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Computer analysis of ERS-1 and RADARSAT ScanSAR narrow images of Great Lakes ice cover using a supervised classification technique indicates that different ice types in the ice cover can be identified and mapped and that wind speed and direction can have a strong influence on the backscatter from open water.
Several algorithms implement improved method of segmenting highly speckled, high-resolution, complex-amplitude synthetic-aperture-radar (SAR) digitized images into regions, within each backscattering characteristics similar or homogeneous from place to place. Method provides for approximate, deterministic solution by two alternative algorithms almost always converging to local minimums: one, Iterative Conditional Modes (ICM) algorithm, which locally maximizes posterior probability density of region labels; other, Maximum Posterior Marginal (MPM) algorithm, which maximizes posterior marginal density of region labels at each pixel location. ICM algorithm optimizes reconstruction of underlying scene. MPM algorithm minimizes expected number of misclassified pixels, possibly better in remote sensing of natural scenes.
A method of extracting sea height information from Synthetic Aperture Radar (SAR) complex data was studied. A fundamental SAR ocean imaging model for gravity waves showed that information about the long wave is present in the SAR complex data, especially its phase. Phase demodulation algorithm followed by linear regression and filtering was employed. Only the latter two steps incorporated a priori information that might be available. A relatively simplified simulation indicated that the finite bandwidth of the SAR system imposed the apparently most serious limitation. A preliminary application to SEASAT-SAR complex imagery was encouraging. Information about the long wave, if incorporated into a more sophisticated phase demodulation structure, i.e., at an earlier point in the algorithm, could conceivably mitigate this bandwidth limitation.
An unsupervised clustering algorithm is used to segment multifrequency polarimetric radar data from the NASA/JPL airborne SAR (synthetic aperture radar). Twenty-two parameters are evaluated for their discriminatory capability for each pixel of an image. A clustering analysis is then performed using different subsets of these parameters. This analysis relies on data taken as part of an intensive field experiment during the summer of 1988 in the vicinity of the Pisgah lava flow in the Mojave Desert in southern California. As part of the experiment, extensive ground truth was acquired, including dielectric constant and topography measurements. Segmentation results show good agreement with these measurements.
Errors in the determination of the platform state vector (position and velocity) limit the accuracy with which individual pixels in a SAR (synthetic aperture radar) image can be located. A method of updating the state vector based on the SAR data is described. The algorithm combines reference targets at known locations with the estimates available from the mission control center, utilizing a maximum likelihood estimator. The targets can be located at calibration sites or be easily recognizable targets of opportunity. The accuracy of the update depends on the number of targets, their geometry, and the accuracy of their position determination. The algorithm is tested using Seasat SAR data. High-accuracy state vectors were perturbed by Gaussian distributed errors with standard deviations and cross-correlations based on worst-case expectations for SIR-C (Shuttle Imaging Radar). Tests using ground targets located in topographic maps (1:24,000) showed that state vector position errors on the order of 300-400 m and velocity accuracies of 2-3 m/s are readily achievable.
The geological data content of Seasat A SAR imagery was assessed by correlating images of the Southern Appalachians with optical and digital techniques using a digital enhancement algorithm. The evaluation was performed in terms of lithology, lineaments, and geological structure. Digital correlation of the images was found to be more effective than optical correlation as a geological mapping instrument when considered in the light of ground truth data. The digital enhancement algorithm consists of a mean square error analysis which preserves the edge structure in the SAR imagery and decreases the noise content. Additionally, digital correlations allowed for faster computer processing of the imagery.
Remote sensing with synthetic aperture radars (SAR's) is a rapidly developing field. Calibration of these sensors is required for the establishment of relationships between radar backscatter and geographical parameters. A review of recent progress in SAR calibration is presented. The quantities measured by SAR are defined and mathematical formulations of the three basic types of SAR images are developed. The establishment of scientific requirements for calibration and the difficulties involved are discussed. Image quality assessment is reviewed and the problems of radiometric calibration of SAR images using the radar equation and internal and external approaches are considered. Polarimetric radar calibration and the development of the necessary algorithms are described. Interferometric phase calibration and its associated problems are reviewed and future challenges in SAR calibration are discussed.
Digital processors for spaceborne imaging radars and application of the technology developed for airborne SAR systems are considered. Transferring algorithms and implementation techniques from airborne to spaceborne SAR processors offers obvious advantages. The following topics are discussed: (1) a quantification of the differences in processing algorithms for airborne and spaceborne SARs; and (2) an overview of three processors for airborne SAR systems.
