This PDF file contains the front matter associated with SPIE Proceedings Volume 8394, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Synthetic aperture radar (SAR) imaging is a powerful tool that can be utilized where other conventional surveillance methods fail. It has a variety of applications including reconnaissance and surveillance for defense purposes, natural resource exploration, and environmental monitoring, among others. SAR systems generally create large datasets that need to be processed to form a final image. Processing this data can be computationally intensive, and applications may demand algorithms that can form images quickly. The goal and motivation of this research is to analyze algorithms that permit a large SAR dataset to be efficiently processed into a high-resolution image of a large scene. The backprojection algorithm (BPA)1 can serve as a baseline for performance relative to other SAR imaging algorithms. It results in accurately formed images for a vast variety of imaging scenarios. The tradeoff comes in its computational complexity which is O(N³) for an N × N pixel image. The polar format algorithm (PFA⁾² is a long-standing and popular alternative to the BPA. The PFA allows the use of fast Fourier Transforms (FFTs), leading to a computational complexity of O(N2 logN) for an N × N pixel image. However, the PFA relies on a far-field approximation, wherein the curved wavefront of the transmitted pulses is approximated as a planar wavefront, thereby introducing spatially variant phase errors and hence distortion and defocus in the PFA formed image. The defocus and distortion errors can be corrected, but this is a non-trivial process.3 It can be shown that first-order Taylor expansion of a differential range expression yields the assumed received signal phase used to generate images from SAR phase history data with the PFA.4 This work focuses on error terms introduced by the PFA assumption that introduce geometric distortion in the resulting image. This distortion causes a point scatterer located at a true (x, y) coordinate to appear at some (x, y) in the formed image, i.e, unwanted translation of point target locations is introduced. Complicating matters, the distortion is a function of a pixel's coordinates in the scene, thus making the distortion spatially-variant such that each pixel will be distorted differently. This is often referred to as an image warping. Previously, it has been assumed that the second-order Taylor series of the differential range defines the dominant error,2, 4, 5 due to the factorial decay of the Taylor series. This assumption is tested here by performing a Taylor expansion on a differential range error expression. Instead of assuming the second-order differential range expansion term to be the sole source of error, the true error term is used to approximate the distortion. The results of this comparison are presented. The differential range error approach will be referred to as the DRE approach and the dominant polynomial approach as the DPE. Additionally, with an accurate distortion approximation, it has been shown that the distortion can be removed in post-processing.3 With this in mind, bounds on scene size are derived limiting the visible distortion to within an arbitary number of resolution cells, both before and after the second-order distortion correction. These bounds are also verified in simulation. The paper is outlined as follows. In Section 2, we will first introduce the differential range term and demonstrate its relationship to the PFA imaging kernel and the source of the phase error terms. Next in Section 3, the distortion functions will be derived from these error terms using both the DRE and DPE approaches before and after applying the second-corrections. Then in Section 4, these results will be bounded such that the worst-case distortion at a specific pixel in the scene is within an arbitrary number of resolution cells, giving an approximated distortion-free scene size. Finally in Section 5, the results and comparison of the approaches will be presented.

The Dual Format Algorithm (DFA) is an alternative to the Polar Format Algorithm (PFA) where the image is formed first to an arbitrary grid instead of a Cartesian grid. The arbitrary grid is specifically chosen to allow for more efficient application of defocus and distortion corrections that occur due to range curvature. We provide a description of the arbitrary image grid and show that the quadratic phase errors are isolated along a single dimension of the image. We describe an application of the DFA to circular SAR data and analyze the image focus. For an example SAR dataset, the DFA doubles the focused image size of the PFA algorithm with post imaging corrections.

An investigation was made into the feasibility of compressing complex Synthetic Aperture Radar (SAR) images using MatrixView^TM compression technology to achieve higher compression ratios than previously achieved. Complex SAR images contain both amplitude and phase information that are severely degraded with traditional compression techniques. This phase and amplitude information allows interferometric analysis to detect minute changes between pairs of SAR images, but is highly sensitive to any degradation in image quality. This sensitivity provides a measure to compare capabilities of different compression technologies. The interferometric process of Coherent Change Detection (CCD) is acutely sensitive to any quality loss and, therefore, is a good measure by which to compare compression capabilities of different technologies. The best compression that could be achieved by block adaptive quantization (a classical compression approach) applied to a set of I and Q phased-history samples, was a Compression Ratio (CR) of 2x. Work by Novak and Frost [3] increased this CR to 3-4x using a more complex wavelet-based Set Partitioning In Hierarchical Trees (SPIHT) algorithm (similar in its core to JPEG 2000). In each evaluation as the CR increased, degradation occurred in the reconstituted image measured by the CCD image coherence. The maximum compression was determined at the point the CCD image coherence remained > 0.9. The same investigation approach using equivalent sample data sets was performed using an emerging technology and product called MatrixView^TM. This paper documents preliminary results of MatrixView's compression of an equivalent data set to demonstrate a CR of 10-12x with an equivalent CCD coherence level of >0.9: a 300-400% improvement over SPIHT.

