Mapping Terrestrial Impact Craters with the TanDEM-X Digital Elevation Model

The TanDEM-X mission generates a global digital elevation model (DEM) with unprecedented properties. We use it for mapping confirmed terrestrial impact craters as listed in the Earth Impact Database. Both for simple and complex craters detailed investigations of the morphology of the particular structure and of the surrounding terrain can be performed

An FPGA/MPSoC Based Low Latency Onboard SAR Processor

This paper describes the concept and prototype implementation of a low latency spaceborne onboard Synthetic Aperture Radar (SAR) processor runing on a Multi-Processor-System-On-Chip (MPSoC) computing device combining an ARM processor and a Field-Programmable-Gate-Array (FPGA). The SAR processor is designed to generate SAR imagery from TerraSAR-X stripmap data for subsequent ship detection and sea state determination. Low latency data processing is a key development goal. Currently, a raw data block of 8k×32k samples, covering 375 km^2 to 500 km^2 , is focused on the hardware within 4 s. Together with an attached level-2 ship detection, wind, and sea state processor, running on the same device, a SAR data processing chain for generation of maritime alerts is formed. This chain is part of a larger prototype system being developed in the frame of the H2020 EO-ALERT project which further comprises an optical data chain, data compression/encryption, and scheduling on multiple reconfigurable MPSoC boards

Computation of Signal Gain and Noise Gain of a SAR Processor

For the applicability of a SAR system as a radiometric measurement system, the knowledge of its end to end gain, from the raw data acquisition to the ready-made SAR image, is a prerequisite. In the process, the computation and desirably the normalization of the SAR processor gain is a task which should not be underestimated, as several processing steps and a multitude of influencing variables has to be considered. In order to satisfactorily solve this task in the framework of SAR processor development, each processing step has to be analyzed thoroughly whether and by which factor it changes the signal power. Another important characteristic with respect to radiometry is the noise figure in the focused SAR image. Here too, the computation of the processor gain is a major issue. However, echo signal and the receiver noise, the latter might be evaluated on base of so-called noise pulse recorded before and after the actual datatake, differ in their spectral shape. Therefore the effect of a signal processing step on both, signal and noise, is in general different and has to be analyzed separately. As the computation of signal and noise gain coincide in their methodology, both aspects shall be jointly discussed in the presentation. Using the example of the Extended Chirp Scaling algorithm which is the basis for the TerraSAR-X Multimode SAR Processor (TMSP), the attention shall be turned to which kinds of processing steps might affect signal and noise gain and how to quantify the effect. A fundamental design decision is whether a SAR processor shall be power or energy normalized. The former has the advantage that brightness and saturation degree of the SAR image do not depend on the pixel spacing in azimuth and range. The squared amplitude of a pixel corresponds to the radar backscatter coefficient. The distinction between power and energy normalization gains relevance for processing steps like spectral zero padding which originally keeps the signal energy but not the power. Here, power normalization necessitates an adequate amplification of the signal. During the range pre-compression step the recorded echo signal is correlated by the complex conjugate signal of a chirp pulse replica. Therefore, the squared chirp pulse energy occurs in the filtered signal because the energy originates once from the transmitted radar pulse and once from the used replica. Accordingly, the signal amplitude has to be attenuated by the integral of the squared amplitude of the chirp (i.e. the chirp energy). Band-pass filters are one potential source for differences between signal and noise gain provided that the bandwidth of the echo signal and of the noise pulses that estimate the receiver noise are different. In this case the band-pass filter removes different portions of power from signal and noise. Another processing step which influences the spectral shape of signal and noise is the azimuth antenna pattern removal which equalizes the SAR echoes along the aperture and intentionally leads to an almost rectangular spectrum of the band limited echo signal. In contrast, no antenna pattern originally occurred in the noise signal so that exactly this processing step impresses the reciprocal antenna pattern to the noise spectrum, amplifying the edges of the spectrum. Once a colored noise spectrum resulted, its spectral shape has to be considered in the processing gain analysis of all subsequent processing steps. In particular, this refers to spectral weighting (e.g. by a Hamming window). A normalization of the applied window function w.r.t. a rectangular spectrum is adequate for the SAR signal. In contrast, the noise gain results as weighted average of the window function, where the weighting function is the spectrum of the colored noise. Due to the importance of the SAR processor for the overall SAR system calibration, a correct normalization is indispensable. It is required from the first days of the mission

Synthetic Aperture Radar Image Formation and Processing on an MPSoC

Satellite remote sensing acquisitions are usually processed after downlink to a ground station. The satellite travel time to the ground station adds to the total latency, increasing the time until a user can obtain the processing results. Performing the processing and information extraction onboard of the satellite can significantly reduce this time. In this study, synthetic aperture radar (SAR) image formation as well as ship detection and extreme weather detection were implemented in a multiprocessor system on a chip (MPSoC). Processing steps with high computational complexity were ported to run on the programmable logic (PL), achieving significant speed-up by implementing a high degree of parallelization and pipelining as well as efficient memory accesses. Steps with lower complexity run on the processing system (PS), allowing for higher flexibility and reducing the need for resources in the PL. The achieved processing times for an area covering 375 km2 were approximately 4 s for image formation, 16 s for ship detection, and 31 s for extreme weather detection. These evelopments combined with new downlink concepts for low-rate information data streams show that the provision of satellite remote sensing results to end users in less than 5 min after acquisition is possible using an adequately equipped satellite

