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Airborne remote sensing with optical sensor systems is an essential tool for a variety of environmental monitoring applications. Depending on the size of the area to be monitored, either unmanned (UAVs) or manned aircraft are more suitable. For survey areas starting at several square kilometers, piloted aircraft remain the preferred carrier platform. However, a specific class of manned aircraft is often not considered: the gyrocopter-type ultralight aircraft. These aircraft are less expensive to operate than conventional fixed wings. Additionally, they are highly maneuverable, offer a high payload and a long endurance, and thus perfectly fill the niche between UAVs and conventional aircraft. Therefore, the authors have developed a modular and easy-to-use sensor carrier system, the FlugKit, to temporarily convert an AutoGyro MTOsport gyrocopter into a full-fledged aerial remote sensing platform mainly for vegetation monitoring. Accordingly, various suitable optical sensor systems in the visible (VIS), near-infrared (NIR), and longwave infrared (LWIR) were explicitly developed for this carrier system. This report provides a deeper insight into the individual components of this remote sensing solution based on a gyrocopter as well as application scenarios already carried out with the system.

Object detection in aerial images is an important task in environmental, economic, and infrastructure-related tasks. One of the most prominent applications is the detection of vehicles, for which deep learning approaches are increasingly used. A major challenge in such approaches is the limited amount of data that arises, for example, when more specialized and rarer vehicles such as agricultural machinery or construction vehicles are to be detected. This lack of data contrasts with the enormous data hunger of deep learning methods in general and object recognition in particular. In this article, we address this issue in the context of the detection of road vehicles in aerial images. To overcome the lack of annotated data, we propose a generative approach that generates top-down images by overlaying artificial vehicles created from 2D CAD drawings on artificial or real backgrounds. Our experiments with a modified RetinaNet object detection network show that adding these images to small real-world datasets significantly improves detection performance. In cases of very limited or even no real-world images, we observe an improvement in average precision of up to 0.70 points. We address the remaining performance gap to real-world datasets by analyzing the effect of the image composition of background and objects and give insights into the importance of background., Comment: 14 pages, 13 figures, 4 tables

Remote sensing systems based on unmanned aerial vehicles (UAVs) are well suited for airborne monitoring of small to medium-sized farmland in agricultural applications. An imaging system is often used in the form of a multispectral multi-camera system to derive well-established vegetation indices (VIs) efficiently. This study investigates the potential of such a multi-camera system with a novel approach to extend spectral sensitivity from visible-to-near-infrared (VNIR) to short-wave infrared (SWIR) (400–1700 nm) for estimating forage mass from an aerial carrier platform. The system test was performed in a grassland fertilizer trial in Germany near Cologne in late July 2019. Within 37 min, a spectral response in four different wavelength bands in the NIR and SWIR range was acquired during two consecutive flights. Spectral image data were calibrated to reflectance using two different methods. The resulting reflectance data sets were processed to orthomosaics for each wavelength band. From these orthomosaics for both calibration methods, the four-band NIR/SWIR GnyLi VI and the two-band NIR/SWIR Normalized Ratio Index (NRI), were calculated. During both UAV flights, spectral ground truth data were recorded with a spectroradiometer on 12 plots in total for validation of camera-based spectral data. The camera and spectroradiometer data sets were directly compared in resulting reflectance and further analyzed with simple linear regression (SLR) models to predict dry matter (DM) yield. In the camera-based SLRs, the NRI performed best with $$R^2$$ R 2 of 0.73 and 0.75 (RMSE: 0.18 and 0.17) before the GnyLi with $$R^{2}$$ R 2 of 0.71 and 0.73 (RMSE: 0.19 and 0.18). These results clearly indicate the potential of the camera system for applications in forage mass monitoring.

Detecting objects in aerial images is an important task in different environmental and infrastructure-related applications. Deep learning object detectors like RetinaNet offer decent detection performance; however, they require a large amount of annotated training data. It is well known that the collection of annotated data is a time consuming and tedious task, which often cannot be performed sufficiently well for remote sensing tasks since the required data must cover a wide variety of scenes and objects. In this paper, we analyze the performance of such a network given a limited amount of training data and address the research question of whether artificially generated training data can be used to overcome the challenge of real-world data sets with a small amount of training data. For our experiments, we use the ISPRS 2D Semantic Labeling Contest Potsdam data set for vehicle detection, where we derive object-bounding boxes of vehicles suitable for our task. We generate artificial data based on vehicle blueprints and show that networks trained only on generated data may have a lower performance, but are still able to detect most of the vehicles found in the real data set. Moreover, we show that adding generated data to real-world data sets with a limited amount of training data, the performance can be increased significantly, and in some cases, almost reach baseline performance levels.

