SAR imaging algorithms: innovations and issues
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SAR SAR imaging algorithms algorithms↗
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Object-oriented feature-tracking algorithms for SAR images of the marginal ice zone
An unsupervised method that chooses and applies the most appropriate tracking algorithm from among different sea-ice tracking algorithms is reported. In contrast to current unsupervised methods, this method chooses and applies an algorithm by partially examining a sequential image pair to draw inferences about what was examined. Based on these inferences the reported method subsequently chooses which algorithm to apply to specific areas of the image pair where that algorithm should work best.
Daida, Jason↗
Classification Of Terrain In Polarimetric SAR Images
Two algorithms processing polarimetric synthetic-aperture-radar data found effective in assigning various parts of SAR images to classes representing different types of terrain. Partially automate interpretation of SAR imagery, reducing amount of photointerpretation needed and putting whole interpretation process on more quantitative and systematic basis. First algorithm implements Bayesian classification scheme "supervised" by use of training data. Second algorithm implements classification procedure unsupervised.
Van Zyl, Jakob J.↗
Improved Radiometric Correction For SAR Images
Correction algorithm reversible and applicable to both detected and complex images. Method described accounts for major effects of transmitter, antenna, receiver, and certain aspects of SAR signal processing upon calibration and provides for appropriate reversible corrections applied in processing. Applicable to both detected (amplitude-only) and complex (amplitude and phase) SAR image data and provides for optional subtraction of estimated noise floor from image.
Freeman, Anthony↗
Information extraction and transmission techniques for spaceborne synthetic aperture radar images
Information extraction and transmission techniques for synthetic aperture radar (SAR) imagery were investigated. Four interrelated problems were addressed. An optimal tonal SAR image classification algorithm was developed and evaluated. A data compression technique was developed for SAR imagery which is simple and provides a 5:1 compression with acceptable image quality. An optimal textural edge detector was developed. Several SAR image enhancement algorithms have been proposed. The effectiveness of each algorithm was compared quantitatively.
Frost, V. S.↗
A new clustering algorithm applicable to multispectral and polarimetric SAR images
We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.
Wong, Yiu-Fai↗
Digital processing considerations for extraction of ocean wave image spectra from raw synthetic aperture radar data
The digital processing requirements of several algorithms for extracting the spectrum of a detected synthetic aperture radar (SAR) image from the raw SAR data are described and compared. The most efficient algorithms for image spectrum extraction from raw SAR data appear to be those containing an intermediate image formation step. It is shown that a recently developed compact formulation of the image spectrum in terms of the raw data is computationally inefficient when evaluated directly, in comparison with the classical method where matched-filter image formation is an intermediate result. It is also shown that a proposed indirect procedure for digitally implementing the same compact formulation is somewhat more efficient than the classical matched-filtering approach. However, this indirect procedure includes the image formation process as part of the total algorithm. Indeed, the computational savings afforded by the indirect implementation are identical to those obtained in SAR image formation processing when the matched-filtering algorithm is replaced by the well-known 'dechirp-Fourier transform' technique. Furthermore, corrections to account for slant-to-ground range conversion, spherical earth, etc., are often best implemented in the image domain, making intermediate image formation a valuable processing feature.
Lahaie, I. J.↗
Phi-s correlation and dynamic time warping - Two methods for tracking ice floes in SAR images
The authors present two algorithms for performing shape matching on ice floe boundaries in SAR (synthetic aperture radar) images. These algorithms quickly produce a set of ice motion and rotation vectors that can be used to guide a pixel value correlator. The algorithms match a shape descriptor known as the Phi-s curve. The first algorithm uses normalized correlation to match the Phi-s curves, while the second uses dynamic programming to compute an elastic match that better accommodates ice floe deformation. Some empirical data on the performance of the algorithms on Seasat SAR images are presented.
