Sentinel-1 Imaging Performance Verification with TerraSAR-X

This paper presents dedicated analyses of TerraSAR-X data with respect to the Sentinel-1 TOPS imaging mode. First, the analysis of Doppler centroid behaviour for high azimuth steering angles, as occurs in TOPS imaging, is investigated followed by the analysis and compensation of residual scalloping. Finally, the Flexible-Dynamic BAQ (FD-BAQ) raw data compression algorithm is investigated for the first time with real TerraSAR-X data and its performance is compared to state-of-the-art BAQ algorithms. The presented analyses demonstrate the improvements of the new TOPS imaging mode as well as the new FD-BAQ data compression algorithm for SAR image quality in general and in particular for Sentinel-1

Adaptive On-Board Signal Compression for SAR Using Machine Learning Methods

Satellites with synthetic aperture radar (SAR) payloads are growing in popularity, with a number of new institutional missions and commercial constellations launched or in planning. As an active instrument operating in the microwave region of the electromagnetic spectrum, SAR provides a number of unique advantages over passive optical instruments, in that it can image in all weather conditions and at night. This allows dense time-series to be built up over areas of interest, that are useful in a variety of Earth observation applications. The polarisation and phase information that can be captured also allows for unique applications not possible in optical frequencies. The data volume of SAR captures is growing due to developments in modern high-resolution multi-modal SAR. Instruments with higher spatial resolution, wider swaths, multiple beams, multiple frequencies and more polarization channels are being launched. Miniaturization and the deployment of SAR constellations is bringing improved revisit times. All of these developments drive an increase in the operational cost due to the increase in data downlink required. These factors will make on-board data compression more crucial to overall system performance, especially in large scale constellations. The current deployed state-of-the-art of on-board compression in SAR space-borne payloads is Block Adaptive Quantization (BAQ) and variations such as Flexible BAQ, Entropy Constrained BAQ and Flexible Dynamic BAQ. Craft Prospect is working on an evolution of these techniques where machine learning will be used to identify signals based on dynamics and features of the received signal, with this edge processing allowing the tagging of raw data. These tags can then be used to better adjust the compression parameters to fit the local optimum in the acquired data. We present the results of a survey of available raw SAR data which was used to inform a selection of applications and frequencies for further study. Following this, we present a comparison of a number of SAR compression algorithms downselected using trade-off metrics such as the bands/applications they can be applied to and various complexity measures. We then show an assessment of AI/ML feasibility and capabilities, with the improvements assessed on mission examples characterised by the SAR modes and architecture for specific SAR applications. Finally, future hardware feasibility and capability is assessed, targeting a Smallsat SAR mission, with a high level roadmap developed to progress the concept toward this goal

Metrics to evaluate compressions algorithms for RAW SAR data

Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP-C) limitations since platforms are becoming smaller, while SAR operating modes are becoming more complex. Due to the computational complexity of the SAR processing required for modern SAR systems, performing the processing on board the platform is not a feasible option. Thus, SAR systems are producing an ever-increasing volume of data that needs to be transmitted to a ground station for processing. Compression algorithms are utilised to reduce the data volume of the raw data. However, these algorithms can cause degradation and losses that may degrade the effectiveness of the SAR mission. This study addresses the lack of standardised quantitative performance metrics to objectively quantify the performance of SAR data-compression algorithms. Therefore, metrics were established in two different domains, namely the data domain and the image domain. The data-domain metrics are used to determine the performance of the quantisation and the associated losses or errors it induces in the raw data samples. The image-domain metrics evaluate the quality of the SAR image after SAR processing has been performed. In this study three well-known SAR compression algorithms were implemented and applied to three real SAR data sets that were obtained from a prototype airborne SAR system. The performance of these algorithms were evaluated using the proposed metrics. Important metrics in the data domain were found to be the compression ratio, the entropy, statistical parameters like the skewness and kurtosis to measure the deviation from the original distributions of the uncompressed data, and the dynamic range. The data histograms are an important visual representation of the effects of the compression algorithm on the data. An important error measure in the data domain is the signal-to-quantisation-noise ratio (SQNR), and the phase error for applications where phase information is required to produce the output. Important metrics in the image domain include the dynamic range, the impulse response function, the image contrast, as well as the error measure, signal-to-distortion-noise ratio (SDNR). The metrics suggested that all three algorithms performed well and are thus well suited for the compression of raw SAR data. The fast Fourier transform block adaptive quantiser (FFT-BAQ) algorithm had the overall best performance, but the analysis of the computational complexity of its compression steps, indicated that it is has the highest level of complexity compared to the other two algorithms. Since different levels of degradation are acceptable for different SAR applications, a trade-off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP-C limitations, there also remains a trade-off between the performance and the computational complexity of the compression algorithm.Dissertation (MEng)--University of Pretoria, 2019.Electrical, Electronic and Computer EngineeringMEngUnrestricte