Demonstrating a SAR Satellite Onboard Processing Chain

The EO-ALERT project, being implemented by a European consortium in the frame of the European Union’s Horizon 2020 programme, has developed, built, and tested a prototype demonstrator chain comprising onboard raw EO data (SAR and optical) Level 1 processing, Level 2 image processing and transfer of final EO alert products to the end user. The goal of the project is to demonstrate the feasibility of the concept aiming at overall latencies below 5 minutes even down to 1 minute. The task of DLR within the project was to implement a low latency SAR onboard Level 1 and Level 2 processing chain. SAR processing is well known for its high demand for computational resources. The targeted latencies together with low power and low mass constraints require the use of Field-Programmable Gate Array (FPGA) technology. Multi-Processor System on a Chip (MPSoC) devices were chosen for all onboard data chain developments. SAR image formation (IF) and SAR image processing (IP) are integrated in a single bitstream file and loaded into a single MPSoC device. The the paper provides an overview of the demonstrator chain built in the project, the SAR processing chain implementation and the achieved test result

The TanDEM-X Digital Elevation Model and Terrestrial Impact Structures

We utilized the TanDEM-X digital elevation model (DEM) for investigating the complete record of confirmed terrestrial impact structures with respect to its suitability to support geological analysis. The consistently high resolution and high accuracy of this model is a prerequisite for detailed morphological studies. This DEM represents an interesting repository to aid in preparing and executing fieldwork for the exploration of new impact crater candidates. For a selection of small, mid-sized, and large impact structures, we here compare the TanDEM-X results with those from other DEMs that were derived either with synthetic aperture radar interferometry or from optical stereo pairs. Our analysis includes high-resolution mapping and the generation of detailed elevation cross sections. Only for very small impact craters, when the diameter is in the order of the pixel posting of TanDEM-X of 12 m or when the texture of the local environment does not support radar remote sensing, accurate analysis is hampered. Our results demonstrate that the high horizontal and vertical accuracies of the TanDEM-X DEM, coupled with its dense pixel grid, provide a considerable improvement in space-borne remote sensing of the complete record of simple and complex terrestrial impact structures over a wide range of diameters

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

TerraSAR-X SAR Data Processing

The TerraSAR-X Mission started operational provision of SAR image products to the scientific and commercial user community in January 2008. An essential prerequisite for the excellent quality of the SAR products was the successful execution of a comprehensive commissioning phase (CP) in 2007. Here, the complete SAR system which comprises instrument commanding, instrument SAR data acquisition as well as SAR processing has been characterized, calibrated and verified. Finally SAR image product verification ensured that the product performance parameters are within the specification. Besides the versatile high-resolution X-Band SAR instrument in space, featuring Stripmap, ScanSAR and Spotlight imaging modes in different polarizations, the TerraSAR Multi-Mode SAR Processor (TMSP) is the central part of the ground segment. Most instrument and SAR calibration parameters have been derived on basis of SAR image products generated by the TMSP. Therefore, already in the beginning of the CP the products had to be relatively radiometric calibrated and geometrical undistorted. An indispensable prerequisite for this was the imaging mode independent normalization of the processor gain as well as the incorporation of external information, i.e. a digital elevation model for the projection of the elevation gain antenna pattern onto the terrain surface and a model of the atmosphere accounting for additional propagation delays. During the CP the TMSP has been adjusted to the in-orbit characteristics of the SAR data and instrument internal calibration. This includes adaptations of calibration pulse processing to a modified internal calibration strategy, accounting for duty cycle dependent pulse energy and temperature dependent gain levels as well as a fine tuning of the signal and geometry based Doppler centroid estimation algorithm. Furthermore, the determination of the reference function for range focusing has been optimized. Finally, the spectral weighting of the SAR data has been adjusted in order to obtain well balanced impulse response function properties in terms of resolution, side lobe ratios and azimuth ambiguities. The presentation reviews the essential features of the TMSP, summarizes the TMSP adjustments and presents results of the SAR product verification

Generation of Rapid Civil Alerts by Satellite On-Board SAR Processing

The concept and prototype implementation of a satellite on-board SAR processing chain designed for Maritime Situation Awareness is described. It aims to reduce the latency between data acquisition and product delivery to about 3-4 minutes. SAR processing is one component of a larger prototype system being developed in the frame of the H2020 EO-ALERT project. It further comprises an optical data chain, data compression/encryption, and delivery. The system employs multiple boards with Multi-Processor-System-On-Chip (MPSoC) combining FPGAs and ARM CPUs. Low latency data processing was a key development goal, hence, a tailored workflow and adapted L1 and L2 processing algorithms ensure that the requirements for latency and product quality are met. The SAR processor is designed to generate SAR imagery from TerraSAR-X stripmap data for subsequent ship detection and sea state determination. The achieved overall L1 and L2 processing times were 60 s for ship detection and 105 s for sea state determination on a 1125 km² SAR image. These results enable further work towards a new generation of Earth Observation satellites with similar processing capabilities on-board, providing users with products only a few minutes after acquisition