Mcconnell, Ross↗
Estimation of Bare Surface Soil Moisture and Surface Roughness Parameter Using L-Band SAR Image Data
An algorithm based on a fit of the single-scattering Integral Equation Method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well- managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively.
Shi, Jian-Cheng↗
Estimation of Bare Surface Soil Moisture and Surface Roughness Parameter Using L-Band SAR Image Data
An algorithm based on a fit of the single-scattering Integral Equation Method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quasi-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well-managed watershed in southwest Oklahoma. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively.
Shi, Jian-Cheng↗
Registration of interferometric SAR images
Interferometric synthetic aperture radar (INSAR) is a new way of performing topography mapping. Among the factors critical to mapping accuracy is the registration of the complex SAR images from repeated orbits. A new algorithm for registering interferometric SAR images is presented. A new figure of merit, the average fluctuation function of the phase difference image, is proposed to evaluate the fringe pattern quality. The process of adjusting the registration parameters according to the fringe pattern quality is optimized through a downhill simplex minimization algorithm. The results of applying the proposed algorithm to register two pairs of Seasat SAR images with a short baseline (75 m) and a long baseline (500 m) are shown. It is found that the average fluctuation function is a very stable measure of fringe pattern quality allowing very accurate registration.
Lin, Qian↗
Modified polar format algorithm for processing spaceborne SAR data
This paper describes a modified polar format algorithm for SAR image-formation processing. the algorithm focuses raw data assuming a spherical reference surface (ground surface), and, unlike standard polar format algorithms, it assumes spherical rather than planar signal wavefronts.
polar format processing↗
An Automated Matching Algorithm for Magellan Stereo Data
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magellan↗
Analog In-Memory Computing for the Synthetic Aperture Radar Polar Format Algorithm
As the utility of synthetic aperture radar (SAR) systems increases in autonomous vehicles, satellites, and other power- and space-constrained edge applications, there is a growing need for processors that can form SAR images at low power. In recent years, analog in-memory compute (AIMC) has shown immense promise for accelerating neural networks and other matrix-vector multiplication (MVM) heavy workloads at the edge. Here, in this work, we examine how the polar format algorithm (PFA), a popular SAR image formation algorithm, can be mapped to these AIMC systems. The PFA maps readily onto analog MVMs because it primarily consists of two linear operations: interpolation of frequency-domain data to a Cartesian grid, followed by a 2-D Fourier transform. This work presents two approaches to map the interpolation operation onto MVMs in analog hardware: a chirp transform and a modified form of sinc interpolation. These mappings introduce algorithmic errors, and their effect on the quality of SAR image formation is examined, both quantitatively and qualitatively. In addition, the impact of errors introduced by the analog hardware is explored to determine which approach is optimal under varying assumptions about the underlying analog memory devices and circuits.
Analog computing↗
Tracking ocean wave spectrum from SAR images
An end to end algorithm for recovery of ocean wave spectral peaks from Synthetic Aperture Radar (SAR) images is described. Current approaches allow precisions of 1 percent in wave number, and 0.6 deg in direction.
Goldfinger, A. D.↗
Reducing Speckle In One-Look SAR Images
Local-adaptive-filter algorithm incorporated into digital processing of synthetic-aperture-radar (SAR) echo data to reduce speckle in resulting imagery. Involves use of image statistics in vicinity of each picture element, in conjunction with original intensity of element, to estimate brightness more nearly proportional to true radar reflectance of corresponding target. Increases ratio of signal to speckle noise without substantial degradation of resolution common to multilook SAR images. Adapts to local variations of statistics within scene, preserving subtle details. Computationally simple. Lends itself to parallel processing of different segments of image, making possible increased throughput.
Nathan, K. S.↗
Analysis and an Image Recovery Algorithm for Ultrasonic Tomography System
The analysis for a circular path spotlight mode SAR in this paper leads to the insight of the system characterics.
SAR↗
Synthetic aperture radar target detection, feature extraction, and image formation techniques
This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images.
Li, Jian↗