W42 - a scalable spatial database system for holding Digital Elevation Models

The design of a scalable system for holding spatial data in general and digital elevation models (DEMs) in specific has to account for the characteristics of data from various application fields. The data can be heterogeneous in coverage, as well as in resolution, information content and quality. A database aiming at the representation of world-wide DEMs has to consider these differences in the design of the system with respect to the structure and the algorithms. The database system W42, which is presented in the work at hand, is a scalable spatial database system capable of holding, extracting, mosaicking, and fusing spatial data represented in raster- as well as in vector-format. Design aspects for this task can be specified as holding spatial data in unique data structures and providing unique access functions to the data. These are subject of this work as well as first experiences gained from the implementation of part of the extensions made for the TanDEM-X mission

Dialectical GAN for SAR Image Translation: From Sentinel-1 to TerraSAR-X

Contrary to optical images, Synthetic Aperture Radar (SAR) images are in different electromagnetic spectrum where the human visual system is not accustomed to. Thus, with more and more SAR applications, the demand for enhanced high-quality SAR images has increased considerably. However, high-quality SAR images entail high costs due to the limitations of current SAR devices and their image processing resources. To improve the quality of SAR images and to reduce the costs of their generation, we propose a Dialectical Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR images. This method is based on the analysis of hierarchical SAR information and the "dialectical" structure of GAN frameworks. As a demonstration, a typical example will be shown where a low-resolution SAR image (e.g., a Sentinel-1 image) with large ground coverage is translated into a high-resolution SAR image (e.g., a TerraSAR-X image). Three traditional algorithms are compared, and a new algorithm is proposed based on a network framework by combining conditional WGAN-GP (Wasserstein Generative Adversarial Network - Gradient Penalty) loss functions and Spatial Gram matrices under the rule of dialectics. Experimental results show that the SAR image translation works very well when we compare the results of our proposed method with the selected traditional methods.Comment: 22 pages, 15 figure

Very-High-Resolution SAR Images and Linked Open Data Analytics Based on Ontologies

In this paper, we deal with the integration of multiple sources of information such as Earth observation (EO) synthetic aperture radar (SAR) images and their metadata, semantic descriptors of the image content, as well as other publicly available geospatial data sources expressed as linked open data for posing complex queries in order to support geospatial data analytics. Our approach lays the foundations for the development of richer tools and applications that focus on EO image analytics using ontologies and linked open data. We introduce a system architecture where a common satellite image product is transformed from its initial format into to actionable intelligence information, which includes image descriptors, metadata, image tiles, and semantic labels resulting in an EO-data model. We also create a SAR image ontology based on our EO-data model and a two-level taxonomy classification scheme of the image content. We demonstrate our approach by linking high-resolution TerraSAR-X images with information from CORINE Land Cover (CLC), Urban Atlas (UA), GeoNames, and OpenStreetMap (OSM), which are represented in the standard triple model of the resource description frameworks (RDFs)

In-depth verification of Sentinel-1 and TerraSAR-X geolocation accuracy using the Australian Corner Reflector Array

This article shows how the array of corner reflectors (CRs) in Queensland, Australia, together with highly accurate geodetic synthetic aperture radar (SAR) techniques—also called imaging geodesy—can be used to measure the absolute and relative geometric fidelity of SAR missions. We describe, in detail, the end-to-end methodology and apply it to TerraSAR-X Stripmap (SM) and ScanSAR (SC) data and to Sentinel-1interferometric wide swath (IW) data. Geometric distortions within images that are caused by commonly used SAR processor approximations are explained, and we show how to correct them during postprocessing. Our results, supported by the analysis of 140 images across the different SAR modes and using the 40 reflectors of the array, confirm our methodology and achieve the limits predicted by theory for both Sentinel-1 and TerraSAR-X. After our corrections, the Sentinel-1 residual errors are 6 cm in range and 26 cm in azimuth, including all error sources. The findings are confirmed by the mutual independent processing carried out at University of Zurich (UZH) and German Aerospace Center (DLR). This represents an improve�ment of the geolocation accuracy by approximately a factor of four in range and a factor of two in azimuth compared with the standard Sentinel-1 products. The TerraSAR-X results are even better. The achieved geolocation accuracy now approaches that of the global navigation satellite system (GNSS)-based survey of the CRs positions, which highlights the potential of the end-to-end SAR methodology for imaging geodesy

Identification of SAR Detected Targets on Sea in Near Real Time Applications for Maritime Surveillance

Remote sensing technologies are widely used in maritime surveillance applications. Nowadays, spaceborne Synthetic Aperture Radar (SAR) systems provide outstanding capabilities for target detection at sea for large areas independently from the weather conditions. The generated value added target detection product is composed by complementary information from the Automatic Identification System (AIS). Resulting information layers provides a more reliable picture on the maritime situation awareness. This paper describes the approach of SAR-AIS data fusion and its visualization means developed for Near Real Time (NRT) Applications for Maritime Situational Awareness by the Maritime Security Lab at the Ground Station in Neustrelitz, part DLR’s German Remote Sensing Data Center (DFD). Presented implementation is based on combination of many open source geospatial libraries and frameworks (e.g., GDAL/OGR, Geoserver, PostgresSQL) and shows their effectiveness in the context of complex automated data processing in the frame of NRT requirements

