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Synthetic aperture radar and digital processing: An introduction

A tutorial on synthetic aperture radar (SAR) is presented with emphasis on digital data collection and processing. Background information on waveform frequency and phase notation, mixing, Q conversion, sampling and cross correlation operations is included for clarity. The fate of a SAR signal from transmission to processed image is traced in detail, using the model of a single bright point target against a dark background. Some of the principal problems connected with SAR processing are also discussed.

Dicenzo, A.↗

A data compression technique for synthetic aperture radar images

A data compression technique is developed for synthetic aperture radar (SAR) imagery. The technique is based on an SAR image model and is designed to preserve the local statistics in the image by an adaptive variable rate modification of block truncation coding (BTC). A data rate of approximately 1.6 bit/pixel is achieved with the technique while maintaining the image quality and cultural (pointlike) targets. The algorithm requires no large data storage and is computationally simple.

Frost, V. S.↗

Synthetic aperture radar signal processing on the MPP

Satellite-borne Synthetic Aperture Radars (SAR) sense areas of several thousand square kilometers in seconds and transmit phase history signal data several tens of megabits per second. The Shuttle Imaging Radar-B (SIR-B) has a variable swath of 20 to 50 km and acquired data over 100 kms along track in about 13 seconds. With the simplification of separability of the reference function, the processing still requires considerable resources; high speed I/O, large memory and fast computation. Processing systems with regular hardware take hours to process one Seasat image and about one hour for a SIR-B image. Bringing this processing time closer to acquisition times requires an end-to-end system solution. For the purpose of demonstration, software was implemented on the present Massively Parallel Processor (MPP) configuration for processing Seasat and SIR-B data. The software takes advantage of the high processing speed offered by the MPP, the large Staging Buffer, and the high speed I/O between the MPP array unit and the Staging Buffer. It was found that with unoptimized Parallel Pascal code, the processing time on the MPP for a 4096 x 4096 sample subset of signal data ranges between 18 and 30.2 seconds depending on options.

Ramapriyan, H. K.↗

Real-Time Processor For Synthetic-Aperture Radar

Images formed aboard airplane. Data processor for synthetic-aperture radar (SAR) operates aboard airplane with radar equipment to produce images of scanned terrain in real time. Providing images immediately, new processor enables SAR operator to monitor scanning activity. Processed images recorded on tape or transmitted to ground stations.

Liu, Kuang Y.↗

Ice sheet studies using synthetic aperture radar

The objective was to demonstrate the utility of synthetic aperture radar in ice sheet studies. The major advantage of SAR imagery over visible imagery is the all-weather capability of radar and the ability to specify look angle. Available digital SAR imagery over ice sheets was collected and examined both qualitatively and quantitatively using corroborative data, such as LANDSAT imagery, to confirm feature identification and interpretations. A simple scattering model will be developed to assess the relative importance of surface topography, composition, and subsurface layering to the intensity of radar backscatter. Recommendations of system parameters will be made for optimal SAR operation over ice sheets.

Bindschadler, R.↗

A digital calibration method for synthetic aperture radar systems

A basic method to calibrate imagery from synthetic aperture radar (SAR) systems is presented. SAR images are calibrated by monitoring all the terms of the radar equation. This procedure includes the use of both external (calibrated reference reflectors) and internal (system-generated calibration signals) sources to monitor the total SAR system transfer function. To illustrate the implementation of the procedure, two calibrated SAR images (X-band, 3.2-cm wavelength) are presented, along with the radar cross-section measurements of specific scenes within each image. The sources of error within the SAR image calibration procedure are identified.

Larson, Richard W.↗

Topography estimation with interferometric synthetic aperture radar using fringe detection

Methods are presented for using Synthetic Aperture Radar (SAR) interferometry data to estimate surface topography. An expression is given to relate the elevation of a ground point to the phase difference of SAR images received from two spatially separated antennas. An iterative algorithm which solves for the position and elevation of each point in the image simultaneously is developed. One of the critical issues that determines the accuracy of the terrain mapping is the phase unwrapping. An approach to the problem by fringe line detection is proposed. The algorithms are tested with two Seasat SAR images of terrain near Yellowstone National Park. The resultant elevation map is compared with a USGS terrain elevation model. The error of the SAR elevation with respect to the digital terrain map is about 8.2 percent of the total terrain variation.

Lin, Qian↗

A VLSI implementation for synthetic aperture radar image processing

A simple physical model for the Synthetic Aperture Radar (SAR) is presented. This model explains the one dimensional and two dimensional nature of the received SAR signal in the range and azimuth directions. A time domain correlator, its algorithm, and features are explained. The correlator is ideally suited for VLSI implementation. A real time SAR architecture using these correlators is proposed. In the proposed architecture, the received SAR data is processed using one dimensional correlators for determining the range while two dimensional correlators are used to determine the azimuth of a target. The architecture uses only three different types of custom VLSI chips and a small amount of memory.

