Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like scene monitoring over time or the scene analysis after sudden events. These tasks often require the fusion of geo-referenced and precisely co-registered multi-sensor data. Images captured by high resolution synthetic aperture radar (SAR) satellites have an absolute geo-location accuracy within few decimeters. This renders SAR images interesting as a source for the geo-location improvement of optical images, whose geo-location accuracy is in the range of some meters. In this paper, we are investigating a deep learning based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data. Image registration between SAR and optical satellite images requires few but accurate and reliable matching points. To derive such matching points a neural network based on a Siamese network architecture was trained to learn the two dimensional spatial shift between optical and SAR image patches. The neural network was trained over TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe. The results of the proposed method confirm that accurate and reliable matching points are generated with a higher matching accuracy and precision than state-of-the-art approaches

A Protective Allergy Vaccine Based on CpG- and Protamine-Containing PLGA Microparticles

Purpose: Allergen-specific immunotherapy (SIT) requires dozens of subcutaneous injections over 3 to 5years in order to control IgE-mediated hypersensitivity, which is a T-helper 2 (Th2)-associated pathology. This study investigates the use of poly(lactide-co-glycolide) (PLGA) microparticles combined with immunostimulatory oligodeoxynucleotide (CpG), as well as protamine in SIT. Materials and Methods: We prepared microparticle formulations with the major allergen of bee venom, phospholipase A2 (PLA2), and analyzed the effect of co-encapsulated or admixed CpG in both naïve and bee venom allergic mice. Results: Mice immunized with microparticles containing only PLA2 induced weak antibody responses. In contrast, the combination with CpG resulted in strong PLA2-specific antibody responses. The presence of CpG was required for the induction of the Th1-associated isotype IgG2a, and the titers of IgG2a in sensitized mice correlated with a better protection against an allergen challenge. The effect of CpG was further strengthened when protamine was co-encapsulated for complexation of CpG. Conclusions: This study shows that allergen-specific immunotherapy with a PLGA-based allergen-delivery system in combination with CpG enhanced the induction of protective IgG2a immune responses. This may improve SIT compliance and shorten its duratio

Exploring the Potential of Conditional Adversarial Networks for Optical and SAR Image Matching

Tasks such as the monitoring of natural disasters or the detection of change highly benefit from complementary information about an area or a specific object of interest. The required information is provided by fusing high accurate co-registered and geo-referenced datasets. Aligned high resolution optical and synthetic aperture radar (SAR) data additionally enables an absolute geo-location accuracy improvement of the optical images by extracting accurate and reliable ground control points (GCPs) from the SAR images. In this paper we investigate the applicability of a deep learning based matching concept for the generation of precise and accurate GCPs from SAR satellite images by matching optical and SAR images. To this end, conditional generative adversarial networks (cGANs) are trained to generate SAR-like image patches from optical images. For training and testing, optical and SAR image patches are extracted from TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe. The artificially generated patches are then used to improve the conditions for three known matching approaches based on normalized cross-correlation (NCC), SIFT and BRISK, which are normally not usable for the matching of optical and SAR images. The results validate that a NCC, SIFT and BRISK based matching greatly benefit, in terms of matching accuracy and precision, from the use of the artificial templates. The comparison with two state-of-the-art optical and SAR matching approaches shows the potential of the proposed method but also revealed some challenges and the necessity for further developments

SAR-to-Optical Image Translation Based on Conditional Generative Adversarial Networks - Optimization, Opportunities and Limits

