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

The Second Spaceborne Imaging Radar Symposium

Summaries of the papers presented at the Second Spaceborne Imaging Radar Symposium are presented. The purpose of the symposium was to present an overwiew of recent developments in the different scientific and technological fields related to spaceborne imaging radars and to present future international plans

Design, analysis and evaluation of sigma-delta based beamformers for medical ultrasound imaging applications

The inherent analogue nature of medical ultrasound signals in conjunction with the abundant merits provided by digital image acquisition, together with the increasing use of relatively simple front-end circuitries, have created considerable demand for single-bit beamformers in digital ultrasound imaging systems. Furthermore, the increasing need to design lightweight ultrasound systems with low power consumption and low noise, provide ample justification for development and innovation in the use of single-bit beamformers in ultrasound imaging systems. The overall aim of this research program is to investigate, establish, develop and confirm through a combination of theoretical analysis and detailed simulations, that utilize raw phantom data sets, suitable techniques for the design of simple-to-implement hardware efficient digital ultrasound beamformers to address the requirements for 3D scanners with large channel counts, as well as portable and lightweight ultrasound scanners for point-of-care applications and intravascular imaging systems. In addition, the stability boundaries of higher-order High-Pass (HP) and Band-Pass (BP) Σ−Δ modulators for single- and dual- sinusoidal inputs are determined using quasi-linear modeling together with the describing-function method, to more accurately model the modulator quantizer. The theoretical results are shown to be in good agreement with the simulation results for a variety of input amplitudes, bandwidths, and modulator orders. The proposed mathematical models of the quantizer will immensely help speed up the design of higher order HP and BP Σ−Δ modulators to be applicable for digital ultrasound beamformers. Finally, a user friendly design and performance evaluation tool for LP, BP and HP modulators is developed. This toolbox, which uses various design methodologies and covers an assortment of modulators topologies, is intended to accelerate the design process and evaluation of modulators. This design tool is further developed to enable the design, analysis and evaluation of beamformer structures including the noise analyses of the final B-scan images. Thus, this tool will allow researchers and practitioners to design and verify different reconstruction filters and analyze the results directly on the B-scan ultrasound images thereby saving considerable time and effort

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

Space and Earth Science Data Compression Workshop

The workshop explored opportunities for data compression to enhance the collection and analysis of space and Earth science data. The focus was on scientists' data requirements, as well as constraints imposed by the data collection, transmission, distribution, and archival systems. The workshop consisted of several invited papers; two described information systems for space and Earth science data, four depicted analysis scenarios for extracting information of scientific interest from data collected by Earth orbiting and deep space platforms, and a final one was a general tutorial on image data compression

MEDSAT: A Small Satellite for Malaria Early Warning and Control

This paper presents the design for a low cost, light satellite used to aid in the control of vector-borne diseases like malaria. The 340 kg satellite contains both a synthetic aperture radar and a visual/infrared multispectral scanner for remotely sensing the region of interest. Most of the design incorporates well established technology, but innovative features include the Pegasus launch vehicle, low mass and volume SAR and VIS/IR sensors, integrated design, low power SAR operation, microprocessor power system control, and advanced data compression and storage. This paper describes the main design considerations of the project which include, the remote sensing task, implementation for malaria control, launch vehicle, orbit, satellite bus, and satellite Subsystems

1994 Science Information Management and Data Compression Workshop

This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on September 26-27, 1994, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival and retrieval of large quantities of data in future Earth and space science missions. It consisted of eleven presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center

Proceedings of the Scientific Data Compression Workshop

Continuing advances in space and Earth science requires increasing amounts of data to be gathered from spaceborne sensors. NASA expects to launch sensors during the next two decades which will be capable of producing an aggregate of 1500 Megabits per second if operated simultaneously. Such high data rates cause stresses in all aspects of end-to-end data systems. Technologies and techniques are needed to relieve such stresses. Potential solutions to the massive data rate problems are: data editing, greater transmission bandwidths, higher density and faster media, and data compression. Through four subpanels on Science Payload Operations, Multispectral Imaging, Microwave Remote Sensing and Science Data Management, recommendations were made for research in data compression and scientific data applications to space platforms

Advanced methods and deep learning for video and satellite data compression

L'abstract è presente nell'allegato / the abstract is in the attachmen