Artificial Intelligence Data Science Methodology for Earth Observation

This chapter describes a Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator, which is a general platform for the handling, analysis, and interpretation of Earth observation satellite images, mainly exploiting big data of the European Copernicus Programme by artificial intelligence (AI) methods. From 2020, the platform will be applied at a regional and national level to various use cases such as urban expansion, forest health, and natural disasters. Its workflows allow the selection of satellite images from data archives, the extraction of useful information from the metadata, the generation of descriptors for each individual image, the ingestion of image and descriptor data into a common database, the assignment of semantic content labels to image patches, and the possibility to search and to retrieve similar content-related image patches. The main two components, namely, data mining and data fusion, are detailed and validated. The most important contributions of this chapter are the integration of these two components with a Copernicus platform on top of the European DIAS system, for the purpose of large-scale Earth observation image annotation, and the measurement of the clustering and classification performances of various Copernicus Sentinel and third-party mission data. The average classification accuracy is ranging from 80 to 95% depending on the type of images

Land subsidence in coastal environments: Knowledge advance in the Venice coastland by TerraSAR-X PSI

The use of satellite SAR interferometric methods has significantly improved the monitoring of ground movements over the last decades, thus opening new possibilities for a more accurate interpretation of land subsidence and its driving mechanisms. TerraSAR-X has been extensively used to study land subsidence in the Venice Lagoon, Italy, with the aim of quantifying the natural and anthropogenic causes. In this paper, we review and update the main results achieved by three research projects supported by DLR AOs (German Aerospace Center Announcement of Opportunity) and conducted to test the capability of TerraSAR-X PSI (Persistent Scatterer Interferometry) to detect ground movements in the complex physiographic setting of the Venice transitional coastal environment. The investigations have been focused on the historical center of Venice, the lagoon inlets where the MoSE is under construction, salt marshes, and newly built-up areas in the littoral. PSI on stacks of stripmap TerraSAR-X images covering short- to long-time periods (i.e., the years 2008\u20132009, 2008\u20132011 and 2008\u20132013) has proven particularly effective to measure land subsidence in the Venice coastland. The very high spatial resolution (3 m) and the short repeat time interval (11 days) of the TerraSAR-X acquisitions make it possible to investigate ground movements with a detail unavailable in the past. The interferometric products, properly calibrated, allowed for a millimetric vertical accuracy of the land movements at both the regional and local scales, even for short-term analyses, i.e., spanning one year only. The new picture of the land movement resulted from processing TerraSAR-X images has significantly contributed to update the knowledge on the subsidence process at the Venice coast

LION Navigator for Transfer to GEO Using Electric Propulsion

GNSS space receivers are widely used for onboard auton-omous navigation of spacecraft platforms in low Earth orbit. Navigation by GNSS up to geosynchronous altitude was made possible through the introduction of a Space Service Volume which defines signal strength up to geo-synchronous altitude. For Galileo, similar definitions are under consideration. On this basis onboard autonomous navigation for commercial communication satellites be-came a realistic possibility, too. Transfer to geostationary orbit is still fully depending on classical RF tracking by ground station for orbit determination. With electrical propulsion, the transfer duration extends to several months. As a consequence onboard autonomous naviga-tion by satellite navigation has become of commercial interest. A GNSS navigation receiver on a spacecraft on transfer orbit has to cope with extreme signal conditions from very low (at perigee) to very high (at super-synchronous apogee) altitude, which is far above the constellation satellites. At this altitude only very rare and weak signals that spill over the limb of the earth can be used. An addi-tional difficulty is the varying spacecraft orientation which is not nadir pointing, as is commonly assumed, but is varying according to the demands of optimal attitude guidance laws and power requirements. By using both GPS and Galileo together the availability of navigation signals is increased. The paper describes the design process to determine basic parameters e.g. number and orientation of receive anten-nas, receiver parameters like C/N0 thresholds, and naviga-tion procedures. Detailed simulations are presented for selected parts of the transfer arc using verified models of the navigation receiver. Finally the geostationary transfer capabilities of the space-borne LION Navigator GNSS receiver are demon-strated in a closed-loop real time test environment under RF stimulation

Satellite Reentry Control via Surface Area Amplification

This project endeavors to find whether it is feasible to use an increase in surface area as a way of increasing the drag on an orbiting object, thus decreasing its orbital lifetime. The surface area increase can be achieved by an apparatus that deploys a balloon. The balloon will act as a parachute that will decrease the potential energy of the object through atmospheric drag. This is most effective by objects that reach the Low Earth altitudes of less than 500 kilometers, where an object is encountering a firmer atmospheric density. The project is carried out through propagating three different element sets to reentry using STK ®. The orbital paths generated by the software are then graphed in Excel ® and presented. The analysis is divided into four main studies. The first study focuses on confirming the effects the atmospheric instability has on the long term predictions of a natural decay. The second study explores how an increase in the scale of the drag, at different points in the orbital path, affects the reentry time. The third study investigates a specific increase of area to mass ratio (A/M) at different points in the trajectory. This is to survey changes in the variability of the reentry time prediction and how the reentry location\u27s variability is altered. To finish off, the last study examines how A/M manipulation affects the reentry time prediction. The project discovered that an increase in A/M decreases the variability in reentry prediction. Furthermore, it discerns the exponential relationship between the time to reentry and the A/M

Towards a 20m global building map from Sentinel-1 SAR Data

This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.Peer ReviewedPostprint (published version