In order to enable the development of powerful machine learning methods for remote sensing-based Earth observation tasks, benchmarks are needed to evaluate the methods and compare them to other methods comprehensively. We present ArtifiVe-Potsdam, a freely available dataset that is targeting vehicle detection in aerial imagery. In particular, the benchmark focuses on enriching real datasets with artificial data and quantifying the added value. The dataset aims to stimulate research on the efficient and cost-effective creation and enrichment of datasets for remote sensing since datasets with a limited number of labels are common and the collection of data and labels is time-consuming and expensive.

Up-to-date information about vegetation types and hydromorphological structures and features are essential for the management of waterways. They are e.g. used for the monitoring and reporting of ri...

In this paper the development of a lightweight and highly modularized airborne sensor platform for remote sensing applications utilizing a gyrocopter as a carrier platform is described. The current sensor configuration consists of a high resolution DSLR camera for VIS-RGB recordings. As a second sensor modality, a snapshot hyperspectral camera was integrated in the aircraft. Moreover a custom-developed thermal imaging system composed of a VIS-PAN camera and a LWIR-camera is used for aerial recordings in the thermal infrared range. Furthermore another custom-developed highly flexible imaging system for high resolution multispectral image acquisition with up to six spectral bands in the VIS-NIR range is presented. The performance of the overall system was tested during several flights with all sensor modalities and the precalculated demands with respect to spatial resolution and reliability were validated. The collected data sets were georeferenced, georectified, orthorectified and then stitched to mosaics.

This study presents the results from a combined aerial survey performed with a hexacopter and a gyrocopter over a part of the Elbe estuary near Hamburg, Germany. The survey was conducted by the Federal Institute of Hydrology, Germany, and the Fraunhofer Application Center for Multimodal and Airborne Sensors as well as by a contracted engineering company with the aim to acquire spatial thermal infrared (TIR) data of the Hahnofer Nebenelbe, a branch of the Elbe estuary. Additionally, RGB and NIR data was captured to facilitate the identification of water surfaces and intertidal mudflats. The temperature distribution of the Elbe estuary affects all biological processes and in consequence the oxygen content, which is a key parameter in water quality. The oxygen levels vary in space between the main fairway and side channels. So far, only point measurements are available for monitoring and calibration/validation of water quality models. To better represent this highly dynamic system with a high spatial and temporal variability, tidal streams, heating and cooling, diffusion and mixing processes, spatially distributed data from several points of time within the tidal cycle are necessary. The data acquisition took place during two tidal cycles over two subsequent days in the summer of 2015. While the piloted gyrocopter covered the whole Hahnofer Nebenelbe seven times, the unmanned hexacopter covered a smaller section of the branch and tidal mudflats with a higher spatial and temporal resolution (16 coverages of the subarea). The gyrocopter data was acquired with a thermal imaging system and processed and georeferenced using the structure from motion algorithm with GPS information from the gyrocopter and optional ground control points. The hexacopter data was referenced based on ground control points and the GPS and position information of the acquisition system. Both datasets from the gyrocopter and the hexacopter are corrected for the effects of the atmosphere and emissivity of the water surface and compared to in situ measurements, taken during the data acquisition. Of particular interest is the effect of the observation angle on the brightness temperature acquired by the wide angle lenses on the platforms, which is up to 40° at the margins of the imagery. Here, both datasets show deviating temperatures, which are probably not due to actual temperature differences. We will discuss the position accuracy achieved over the water areas, the adaptation of atmospheric and emissivity correction to the observation angle and subsequent improvement of the temperature data. With two datasets of the same research area at different resolutions we will investigate the effects of the acquisition platforms, acquisition system and resolutions on the accuracy of the remotely sensed temperatures as well as their ability to represent temperature patterns of tidal currents and mixing processes.

