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.
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.
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.
In recent papers the authors discussed the advantages of forming spotlight-mode SAR imagery from phase history data via a technique that is rooted in the principles of phased-array beamforming, which is closely related to back-projection. The application of a traditional autofocus algorithm, such as Phase Gradient Autofocus (PGA), requires some care in this situation. Specifically, a stated advantage of beamforming is that it easily allows for reconstruction of the SAR image onto an arbitrary imaging grid. One very useful grid, for example, is a Cartesian grid in the ground plane. Autofocus via PGA for such an image, however, cannot be performed in a straightforward manner, because in PGA a Fourier transform relationship is required between the image domain and the range-compressed phase history, and this is not the case for such an imaging grid. In this paper we propose a strategy for performing autofocus in this situation, and discuss its limitations. We demonstrate the algorithm on synthetic phase errors applied to real SAR imagery.
While the chief cause of defocus in airborne spotlight-mode imagery is uncompensated errors in the measurement of the aircraft position as it traverses the synthetic aperture, another physical phenomenon can cause blurring in the formed SAR image as well. This is the injection of phase errors into the collected SAR phase history data by random fluctuations in the index of refraction as the microwave pulses propagate through an atmosphere that contains irregularities in the tropospheric water vapor distribution. In this paper, we show that in SAR imagery collected under certain conditions, these phase errors can be detected and corrected using a robust autofocus algorithm such as Phase Gradient Autofocus (PGA). The phase errors are confirmed as having been propagation-induced by demonstrating that they exhibit a power-law spectrum described by Tatarski, based on the turbulence model of Kolmogorov.
The convolution/back-projection (CBP) algorithm has recently once again been touted as the "gold standard" for spotlight-mode SAR image formation, as it is proclaimed to achieve better image quality than the well-known and often employed polar formatting algorithm (PFA) 1. In addition, it has been suggested that PFA is less flexible than CBP in that PFA can only compute the SAR image on one grid and PFA cannot add or subtract pulses from the imaging process. The argument for CBP acknowledges the computational burden of CBP compared to PFA, but asserts that the increased image accuracy and flexibility of the formation process is warranted, at least in some imaging scenarios. Because CBP can now be sped up by the proper algorithm design, it becomes, according to this line of analysis, the clear algorithm of choice for SAR image formation. In this paper we reject the above conclusion by showing that PFA and CBP achieve the same image quality, and that PFA has complete flexibility, including choice of imaging plane, size of illuminated beam area to be imaged, resolution of the image, and others. We demonstrate these claims via formation of both simulated and real SAR imagery using both algorithms.
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.
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.