Performance-Optimized Quantization for SAR and InSAR Applications

For the design of present and next-generation spaceborne SAR missions, constantly increasing data rates are being demanded, which impose stringent requirements in terms of onboard memory and downlink capacity. In this scenario, the efficient quantization of SAR raw data is of primary importance since the utilized compression rate is directly related to the volume of data to be stored and transmitted to the ground, and at the same time, it affects the resulting SAR imaging performance. In this article, we introduce the performance-optimized block-adaptive quantization (PO-BAQ), a novel approach for SAR raw data compression that aims at optimizing the resource allocation and, at the same time, the quality of the resulting SAR and InSAR products. This goal is achieved by exploiting the a priori knowledge of the local SAR backscatter statistics, which allows for the generation of high-resolution bitrate maps that can be employed to fulfill a predefined performance requirement. Analyses of experimental TanDEM-X interferometric data are presented, which demonstrates the potential of the proposed method as a helpful tool for performance budget definition and data rate optimization of present and future SAR missions

A PARTIAL ACQUISITION TECHNIQUE OF SAR SYSTEM USING COMPRESSIVE SAMPLING METHOD

In line with the development of Synthetic Aperture Radar (SAR) technology, there is a serious problem when the SAR signal is acquired using high rate analog digital converter (ADC), that require large volumes data storage. The other problem on compressive sensing method,which frequently occurs, is a large measurement matrix that may cause intensive calculation. In this paper, a new approach was proposed, particularly on the partial acquisition technique of SAR system using compressive sampling method in both the azimuth and range direction. The main objectives of the study are to reduce the radar raw data by decreasing the sampling rate of ADC and to reduce the computational load by decreasing the dimension of the measurement matrix. The simulation results found that the reconstruction of SAR image using partial acquisition model has better resolution compared to the conventional method (Range Doppler Algorithm/RDA). On a target of a ship, that represents a low-level sparsity, a good reconstruction image could be achieved from a fewer number measurement. The study concludes that the method may speed up the computation time by a factor 4.49 times faster than with a full acquisition matrix

Metrics to evaluate compressions algorithms for RAW SAR data

Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP-C) limitations since platforms are becoming smaller, while SAR operating modes are becoming more complex. Due to the computational complexity of the SAR processing required for modern SAR systems, performing the processing on board the platform is not a feasible option. Thus, SAR systems are producing an ever-increasing volume of data that needs to be transmitted to a ground station for processing. Compression algorithms are utilised to reduce the data volume of the raw data. However, these algorithms can cause degradation and losses that may degrade the effectiveness of the SAR mission. This study addresses the lack of standardised quantitative performance metrics to objectively quantify the performance of SAR data-compression algorithms. Therefore, metrics were established in two different domains, namely the data domain and the image domain. The data-domain metrics are used to determine the performance of the quantisation and the associated losses or errors it induces in the raw data samples. The image-domain metrics evaluate the quality of the SAR image after SAR processing has been performed. In this study three well-known SAR compression algorithms were implemented and applied to three real SAR data sets that were obtained from a prototype airborne SAR system. The performance of these algorithms were evaluated using the proposed metrics. Important metrics in the data domain were found to be the compression ratio, the entropy, statistical parameters like the skewness and kurtosis to measure the deviation from the original distributions of the uncompressed data, and the dynamic range. The data histograms are an important visual representation of the effects of the compression algorithm on the data. An important error measure in the data domain is the signal-to-quantisation-noise ratio (SQNR), and the phase error for applications where phase information is required to produce the output. Important metrics in the image domain include the dynamic range, the impulse response function, the image contrast, as well as the error measure, signal-to-distortion-noise ratio (SDNR). The metrics suggested that all three algorithms performed well and are thus well suited for the compression of raw SAR data. The fast Fourier transform block adaptive quantiser (FFT-BAQ) algorithm had the overall best performance, but the analysis of the computational complexity of its compression steps, indicated that it is has the highest level of complexity compared to the other two algorithms. Since different levels of degradation are acceptable for different SAR applications, a trade-off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP-C limitations, there also remains a trade-off between the performance and the computational complexity of the compression algorithm.Dissertation (MEng)--University of Pretoria, 2019.TM2019Electrical, Electronic and Computer EngineeringMEngUnrestricte

