Publications

Abstract not provided.

In past research, two-pass repeat-geometry synthetic aperture radar (SAR) coherent change detection (CCD) predominantly utilized the sample degree of coherence as a measure of the temporal change occurring between two complex-valued image collects. Previous coherence-based CCD approaches tend to show temporal change when there is none in areas of the image that have a low clutter-to-noise power ratio. Instead of employing the sample coherence magnitude as a change metric, in this paper, we derive a new maximum-likelihood (ML) temporal change estimate-the complex reflectance change detection (CRCD) metric to be used for SAR coherent temporal change detection. The new CRCD estimator is a surprisingly simple expression, easy to implement, and optimal in the ML sense. This new estimate produces improved results in the coherent pair collects that we have tested.

In previous research, two-pass repeat-geometry synthetic aperture radar (SAR) coherent change detection (CCD) predominantly utilized the sample degree of coherence as a measure of the temporal change occurring between two complex-valued image collects. Previous coherence-based CCD approaches tend to show temporal change when there is none in areas of the image that have a low clutter-to-noise power ratio. Instead of employing the sample coherence magnitude as a change metric, in this paper, we derive a new maximum-likelihood (ML) temporal change estimate—the complex reflectance change detection (CRCD) metric to be used for SAR coherent temporal change detection. The new CRCD estimator is a surprisingly simple expression, easy to implement, and optimal in the ML sense. As a result, this new estimate produces improved results in the coherent pair collects that we have tested.

Typical synthetic aperture RADAR (SAR) imaging employs a co-located RADAR transmitter and receiver. Bistatic SAR imaging separates the transmitter and receiver locations. A bistatic SAR configuration allows for the transmitter and receiver(s) to be in a variety of geometric alignments. Sandia National Laboratories (SNL) / New Mexico proposed the deployment of a ground-based RADAR receiver. This RADAR receiver was coupled with the capability of digitizing and recording the signal collected. SNL proposed the possibility of creating an image of targets the illuminating SAR observes. This document describes the developed hardware, software, bistatic SAR configuration, and its deployment to test the concept of a ground-based bistatic SAR. In the proof-of-concept experiments herein, the RADAR transmitter will be a commercial SAR satellite and the RADAR receiver will be deployed at ground level, observing and capturing RADAR ground/targets illuminated by the satellite system.

While typical SAR imaging employs a co-located (monostatic) RADAR transmitter and receiver, bistatic SAR imaging separates the transmitter and receiver locations. The transmitter and receiver geometry determines if the scattered signal is back scatter, forward scatter, or side scatter. The monostatic SAR image is backscatter. Therefore, depending on the transmitter/receiver collection geometry, the captured imagery may be quite different that that sensed at the monostatic SAR. This document presents imagery and image products formed from captured signals during the validation stage of the bistatic SAR research. Image quality and image characteristics are discussed first. Then image products such as two-color multi-view (2CMV) and coherent change detection (CCD) are presented.

This report describes the significant processing steps that were used to take the raw recorded digitized signals from the bistatic synthetic aperture RADAR (SAR) hardware built for the NCNS Bistatic SAR project to a final bistatic SAR image. In general, the process steps herein are applicable to bistatic SAR signals that include the direct-path signal and the reflected signal. The steps include preprocessing steps, data extraction to for a phase history, and finally, image format. Various plots and values will be shown at most steps to illustrate the processing for a bistatic COSMO SkyMed collection gathered on June 10, 2013 on Kirtland Air Force Base, New Mexico.

In this paper, we derive a new optimal change metric to be used in synthetic aperture RADAR (SAR) coherent change detection (CCD). Previous CCD methods tend to produce false alarm states (showing change when there is none) in areas of the image that have a low clutter-to-noise power ratio (CNR). The new estimator does not suffer from this shortcoming. It is a surprisingly simple expression, easy to implement, and is optimal in the maximum-likelihood (ML) sense. The estimator produces very impressive results on the CCD collects that we have tested.

Abstract not provided.

Abstract not provided.

Beamforming is a methodology for collection-mode-independent SAR image formation. It is essentially equivalent to backprojection. The authors have in previous papers developed this idea and discussed the advantages and disadvantages of the approach to monostatic SAR image formation vis-à-vis the more standard and time-tested polar formatting algorithm (PFA). In this paper we show that beamforming for bistatic SAR imaging leads again to a very simple image formation algorithm that requires a minimal number of lines of code and that allows the image to be directly formed onto a three-dimensional surface model, thus automatically creating an orthorectified image. The same disadvantage of beamforming applied to monostatic SAR imaging applies to the bistatic case, however, in that the execution time for the beamforming algorithm is quite long compared to that of PFA. Fast versions of beamforming do exist to help alleviate this issue. Results of image reconstructions from phase history data are presented. © 2010 Copyright SPIE - The International Society for Optical Engineering.

In this paper we show that the technique for spotlight-mode SAR image formation generally known as "backprojection" or "time- domain" is most easily derived and described in terms of the well-known methods of phased-array beamforming. By contrast, backprojection has been typically developed via analogy to tomographic imaging [1], which restricts this technique to the case of planar wavefronts. We demonstrate how the very simple notion of delay-and-sum beamforming leads directly to the backprojection algorithm for SAR, including the case for curved wavefronts. We further explain why backprojection offers a certain elegant simplicity for SAR imaging, and allows direct one-step computation of several useful SAR products, including an orthographically correct image free of any geometric or defocus effects from wavefront curvature and also free of the effects of terrain-elevation-induced defocus. (This product requires as an input a pre-existing digital elevation map (DEM) of the scene to be imaged.) In addition, we'll demonstrate why beamforming yields a mode-independent SAR image formation algorithm, i.e. one that can just as easily accommodate strip-map or spotlight-mode phase histories collected on an arbitrary flight path.

Abstract not provided.

Coherent stereo pairs from cross-track synthetic aperture radar (SAR) collects allow fully automated correlation matching using magnitude and phase data. Yet, automated feature matching (correspondence) becomes more difficult when imaging rugged terrain utilizing large stereo crossing angle geometries because high-relief features can undergo significant spatial distortions. These distortions sometimes cause traditional, shift-only correlation matching to fail. This paper presents a possible solution addressing this difficulty. Changing the complex correlation maximization search from shift-only to shift-and-scaling using the downhill simplex method results in higher correlation. This is shown on eight coherent spotlight-mode cross-track stereo pairs with stereo crossing angles averaging 93.7° collected over terrain with slopes greater than 20°. The resulting digital elevation maps (DEMs) are compared to ground truth. Using the shift-scaling correlation approach to calculate disparity, height errors decrease and the number of reliable DEM posts increase.

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Useful products generated from interferometric synthetic aperture radar (IFSAR) complex data include height measurement, coherent change detection, and classification. The IFSAR coherence is a spatial measure of complex correlation between two collects, a product of IFSAR signal processing. A tacit assumption in such IFSAR signal processing is that the terrain height is constant across an averaging box used in the process of correlating the two images. This paper presents simulations of IFSAR coherence if two targets with different heights exist in a given correlation cell, a condition in IFSAR collections produced by layover. It also includes airborne IFSAR data confirming the simulation results. The paper concludes by exploring the implications of the results on IFSAR height measurements and classification.