The FIVIS simulator system addresses the classical visual and acoustical cues as well as vestibular and further physiological cues. Sensory feedback from skin, muscles, and joints are integrated within this virtual reality visualization environment. By doing this it allows for simulating otherwise dangerous traffic situations in a controlled laboratory environment. The system has been successfully applied for road safety education applications of school children. In further research studies it is applied to perform multimedia perception experiments. It has been shown, that visual cues dominate by far the perception of visual depth in the majority of applications but the quality of depth perception might depend on the availability of other sensory information. This however, needs to be investigated in more detail in the future.

During laser osteotomy surgery, plasma arises at the place of ablation. It was the aim of this study to explore whether a spectroscopic analysis of this plasma would allow identification of the type of tissue that was affected by the laser. In an experimental setup (Rofin SCx10, CO(2) Slab Laser, wavelength 10.6 μm, pulse duration 80 μs, pulse repetition rate 200 Hz, max. output in cw-mode 100 W), the plasma spectra evoked by a pulsed laser, cutting 1-day postmortem pig and cow bones, were recorded. Spectra were compared to the reference spectrum of bone via correlation analysis. Our measurements show a clear differentiation between the plasma spectra when cutting either a bone or a soft tissue. The spectral changes could be detected from one to the next spectrum within 200 ms. Continuous surveillance of plasma spectra allows us to differentiate whether bone or soft tissue is hit by the last laser pulse. With this information, it may be possible to stop the laser when cutting undesired soft tissue and to design an automatic control of the ablation process.

For medical applications, erbium lasers are usually equipped with articulated mirror arms or special glass fibers. However, only with mirror arms is it so far possible to transmit high average powers or pulse energies in the region of 1 J to achieve suitable energy densities for fast tissue preparation. An alternative to the glass fiber systems mentioned above are liquid-core light guides. An extremely flexible liquid-core light guide was used to connect a dental Er:YAG laser system to an especially adapted dental laser applicator. The core liquid was continuously circulated during laser irradiation to transmit pulse energies up to 1.1 J. A modified laser handpiece was used for exemplary clinical treatment. The experimental setup with the highly flexible light guide was completed successfully, and its ease of handling for a dental surgeon was demonstrated in the clinical treatment of leukoplakia of the oral cheek mucosa. Complete ablation of the epithelium with the laser was performed. One year postoperatively, the patient remains disease-free. This article describes the technical realization of a liquid-core light guide system for medical applications. We report about the first successful clinical treatment of oral hyperkeratosis using this new light guide technology.

The objective of the FIVIS project is to develop a bicycle simulator which is able to simulate real life bicycle ride situations as a virtual scenario within an immersive environment. A sample test bicycle is mounted on a motion platform to enable a close to reality simulation of turns and balance situations. The visual field of the bike rider is enveloped within a multi-screen visualisation environment which provides visual data relative to the motion and activity of the test bicycle. That means the bike rider has to pedal and steer the bicycle as a usual bicycle, while the motion is recorded and processed to control the simulation. Furthermore, the platform is fed with real forces and accelerations that have been logged by a mobile data acquisition system during real bicycle test drives. Thus, using a feedback system makes the movements of the platform match to the virtual environment and the reaction of the driver (e.g. steering angle, step rate).

For almost all modern means of transportation (car, train, airplane) driving simulators exist that provide realistic models of complex traffic situations under defined laboratory conditions. For many years, these simulators have been successfully used for drivers’ training and education and have considerably contributed to the overall road safety. Unfortunately, there is no such advanced system for the bicycle, although the number of bike accidents has been increasing against the common trend during the last decade. Hence the objective of this project is to design a real bicycle simulator that is able to generate any desired traffic situation within an immersive visualization environment. For this purpose the bike is mounted onto a motion platform with six degrees of freedom that enables a close-to-reality simulation of external forces acting on the bike. This system is surrounded by three projection walls displaying a virtual scenario. A physical model is developed in order to compute the bike’s mechanical behavior that corresponds to the visualized traffic and the reaction of the driver. In order to validate the model an off-the-shelve mountain bike is equipped with a set of physical sensors (e.g. acceleration, steering angle, declination) to monitor the mechanics of the bike during real test drives. This data is also used to feed the motion platform with real measurements (e.g. to model a bumpy street). As the driver in our bike simulator experiences both controllable physical and visual stimuli, this system facilitates a range of completely new applications in the field of safety at work, in the area of neuropsychological research and in road safety education.

