to take still cameras aloft and remotely gather aerial imagery. To date, many of these unmanned devices are still used for what has been referred to as Low-Altitude Aerial Photography or LAAP. In addition to these more traditional camera platforms, radio-controlled (multi-)copter platforms have recently added a new aspect to LAAP. Although model airplanes have been around for several decades, the decreasing cost, increasing functionality and stability of ready-to-fly multi-copter systems has proliferated their use among non-hobbyists. As such, they became a very popular tool for aerial imaging. The overwhelming amount of currently available brands and types (heli-, dual-, tri-, quad-, hexa-, octo-, dodeca-, deca-hexa and deca-octocopters), together with the wide variety of navigation options (e.g. altitude and position hold, waypoint flight) and camera mounts indicate that these platforms are here to stay for some time. Given the multitude of still camera types and the image quality they are currently capable of, endless combinations of low- and high-cost LAAP solutions are available. In addition, LAAP allows for the exploitation of new imaging techniques, as it is often only a matter of lifting the appropriate device (e.g. video cameras, thermal frame imagers, hyperspectral line sensors). Archaeologists were among the first to adopt this technology, as it provided them with a means to easily acquire essential data from a unique point of view, whether for simple illustration purposes of standing historic structures or to compute three-dimensional (3D) models and orthophotographs from excavation areas. However, even very cheap multi-copters models require certain skills to pilot them safely. Additionally, malfunction or overconfidence might lift these devices to altitudes where they can interfere with manned aircrafts. As such, the safe operation of these devices is still an issue, certainly when flying on locations which can be crowded (such as students on excavations or tourists walking around historic places). As the future of UAS regulation remains unclear, this talk presents an alternative approach to aerial imaging: the Fotokite. Developed at the ETH Zürich, the Fotokite is a tethered flying camera that is essentially a multi-copter connected to the ground with a taut tether to achieve controlled flight. Crucially, it relies solely on onboard IMU (Inertial Measurement Unit) measurements to fly, launches in seconds, and is classified as not a UAS (Unmanned Aerial System), e.g. in the latest FAA (Federal Aviation Administration) UAS proposal. As a result it may be used for imaging cultural heritage in a variety of environments and settings with minimal training by non-experienced pilots. Furthermore, it is subject to less extensive certification, regulation and import/export restrictions, making it a viable solution for use at a greater range of sites than traditional methods. Unlike a balloon or a kite it is not subject to particular weather conditions and, thanks to active stabilization, is capable of a variety of intelligent flight modes. Finally, it is compact and lightweight, making it easy to transport and deploy, and its lack of reliance on GNSS (Global Navigation Satellite System) makes it possible to use in urban, overbuilt areas. After outlining its operating principles, the talk will present some archaeological case studies in which the Fotokite was used, hereby assessing its capabilities compared to the conventional UAS’s on the market

Positioning in time and space: cost-effective exterior orientation for airborne archaeological photographs

Since manned, airborne aerial reconnaissance for archaeological purposes is often characterised by more-or-less random photographing of archaeological features on the Earth, the exact position and orientation of the camera during image acquisition becomes very important in an effective inventorying and interpretation workflow of these aerial photographs. Although the positioning is generally achieved by simultaneously logging the flight path or directly recording the camera's position with a GNSS receiver, this approach does not allow to record the necessary roll, pitch and yaw angles of the camera. The latter are essential elements for the complete exterior orientation of the camera, which allows – together with the inner orientation of the camera – to accurately define the portion of the Earth recorded in the photograph. This paper proposes a cost-effective, accurate and precise GNSS/IMU solution (image position: 2.5 m and orientation: 2°, both at 1σ) to record all essential exterior orientation parameters for the direct georeferencing of the images. After the introduction of the utilised hardware, this paper presents the developed software that allows recording and estimating these parameters. Furthermore, this direct georeferencing information can be embedded into the image's metadata. Subsequently, the first results of the estimation of the mounting calibration (i.e. the misalignment between the camera and GNSS/IMU coordinate frame) are provided. Furthermore, a comparison with a dedicated commercial photographic GNSS/IMU solution will prove the superiority of the introduced solution. Finally, an outlook on future tests and improvements finalises this article

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

Airborne laser bathymetry for documentation of submerged archaeological sites in shallow water

Knowledge of underwater topography is essential to the understanding of the organisation and distribution of archaeological sites along and in water bodies. Special attention has to be paid to intertidal and inshore zones where, due to sea-level rise, coastlines have changed and many former coastal sites are now submerged in shallow water. Mapping the detailed inshore topography is therefore important to reconstruct former coastlines, identify sunken archaeological structures and locate potential former harbour sites. However, until recently archaeology has lacked suitable methods to provide the required topographical data of shallow underwater bodies. Our research shows that airborne topo-bathymetric laser scanner systems are able to measure surfaces above and below the water table over large areas in high detail using very short and narrow green laser pulses, even revealing sunken archaeological structures in shallow water. Using an airborne laser scanner operating at a wavelength in the green visible spectrum (532 nm) two case study areas in different environmental settings (Kolone, Croatia, with clear sea water; Lake Keutschach, Austria, with turbid water) were scanned. In both cases, a digital model of the underwater topography with a planimetric resolution of a few decimeters was measured. While in the clear waters of Kolone penetration depth was up to 11 meters, turbid Lake Keutschach allowed only to document the upper 1.6 meters of its underwater topography. Our results demonstrate the potential of this technique to map submerged archaeological structures over large areas in high detail providing the possibility for systematic, large scale archaeological investigation of this environment

Classification of airborne laser scanning point clouds based on binomial logistic regression analysis

This article presents a newly developed procedure for the classification of airborne laser scanning (ALS) point clouds, based on binomial logistic regression analysis. By using a feature space containing a large number of adaptable geometrical parameters, this new procedure can be applied to point clouds covering different types of topography and variable point densities. Besides, the procedure can be adapted to different user requirements. A binomial logistic model is estimated for all a priori defined classes, using a training set of manually classified points. For each point, a value is calculated defining the probability that this point belongs to a certain class. The class with the highest probability will be used for the final point classification. Besides, the use of statistical methods enables a thorough model evaluation by the implementation of well-founded inference criteria. If necessary, the interpretation of these inference analyses also enables the possible definition of more sub-classes. The use of a large number of geometrical parameters is an important advantage of this procedure in comparison with current classification algorithms. It allows more user modifications for the large variety of types of ALS point clouds, while still achieving comparable classification results. It is indeed possible to evaluate parameters as degrees of freedom and remove or add parameters as a function of the type of study area. The performance of this procedure is successfully demonstrated by classifying two different ALS point sets from an urban and a rural area. Moreover, the potential of the proposed classification procedure is explored for terrestrial data