Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Extending StructureNet to generate physically feasible 3D shapes
    ( 2021)
    Koch, Jannik
    ;
    Haraké, Laura
    ;
    Jung, Alisa
    ;
    Dachsbacher, Carsten
    StructureNet is a recently introduced n-ary graph network that generates 3D structures with awareness of geometric part relationships and promotes reasonable interactions between shape parts. However, depending on the inferred latent space, the generated objects may lack physical feasibility, since parts might be detached or not arranged in a load-bearing manner. We extend StructureNet's training method to optimize the physical feasibility of these shapes by adapting its loss function to measure the structural intactness. Two new changes are hereby introduced and applied on disjunctive shape parts: First, for the physical feasibility of linked parts, forces acting between them are determined. Considering static equilibrium, compression and friction, they are assembled in a constraint system as the Measure of Infeasibility. The required interfaces between these parts are identified using Constructive Solid Geometry. Secondly, we define a novel metric called Hover Penalty that detects and penalizes unconnected shape parts to improve the overall feasibility. The extended StructureNet is trained on PartNet's chair data set, using a bounding box representation for the geometry. We demonstrate first results that indicate a significant reduction of hovering shape parts and a promising correction of shapes that would be physically infeasible.
  • Publication
    High-pulse-energy actively Q-switched polarization-maintaining Tm3+-doped silica fiber laser
    ( 2020)
    Schneider, Julian
    ;
    Forster, Patrick
    ;
    Eichhorn, Marc
    ;
    Kieleck, Christelle
    A diode-pumped Q-switched thulium-doped fiber laser is reported providing 43 ns short pulses with pulse energies of 800 µJ at a wavelength of 2050 nm.
  • Publication
    Superpoints in RANSAC planes: A new approach for ground surface extraction exemplified on point classification and context-aware reconstruction
    ( 2020)
    Bulatov, Dimitri
    ;
    Stütz, Dominik
    ;
    Lucks, Lukas
    ;
    Weinmann, Martin
    In point clouds obtained from airborne data, the ground points have traditionally been identified as local minima of the altitude. Subsequently, the 2.5D digital terrain models have been computed by approximation of a smooth surfaces from the ground points. But how can we handle purely 3D surfaces of cultural heritage monuments covered by vegetation or Alpine overhangs, where trees are not necessarily growing in bottom-to-top direction? We suggest a new approach based on a combination of superpoints and RANSAC implemented as a filtering procedure, which allows efficient handling of large, challenging point clouds without necessity of training data. If training data is available, covariance-based features, point histogram features, and dataset-dependent features as well as combinations thereof are applied to classify points. Results achieved with a Random Forest classifier and non-local optimization using Markov Random Fields are analyzed for two challenging datasets: an airborne laser scan and a photogrammetrically reconstructed point cloud. As an application, surface reconstruction from the thus cleaned point sets is demonstrated.
  • Publication
    Strategic optimization of convolutional neural networks for hyperspectral land cover classification
    ( 2020)
    Bühler, C.
    ;
    Schenkel, F.
    ;
    Groß, W.
    ;
    Schaab, G.
    ;
    Middelmann, W.
    Hyperspectral data recorded by future earth observation satellites will have up to hundreds of narrow bands that cover a wide range of the electromagnetic spectrum. The spatial resolution (around 30 meters) of such data, however, can impede the integration of the spatial domain for a classification due to spectrally mixed pixels and blurred edges in the data. Hence, the ability of performing a meaningful classification only relying on spectral information is important. In this study, a model for the spectral classification of hyperspectral data is derived by strategically optimizing a convolutional neural network (1D-CNN). The model is pre-trained and optimized on imagery of different nuts, beans, peas and dried fruits recorded with the Cubert ButterflEye X2 sensor. Subsequently, airborne hyperspectral datasets (Greding, Indian Pines and Pavia University) are used to evaluate the CNN's capability of transfer learning. For that, the datasets are classified with the pre-trained weights and, for comparison, with the same model architecture but trained from scratch with random weights. The results show substantial differences in classification accuracies (from 71.8% to 99.8% overall accuracy) throughout the used datasets, mainly caused by variations in the number of training samples, the spectral separability of the classes as well as the existence of mixed pixels for one dataset. For the dataset that is classified least accurately, the greatest improvement with pre-training is achieved (difference of 3.3% in overall accuracy compared to the non-pre-trained model). For the dataset that is classified with the highest accuracy, no significant transfer learning was observed.