The objective of the FIVIS project is to develop a bicycle simulator which is able to simulate real life bicycle ride situations as a virtual scenario within an immersive environment. A sample test bicycle is mounted on a motion platform to enable a close to reality simulation of turns and balance situations. The visual field of the bike rider is enveloped within a multi-screen visualization environment which provides visual data relative to the motion and activity of the test bicycle. This implies the bike rider has to pedal and steer the bicycle as they would a traditional bicycle, while forward motion is recorded and processed to control the visualization. Furthermore, the platform is fed with real forces and accelerations that have been logged by a mobile data acquisition system during real bicycle test drives. Thus, using a feedback system makes the movements of the platform reflect the virtual environment and the reaction of the driver (e.g. steering angle, step rate).

The objective of this project is to develop a real bicycle simulator with the ability to represent real life traffic situations as a virtual scenario within an immersive environment. The bicycle will be fixed onto a motion platform to enable a close to reality simulation of turns and balance situations. The platform is fed with real forces and accelerations that have been logged by a mobile data acquisition system during real bicycle test drives. Thus, using a feedback system makes the movements of the platform match to the virtual environment and the reaction of the driver (e.g. steering angle, step rate).

3D models of the skin surface of patients are created by ultra-fast holography and automatic scan matching of synchronously recorded holograms. By recording with a pulsed laser and continuous-wave optical reconstruction of the holographic real image, motion artifacts are eliminated. Focal analysis of the real image yields a surface relief of the patient. To generate a complete 360 ‐ patient model, several synchronously recorded reliefs are registered by automatic scan matching. We find the transformation consisting of a rotation and a translation that minimizes a cost function containing the Euclidian distances between points pairs from two surface relief maps. A variant of the ICP (Iterative Closest Points) algorithm 2 is used to compute such a minimum. We propose a new fast approximation based on kDtrees for the problem of creating the closest point pairs on which the ICP algorithm spends most of its time.

A novel 3d reconstruction method for medical application has been applied for the examination and documentation of a 2000-year-old bog body. An ultra-fast pulsed holographic camera has been modified to allow imaging of the bog body from different views. Full-scale daylight copies of the master holograms give a detailed impressive three-dimensional view of the mummy and can be exhibited instead of the object. In combination with a rapid prototyping model (built by the Rapid Prototyping group of the Stiftung caesar , Bonn, Germany) derived from computer tomography (CT) data our results are an ideal basis for a future facial reconstruction.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A method to measure precisely the 2D portrait of patients undergoing maxillofacial surgery based on holography is presented. We record holograms of patients with a pulsed Nd:YLF laser system on high resolution photographic glass plates. These images contain the 3D spatial information which, due to the extremely short recording time, is not affected at all by involuntary movements. The reconstructed real image of the hologram is sliced into a series of 2D projections by means of a screen. A first approach to reconstruct the patient's 3D surface information from the captured data set is presented.

Hologram tomography is a two-step method for three-dimensional topometry of extended objects. The first step consists of the hologram recording with a single laser pulse of 35 ns duration and storage in a photosensitive material. In the second step the hologram is optically reconstructed and digitized, which leads to a set of two-dimensional projections at different axial positions. A maximization of a focus measure has to be performed to extract the surface position out of the projections. Unlike with well-established methods, where the comparison of sharpness values is done parallel to the optical axis, we propose an iterative solution to perform the maximization along the direction of image formation, which is evaluated for each surface point individually. This leads to a better reproducibility of the surface in the off-axis regions.

Novel methods of laser measurements and interventions in the framework of image-guided surgery are presented. The first innovation concerns the basis of error propagation studies in registration chains from CT via OR navigator to the surgical laser tool holder. Here, we propose a holographic ground truth. The second innovation concerns a laser-based inherent sound-guidance principle for burr hole depth measurement.

Since optical vibrational spectroscopy is exquisitely sensitive to the biochemical composition of the sample and variations therein, it is possible to monitor metabolic processes and biochemical states in tissue and cells. The chances of identifying spectral indicators of diseases are much more favourable when spectroscopic imaging methods are used. Such images permit a direct correlation between the spectroscopic information and the sample histopathology. The spectroscopic imaging methods, which are barely ten years old at this point in time, become now a valuable technique for biomedical applications. In this contribution we illustrate the capability and versatility of spectroscopic imaging on the example of identification and visualization of brain tumors.