Premkumar, A.↗

Perceptual compression of magnitude-detected synthetic aperture radar imagery

A perceptually-based approach for compressing synthetic aperture radar (SAR) imagery is presented. Key components of the approach are a multiresolution wavelet transform, a bit allocation mask based on an empirical human visual system (HVS) model, and hybrid scalar/vector quantization. Specifically, wavelet shrinkage techniques are used to segregate wavelet transform coefficients into three components: local means, edges, and texture. Each of these three components is then quantized separately according to a perceptually-based bit allocation scheme. Wavelet coefficients associated with local means and edges are quantized using high-rate scalar quantization while texture information is quantized using low-rate vector quantization. The impact of the perceptually-based multiresolution compression algorithm on visual image quality, impulse response, and texture properties is assessed for fine-resolution magnitude-detected SAR imagery; excellent image quality is found at bit rates at or above 1 bpp along with graceful performance degradation at rates below 1 bpp.

Gorman, John D.↗

Commercial Synthetic Aperture Radar Data for Surface Deformation and Change

The commercial synthetic aperture radar (SAR) market is experiencing year-over-year growth and is currently capable of imaging anywhere in the world within an hour at X-band. Surface Deformation and Change (SDC) is a mission study at NASA to investigate innovative architectures beyond the NASA-ISRO SAR (NISAR) mission in the next decade. In this article we present preliminary findings on the technical capabilities of the currently available commercial SAR data, and investigate their applicability to SDC goals, such as imaging quality and retrieval of surface motion.

SAR↗

US East Coast synthetic aperture radar wind atlas for offshore wind energy

We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from -0.8 to 0.6 m s -1 . Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to -0.2 to 0.0 m s -1 , while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s -1 . The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s -1 for SAR and less than 0.2m s -1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

17 WIND ENERGY↗

Synthetic aperture radar system design for random field classification

An optimum design study is carried out for synthetic aperture radar systems intended for classifying randomly reflecting areas (such as agricultural fields) characterized by a reflectivity density spectral density. The problem solution is obtained, neglecting interfield interference and assuming areas of known configuration and location, as well as a certain Gaussian signal field property. The optimum processor is nonlinear, but includes conventional matched filter processing. A set of summary design curves is plotted, and is applied to the design of a satellite synthetic aperture radar system.

Harger, R. O.↗

Shuttle Imaging Radar-C mission operations - Technology test bed for Earth Observing System synthetic aperture radar

The mission operations for the Space Radar Lab (SRL), particularly in the areas of real-time replanning and science activity coordination, are presented. The two main components of SRL are the Shuttle Imaging Radar-C and the X-Band Synthetic Aperture Radar. The Earth Observing System SAR will be a multispectral, multipolarization radar satellite that will provide information over an entire decade, permitting scientists to monitor large-scale changes in the earth's environment over a long period of time.

Trimble, J. P.↗

Scheduling Mission Reconfiguration for an Interferometry Synthetic Aperture Radar Using Deep Reinforcement Learning

This paper presents a method to intelligently adapt the baseline of a synthetic aperture radar based on Deep Rein- forcement Learning to help create plans for missions that use formation flight for Earth observation purposes. The main contribution of this paper is the initial results we have found from applying the tool to a toy mission: measuring the ver- tical structure of forests by using a synthetic aperture radar mounted on a formation of 7 satellites orbiting the Earth in a Sun Synchronous Orbit. We have found that with a reward function based on expected science return over time and fuel usage, the Deep Reinforcement Learning planner is able to create plans with positive scientific returns while minimizing fuel usage. We also find that fuel usage and collision avoid- ance planning is better done with traditional methods, as Deep Reinforcement Learning does not converge to optimal solutions.

Viros-i-Martin, Antoni↗

Utah FORGE: Interferometric Synthetic Aperture Radar Data from 2023 and 2024

The dataset comprises Interferometric Synthetic Aperture Radar (InSAR) data from the TerraSAR-X and TanDEM-X satellite missions, covering the Utah FORGE site. This data includes interferometric pairs created using GMT-SAR processing software, chosen for their short orbital separations between May 1, 2023, and June 30, 2024. Included are various data and metadata, including Digital Elevation Models, unit vectors, and correlation coefficients. The dataset is packaged in several compressed tar files and formatted in NetCDF. To utilize this dataset, users will need software capable of handling NetCDF files and tools for decompressing tar files.

15 GEOTHERMAL ENERGY↗