EO-ALERT: NEXT GENERATION SATELLITE PROCESSING CHAIN FOR RAPID CIVIL ALERTS

In this paper, we provide an overview of the H2020 EU project EO-ALERT. The aim of EO-ALERT is to propose the definition and development of the next generation Earth observation (EO) data and processing chain, based on a novel flight segment architecture moving optimised key EO data processing elements from the ground segment to on-board the satellite. The objective is to address the need for increased throughput in EO data chain, delivering EO products to the end user with very low latency

Mexico City land subsidence in 2014-2015 with Sentinel-1 IW TOPS: results using the Intermittent SBAS (ISBAS) technique

Differential Interferometric Synthetic Aperture Radar (DInSAR) can be considered as an efficient and cost effective technique for monitoring land subsidence due to its large spatial coverage and high accuracy provided. The recent commissioning of the first Sentinel-1 satellite offers improved support to operational surveys using DInSAR due to regular observations from a wide-area product. In this paper we show the results of an intermittent small-baseline subset (ISBAS) time-series analysis of 18 Interferometric Wide swath (IW) products of a 39,000 km2 area of Mexico acquired between 3 October 2014 and 7 May 2015 using the Terrain Observation with Progressive Scans in azimuth (TOPS) imaging mode. The ISBAS processing was based upon the analysis of 143 small-baseline differential interferograms. After the debursting, merging and deramping steps necessary to process Sentinel-1 IW roducts, the method followed a standard approach to the DInSAR analysis. The Sentinel-1 ISBAS results confirm the magnitude and extent of the deformation that was observed in Mexico City, Chalco, Ciudad Nezahualcóyotl and Iztapalapa by other C-band and L-band DInSAR studies during the 1990s and 2000s. Subsidence velocities from the Sentinel-1 analysis are, in places, in excess of -24 cm/year along the satellite line-of-sight, equivalent to over ~-40 cm/year vertical rates. This paper demonstrates the potential of Sentinel-1 IW TOPS imagery to support wide-area DInSAR surveys over what is a very large and diverse area in terms of land cover and topography

Application of Sentinel-1 SAR Imagery for Flood Detection and Monitoring, Case Study of Floods in Vrgorac Region during November and December 2020

U radu su prikazane metode digitalne obrade slike koje, slijedno primijenjene, omogućavaju detekciju i praćenje poplavljenih površina korištenjem satelitskih snimki dobivenih radarom sa sintetičkom aperturom (SAR) sa satelita Sentinel-1 na studiji slučaja poplave u vrgoračkom kraju, tijekom studenog i prosinca 2020. godine. Metoda se temelji na raspršenju polariziranog radarskog vala na vodenim površinama. U radu su komentirane prednosti i nedostatci korištenja otvorenih resursa te je predloženo žurno pristupanje Republike Hrvatske Povelji o svemiru i katastrofama.The paper presents digital image processing methods that, applied sequentially, enable detection and monitoring of flooded areas using satellite images obtained by Synthetic Aperture Radar (SAR) from Sentinel-1 satellite in the case study of floods in the Vrgorac region, during November and December 2020. The method is based on the scattering of a polarized radar wave on water surfaces. The paper comments on the advantages and disadvantages of using open resources and proposes the urgent accession of the Republic of Croatia to the Charter on Space

Application of Sentinel-1 SAR Imagery for Flood Detection and Monitoring, Case Study of Floods in Vrgorac Region during November and December 2020

U radu su prikazane metode digitalne obrade slike koje, slijedno primijenjene, omogućavaju detekciju i praćenje poplavljenih površina korištenjem satelitskih snimki dobivenih radarom sa sintetičkom aperturom (SAR) sa satelita Sentinel-1 na studiji slučaja poplave u vrgoračkom kraju, tijekom studenog i prosinca 2020. godine. Metoda se temelji na raspršenju polariziranog radarskog vala na vodenim površinama. U radu su komentirane prednosti i nedostatci korištenja otvorenih resursa te je predloženo žurno pristupanje Republike Hrvatske Povelji o svemiru i katastrofama.The paper presents digital image processing methods that, applied sequentially, enable detection and monitoring of flooded areas using satellite images obtained by Synthetic Aperture Radar (SAR) from Sentinel-1 satellite in the case study of floods in the Vrgorac region, during November and December 2020. The method is based on the scattering of a polarized radar wave on water surfaces. The paper comments on the advantages and disadvantages of using open resources and proposes the urgent accession of the Republic of Croatia to the Charter on Space