This paper describes a technique to generate geocoded synthetic aperture radar (SAR) imagery corrected for terrain induced geometric distortions. This algorithm transforms the raw slant range image, generated by the signal processor, into a map registered product, resampled to either Universal Transverse Mercator (UTM) or Polar Stereographic projections, and corrected for foreshortening. The technique utilizes the space platform trajectory information in conjunction with a digital elevation map (DEM) of the target area to generate an ortho-radar map with near-autonomous operation. The current procedure requires only two to three tie-points to compensate for the platform position uncertainty that results in translational error between the image and the DEM. This approach is unique in that it does not require generation of a simulated radar image from the DEM or a grid of tie-points to characterize the image-to-map distortions. Rather, it models the inherent distortions based on knowledge of the radar data collection characteristics, the signal Doppler parameters, and the local terrain height to automatically predict the registration transformation. This algorithm has been implemented on a minicomputer system equipped with an array processor and a large random-access memory to optimize the throughput.
Active and passive microwave data collected during the 1984 summer Marginal Ice Zone Experiment in the Fram Strait (MIZEX 84) are used to compare ice concentration estimates derived from synthetic aperture radar (SAR) data to those obtained from passive microwave imagery at several frequencies. The comparison is carried out to evaluate SAR performance against the more established passive microwave technique, and to investigate discrepancies in terms of how ice surface conditions, imaging geometry, and choice of algorithm parameters affect each sensor. Active and passive estimates of ice concentration agree on average to within 12%. Estimates from the multichannel passive microwave data show best agreement with the SAR estimates because the multichannel algorithm effectively accounts for the range in ice floe brightness temperatures observed in the MIZ.
This paper summarizes the algorithm and hardware selection phases of the ScanSAR Processor (SSP) and Precision Processor (PP) implementation task for the Alaska SAR Facility (ASF). The SSP is being designed to specifically process RADARSAT ScanSAR mode SAR data while the PP is being designed to produce high precision image products from continuous mode SAR data from RADARSAT as well as ERS-1,2 and JERS-1. This paper describes the algorithms selected for the SSP and the PP; and reports on the hardware selection process in arriving at the target computing platform for these processors.
The Point Scattering Method of simulating radar imagery rigorously models all aspects of the imaging radar phenomena. Its computational algorithms operate on a symbolic representation of the terrain test site to calculate such parameters as range, angle of incidence, resolution cell size, etc. Empirical backscatter data and elevation data are utilized to model the terrain. Additionally, the important geometrical/propagation effects such as shadow, foreshortening, layover, and local angle of incidence are rigorously treated. Applications of radar image simulation to a proposed calibrated SAR system are highlighted: soil moisture detection and vegetation discrimination.
Precise, densely sampled maps of ice motion have been derived from digital Seasat SAR imagery, in order to determine the small scale spatial variability of ice formation. The SAR images were processed to remove geometric distortions, and then located to an accuracy of about 100 m by means of a spacecraft orbital data/SAR characteristics algorithm, independently of attitude and ground reference point data. Ice features common to an overlapping pair of images yielded vector plots of ice motion that indicate a high degree of spatial deformation, demonstrating the potential value of spaceborne SAR data.
We present a real-time high-performance and fault-tolerant FPGA-based hardware architecture for the processing of synthetic aperture radar (SAR) images in future spaceborne system. In particular, we will discuss the integrated design approach, from top-level algorithm specifications and system requirements, design methodology, functional verification and performance validation, down to hardware design and implementation.
A real-time high-performance and fault-tolerant FPGA-based hardware architecture for the processing of synthetic aperture radar (SAR) images has been developed for advanced spaceborne radar imaging systems. In this paper, we present the integrated design approach, from top-level algorithm specifications, system architectures, design methodology, functional verification, performance validation, down to hardware design and implementation.
Segmentation of multifrequency, multilook synthetic-aperture radar (SAR) image intensity data into regions, within each of which backscattering characteristics of target scene considered homogeneous, enhanced by use of two statistical models. One represents statistics of multifrequency, multilook speckled intensities of SAR picture elements; other represents statistics of labels applied to regions into which picture elements grouped. Each region represents different type of terrain, terrain cover, or other surface; e.g., forest, agricultural land, sea ice, or water. Segmentation of image into regions of neighboring picture elements accomplished by method similar to that described in "Algorithms For Segmentation Of Complex-Amplitude SAR Data" (NPO-18524).
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.
The problem of an ultrasonic reflectivity tomography is similar to that of a spotlight-mode aircraft Synthetic Aperture Radar (SAR) system. The analysis for a circular path spotlight mode SAR in this paper leads to the insight of the system characteristics. It indicates that such a system when operated in a wide bandwidth is capable of achieving the ultimate resolution; one quarter of the wavelength of the carrier frequency. An efficient processing algorithm based on the exact two dimensional spectrum is presented. The results of simulation indicate that the impulse responses meet the predicted resolution performance. Compared to an algorithm previously developed for the ultrasonic reflectivity tomography, the throughput rate of this algorithm is about ten times higher.