Due to its all time capability, synthetic aperture radar (SAR) remote sensing plays an important role in Earth observation. The ability to interpret the data is limited, even for experts, as the human eye is not familiar to the impact of distance-dependent imaging, signal intensities detected in the radar spectrum as well as image characteristics related to speckle or steps of post-processing. This paper is concerned with machine learning for SAR-to-optical image-to-image translation in order to support the interpretation and analysis of original data. A conditional adversarial network is adopted and optimized in order to generate alternative SAR image representations based on the combination of SAR images (starting point) and optical images (reference) for training. Following this strategy, the focus is set on the value of empirical knowledge for initialization, the impact of results on follow-up applications, and the discussion of opportunities/drawbacks related to this application of deep learning. Case study results are shown for high resolution (SAR: TerraSAR-X, optical: ALOS PRISM) and low resolution (Sentinel-1 and -2) data. The properties of the alternative image representation are evaluated based on feedback from experts in SAR remote sensing and the impact on road extraction as an example for follow-up applications. The results provide the basis to explain fundamental limitations affecting the SAR-to-optical image translation idea but also indicate benefits from alternative SAR image representations

Hepatitis C Virus Induced Endothelial Inflammatory Response Depends on the Functional Expression of TNF alpha Receptor Subtype 2

In hepatitis C virus (HCV) infection, morbidity and mortality often result from extrahepatic disease manifestations. We provide evidence for a role of receptors of the innate immune system in virally induced inflammation of the endothelium in vitro and in vivo. Corresponding to the in vitro finding of an HCV-dependent induction of proinflammatory mediators in endothelial cells, mice treated with poly (I: C) exhibit a significant reduction in leukocyte rolling velocity, an increase in leukocyte adhesion to the vessel wall and an increased extravasation of leukocytes. HCV directly promotes activation, adhesion and infiltration of inflammatory cells into the vessel wall by activation of endothelial viral receptors. Poly (I: C) induces the expression of TLR3 in vivo and hereby allows for amplification of all of the aforementioned responses upon viral infection. Proinflammatory effects of viral RNA are specifically mediated by TLR3 and significantly enhanced by tumor necrosis factor alpha (TNFa). HCV-RNA induces the endothelial expression of TNFa and TNFa receptor subtype 2 and we provide evidence that leucocyte adhesion and transmigration in response to activation of viral RNA receptors seem to depend on expression of functional TNFR2. Our results demonstrate that endothelial cells actively participate in immune mediated vascular inflammation caused by viral infections

Generation of Reference Vehicle Trajectories in real-world Situations using Aerial Imagery from a Helicopter

Highly accurate reference vehicle trajectories are required in the automotive domain e.\,g. for testing mobile GNSS devices. Common methods used to determine reference trajectories are based on the same working principles as the device under test and suffer from the same underlying error problems. In this paper, a new method to generate reference vehicle trajectories in real-world situations using simultaneously acquired aerial imagery from a helicopter is presented. This method requires independent height information which is coming from a LIDAR DTM and the relative height of the GNSS device. The reference trajectory is then derived by forward intersection of the vehicle position in each image with the DTM. In this context, the influence of all relevant error sources were analysed, like the error from the LIDAR DTM, from the sensor latency, from the semi-automatic matching of the vehicle marking, and from the image orientation. Results show that the presented method provides a tool for creating reference trajectories that is independent of the GNSS reception at the vehicle. Moreover, it can be demonstrated that the proposed method reaches an accuracy level of 10 cm, which is defined as necessary for certification and validation of automotive GNSS devices

Cdk5 Regulates Accurate Maturation of Newborn Granule Cells in the Adult Hippocampus

Newborn granule cells become functionally integrated into the synaptic circuitry of the adult dentate gyrus after a morphological and electrophysiological maturation process. The molecular mechanisms by which immature neurons and the neurites extending from them find their appropriate position and target area remain largely unknown. Here we show that single-cell–specific knockdown of cyclin-dependent kinase 5 (cdk5) activity in newborn cells using a retrovirus-based strategy leads to aberrant growth of dendritic processes, which is associated with an altered migration pattern of newborn cells. Even though spine formation and maturation are reduced in cdk5-deficient cells, aberrant dendrites form ectopic synapses onto hilar neurons. These observations identify cdk5 to be critically involved in the maturation and dendrite extension of newborn neurons in the course of adult neurogenesis. The data presented here also suggest a mechanistic dissociation between accurate dendritic targeting and subsequent synapse formation