SUBMANIFOLD SPARSE CONVOLUTIONAL NETWORKS FOR SEMANTIC SEGMENTATION OF LARGE-SCALE ALS POINT CLOUDS

Semantic segmentation of point clouds is one of the main steps in automated processing of data from Airborne Laser Scanning (ALS). Established methods usually require expensive calculation of handcrafted, point-wise features. In contrast, Convolutional Neural Networks (CNNs) have been established as powerful classifiers, which at the same time also learn a set of features by themselves. However, their application to ALS data is not trivial. Pure 3D CNNs require a lot of memory and computing time, therefore most related approaches project ALS point clouds into two-dimensional images. Sparse Submanifold Convolutional Networks (SSCNs) address this issue by exploiting the sparsity often inherent in 3D data. In this work, we propose the application of SSCNs for efficient semantic segmentation of voxelized ALS point clouds in an end-to-end encoder-decoder architecture. We evaluate this method on the ISPRS Vaihingen 3D Semantic Labeling benchmark and achieve state-of-the-art 85.0% overall accuracy. Furthermore, we demonstrate its capabilities regarding large-scale ALS data by classifying a 2.5 km2 subset containing 41 M points from the Actueel Hoogtebestand Nederland (AHN3) with 95% overall accuracy in just 48 s inference time or with 96% in 108 s

Persistent scatterer aided facade lattice extraction in single airborne optical oblique images

We present a new method to extract patterns of regular facade structures from single optical oblique images. To overcome the missing three-dimensional information we incorporate structural information derived from Persistent Scatter (PS) point cloud data into our method. Single oblique images and PS point clouds have never been combined before and offer promising insights into the compatibility of remotely sensed data of different kinds. Even though the appearance of facades is significantly different, many characteristics of the prominent patterns can be seen in both types of data and can be transferred across the sensor domains. To justify the extraction based on regular facade patterns we show that regular facades appear rather often in typical airborne oblique imagery of urban scenes. The extraction of regular patterns is based on well established tools like cross correlation and is extended by incorporating a module for estimating a window lattice model using a genetic algorithm. Among others the results of our approach can be used to derive a deeper understanding of the emergence of Persistent Scatterers and their fusion with optical imagery. To demonstrate the applicability of the approach we present a concept for data fusion aiming at facade lattices extraction in PS and optical data

Accurate matching and reconstruction of line features from ultra high resolution stereo aerial images

In this study, a new reconstruction approach is proposed for the line segments that are nearly-aligned(<= 10 degrees) with the epipolar line. The method manipulates the redundancy inherent in line pair-relations to generate artificial 3D point entities and utilize those entities during the estimation process to improve the height values of the reconstructed line segments. The best point entities for the reconstruction are selected based on a newly proposed weight function. To test the performance of the proposed approach, we selected three test patches over a built up area of the city of Vaihingen-Germany. Based on the results, the proposed approach produced highly promising reconstruction results for the line segments that are nearly-aligned with the epipolar line

Modeling spacecraft oscillations in hrsc images of mars express

Since January 2004 the High Resolution Stereo Camera (HRSC) is mapping planet Mars. The multi-line sensor on board the ESA Mission Mars Express images the Martian surface with a resolution of up to 1 2 m per pixel in three dimensions and in color. As part of the Photogrammetric/Cartographic Working Group of the HRSC Science Team the Institute of Photogrammetry and GeoInformation (IPI) of the Leibniz Universitat Hannover is involved in photogrammetrically processing the HRSC image data. To derive high quality 3D surface models, color orthoimages or other products, the accuracy of the observed position and attitude information in many cases should be improved. This is carried out via a bundle adjustment. In a considerable number of orbits the results of the bundle adjustment are disturbed by high frequency oscillations. This paper describes the impact of the high frequency angular spacecraft movement to the processing results of the last seven years of image acquisition and how the quality of the HRSC data products is significantly improved by modeling these oscillations.DLR/50 QM 090

Multi-source hierarchical conditional random field model for feature fusion of remote sensing images and LiDAR data

Feature fusion of remote sensing images and LiDAR points cloud data, which have strong complementarity, can effectively play the advantages of multi-class features to provide more reliable information support for the remote sensing applications, such as object classification and recognition. In this paper, we introduce a novel multi-source hierarchical conditional random field (MSHCRF) model to fuse features extracted from remote sensing images and LiDAR data for image classification. Firstly, typical features are selected to obtain the interest regions from multi-source data, then MSHCRF model is constructed to exploit up the features, category compatibility of images and the category consistency of multi-source data based on the regions, and the outputs of the model represents the optimal results of the image classification. Competitive results demonstrate the precision and robustness of the proposed method