  • Publication
    Incorporating interferometric coherence into LULC classification of airborne PolSAR-images using fully convolutional networks
    ( 2020)
    Schmitz, Sylvia
    ;
    Weinmann, Martin
    ;
    Thiele, Antje
    Inspired by the application of state-of-the-art Fully Convolutional Networks (FCNs) for the semantic segmentation of high-resolution optical imagery, recent works transfer this methodology successfully to pixel-wise land use and land cover (LULC) classification of PolSAR data. So far, mainly single PolSAR images are included in the FCN-based classification processes. To further increase classification accuracy, this paper presents an approach for integrating interferometric coherence derived from co-registered image pairs into a FCN-based classification framework. A network based on an encoder-decoder structure with two separated encoder branches is presented for this task. It extracts features from polarimetric backscattering intensities on the one hand and interferometric coherence on the other hand. Based on a joint representation of the complementary features pixel-wise classification is performed. To overcome the scarcity of labelled SAR data for training and testing, annotations are generated automatically by fusing available LULC products. Experimental evaluation is performed on high-resolution airborne SAR data, captured over the German Wadden Sea. The results demonstrate that the proposed model produces smooth and accurate classification maps. A comparison with a single-branch FCN model indicates that the appropriate integration of interferometric coherence enables the improvement of classification performance.
  • Publication
    Automatic Generation of Training Data for Land Use and Land Cover Classification by Fusing Heterogeneous Data Sets
    ( 2020)
    Schmitz, Sylvia
    ;
    Weinmann, Martin
    ;
    Weidner, Uwe
    ;
    Hammer, Horst
    ;
    Thiele, Antje
    Nowadays, automatic classification of remote sensing data can efficiently produce maps of land use and land cover, which provide an essential source of information in the field of environmental sciences. Most state-of-the-art algorithms use supervised learning methods that require a large amount of annotated training data. In order to avoid time-consuming manual labelling, we propose a method for the automatic annotation of remote sensing data that relies on available land use and land cover information. Using the example of automatic labelling of SAR data, we show how the Dempster-Shafer evidence theory can be used to fuse information from different land use and land cover products into one training data set. Our results confirm that the combination of information from OpenStreetMap, CORINE Land Cover 2018, Global Surface Water and the SAR data itself leads to reliable class assignments, and that this combination outperforms each considered single land use and land cover product.
  • Publication
    High-pulse-energy Q-switched Ho3+:YAG laser
    ( 2020)
    Büker, H.
    ;
    Braesicke, P.
    ;
    Kieleck, Christelle
    ;
    Eichhorn, Marc
    We report on a high-energy Ho3+:YAG laser with a maximum average power of 20.1W at a central wavelength of 2.09µm and M2 < 1.5. In Q-switched operation a maximum pulse energy of 15mJ at 700Hz repetition rate was achieved.
  • Publication
    Software-defined Flow Reservation: Configuring IEEE 802.1Q Time-Sensitive Networks by the Use of Software-Defined Networking
    ( 2019)
    Gerhard, Tim
    ;
    Kobzan, Thomas
    ;
    Blöcher, Immanuel
    ;
    Hendel, Maximilian
    Communication systems are core elements of future cyber-physical systems (Industrial Internet, Industry 4.0, Internet of Things) which form the basis for applications such as smart production, smart grid, smart city, and the like. These applications will comprise data streams with very different communication requirements regarding data rate, latency, jitter, and reliability that may also vary over time. This calls for communication solutions that on one hand can adapt flexibly and on-demand to these needs and on the other hand can seamlessly support data streams with different quality-of-service requirements. Within this paper we present Software-defined Flow Reservation (SDFR), an approach that combines Software-defined Networking (SDN) technologies with Time-Sensitive Networking. Special focus is on the configuration of the time-sensitive network. SDFR implements the IEEE 802.1Qcc standard in the logically centralized SDN controller and, thus, allows for a flexible software-driven configuration of the underlying network. This flexibility enables operators to support multiple configuration interfaces simultaneously and to easily modify traffic classes, scheduling approaches, and network behavior. SDFR allows to re-use existing solutions for well-understood SDN use cases while simultaneously supporting novel TSN-specific solutions.
  • Publication
    A comparative study of coherence estimators for interferometric SAR image coregistration and coherent change detection
    ( 2019)
    Hammer, Horst
    ;
    Thiele, Antje
    ;
    Lorenz, Fabian
    ;
    Cadario, Erich
    ;
    Kuny, Silvia
    ;
    Schulz, Karsten
    The coherent nature of the SAR imaging process opens up the opportunity to create interferometric image pairs, which carry a large amount of information about the scene. In this paper, the interferometric coherence is investigated in detail. Coherence is a measure for the temporal stability of the scene with respect to the phase information. Classically, coherence is used for the task of co-registration of the image pair, with the goal of coherence maximization, since such a co-registration will yield the most reliable interferometric phase information. The second important field of application is coherent change detection, i.e. the detection of changes in the scene that most often do not change the backscattering properties of the images and thus are not detectable in the amplitude images. For such an application it is of importance to maximize the contrast between the incoherent changed parts of the scene and the coherent surroundings. With these two applications in mind, in this paper, several published coherence estimation schemes are investigated. The different coherence estimators are applied to an airborne data set, and results regarding coherence maximization and coherence contrast maximization are shown.