FROM MULTIPLE POLYGONS TO SINGLE GEOMETRY: OPTIMIZATION OF POLYGON INTEGRATION FOR CROWDSOURCED DATA

Paid crowdsourcing is a popular approach for creating training data in machine learning, but output quality can suffer from various drawbacks, such as noisy data. One solution is to obtain multiple acquisitions of the same dataset and perform integration steps, which can be challenging for geometries such as polygons. In this paper, we propose a raster-based polygon integration approach for the use of crowdsourced data, providing a solution for integrating multiple geometric shapes into single geometries. We analyze the effects of the choice of the integration threshold parameter for different sample sizes on the quality measures intersection over union (IoU) and Hausdorff distance, and provide a recommendation for its optimal selection based on empirical analysis. Additionally, further possibilities to improve integration results are explored, i.e., methods of filtering data before integration by outlier detection

VEHICLE OCCLUSION REMOVAL FROM SINGLE AERIAL IMAGES USING GENERATIVE ADVERSARIAL NETWORKS

Removing occluding objects such as vehicles from drivable areas allows precise extraction of road boundaries and related semantic objects such as lane-markings, which is crucial for several applications such as generating high-definition maps for autonomous driving. Conventionally, multiple images of the same area taken at different times or from various perspectives are used to remove occlusions and to reconstruct the occluded areas. Nevertheless, these approaches require large amounts of data, which are not always available. Furthermore, they do not work for static occlusions caused by, among others, parked vehicles. In this paper, we address occlusion removal based on single aerial images using generative adversarial networks (GANs), which are able to deal with the mentioned challenges. To this end, we adapt several state-of-the-art GAN-based image inpainting algorithms to reconstruct the missing information. Results indicate that the StructureFlow algorithm outperforms the competitors and the restorations obtained are robust, with high visual fidelity in real-world applications. Furthermore, due to the lack of annotated aerial vehicle removal datasets, we generate a new dataset for training and validating the algorithms, the Aerial Vehicle Occlusion Removal (AVOR) dataset. To the best of our knowledge, our work is the first to address vehicle removal using deep learning algorithms to enhance maps

Population pharmacokinetics at two dose levels and pharmacodynamic profiling of flucloxacillin

Flucloxacillin is often used for the treatment of serious infections due to sensitive staphylococci. The pharmacokinetic (PK)-pharmacodynamic (PD) breakpoint of flucloxacillin has not been determined by the use of population PK. Targets based on the duration of non-protein-bound concentrations above the MIC (fT(> MIC)) best correlate with clinical cure rates for beta-lactams. We compared the breakpoints for flucloxacillin between several dosage regimens. In a randomized, two-way crossover study, 10 healthy volunteers received 500 mg and 1,000 mg flucloxacillin as 5-min intravenous infusions. Drug concentrations were determined by high-pressure liquid chromatography. We used the programs WinNonlin for noncompartmental analysis and statistics and NONMEM for population PK and Monte Carlo simulation. We compared the probability of target attainment (PTA) for intermittent- and continuous-dosage regimens based on the targets of fT(> MIS)s of >= 50% and >= 30% of the dosing interval. The clearance and the volume of distribution were very similar after the administration of 500 mg and 1,000 mg flucloxacillin. We estimated renal and nonrenal clearances of 5.37 liters/h (coefficient of variation, 19%) and 2.73 liters/h (33%). For near maximal killing (target, fT(> MIC) of >= 50%) flucloxacillin showed a robust (>= 90%) PTA up to MICs of 0.75 to 1 mg/liter (PTA of 860/v at 1 mg/liter) for a continuous or a prolonged infusion of 6 g/day. Short-term infusions of 6 g/day had a lower breakpoint of 0.25 to 0.375 mg/liter. The flucloxacillin PK was linear for doses of 500 mg and 1,000 mg. Prolonged and continuous infusion at a 66% lower daily dose achieved the same PK-PD breakpoints as short-term infusions. Prolonged infusion and continuous infusion are appealing options for the treatment of serious infections caused by sensitive staphylococci