We investigate the usage of an adaptive method, the Iterative Adaptive Approach (IAA), in combination with a maximum a posteriori (MAP) estimate to reconstruct high resolution SAR images that are both sparse and accurate. IAA is a nonparametric weighted least squares algorithm that is robust and user parameter-free. IAA has been shown to reconstruct SAR images with excellent side lobes suppression and high resolution enhancement. We first reconstruct the SAR images using IAA, and then we enforce sparsity by using MAP with a sparsity inducing prior. By coupling these two methods, we can produce a sparse and accurate high resolution image that are conducive for feature extractions and target classification applications. In addition, we show how IAA can be made computationally efficient without sacrificing accuracies, a desirable property for SAR applications where the size of the problems is quite large. We demonstrate the success of our approach using the Air Force Research Lab's "Gotcha Volumetric SAR Data Set Version 1.0" challenge dataset. Via the widely used FFT, individual vehicles contained in the scene are barely recognizable due to the poor resolution and high side lobe nature of FFT. However with our approach clear edges, boundaries, and textures of the vehicles are obtained.

In this paper we apply a sparse signal recovery technique for synthetic aperture radar (SAR) image formation from interrupted phase history data. Timeline constraints imposed on multi-function modern radars result in interrupted SAR data collection, which in turn leads to corrupted imagery that degrades reliable change detection. In this paper we extrapolate the missing data by applying the basis pursuit denoising algorithm (BPDN) in the image formation step, effectively, modeling the SAR scene as sparse. We investigate the effects of regular and random interruptions on the SAR point spread function (PSF), as well as on the quality of both coherent (CCD) and non-coherent (NCCD) change detection. We contrast the sparse reconstruction to the matched filter (MF) method, implemented via Fourier processing with missing data set to zero. To illustrate the capabilities of the gap-filling sparse reconstruction algorithm, we evaluate change detection performance using a pair of images from the GOTCHA data set.

Developers have been increasingly successful at installing SAR systems on small UAVs. Within that domain, there is a continuous drive to make systems smaller and cheaper in order to minimize the value of assets placed at risk. We would therefore like to reduce the SAR system hardware to the minimal complement required to form a useful image. As inertial navigation/motion units can signicantly add to platform size, weight, and cost, developing data-adaptive approaches to form useful imagery with minimal navigational inputs is desirous. This paper outlines a challenge problem to focus research eorts towards that end.

We describe an approach to autofocusing for large apertures on curved SAR trajectories. It is a phase-gradient type method in which phase corrections compensating trajectory perturbations are estimated not directly from the image itself, but rather on the basis of partial" SAR data { functions of the slow and fast times { recon- structed (by an appropriate forward-projection procedure) from windowed scene patches, of sizes comparable to distances between distinct targets or localized features of the scene. The resulting partial data" can be shown to contain the same information on the phase perturbations as that in the original data, provided the frequencies of the perturbations do not exceed a quantity proportional to the patch size. The algorithm uses as input a sequence of conventional scene images based on moderate-size subapertures constituting the full aperture for which the phase corrections are to be determined. The subaperture images are formed with pixel sizes comparable to the range resolution which, for the optimal subaperture size, should be also approximately equal the cross-range resolution. The method does not restrict the size or shape of the synthetic aperture and can be incorporated in the data collection process in persistent sensing scenarios. The algorithm has been tested on the publicly available set of GOTCHA data, intentionally corrupted by random-walk-type trajectory uctuations (a possible model of errors caused by imprecise inertial navigation system readings) of maximum frequencies compatible with the selected patch size. It was able to eciently remove image corruption for apertures of sizes up to 360 degrees.

We consider a mono-static synthetic aperture radar (SAR) system ying over a scene of interest, making multiple visits. During each visit, antenna is traversing a dierent arbitrary, but known trajectory. Therefore, the dierence in the positions of the antennas, which we refer to as the baseline vector, is arbitrary and changes at each visit. Our objective is to estimate the displacement in the ground topography and reconstruct the scene radiance. We perform a spatio-temporal correlation of the received signals measured by each antenna. This results in a novel model that relates the correlated signal to the displacement. Next, we estimate the displacement in the ground topography and reconstruct the radiance of the scene by using a ltered-backprojection (FBP) - type method combined with an entropy minimization technique. Finally, we present numerical experiments to demonstrate the performance of the proposed method.

Monostatic Synthetic Aperture Radar (SAR) Coherent Change Detection (CCD) has been found to be of great utility in detecting changes that occur on the ground. Detectable changes of interest include vehicle tracks and water flow. The CCD procedure involves performing repeat pass radar collections, to form a coherence product, where ground disturbances can induce detectable incoherence. However there is usually a difference in the radar collection geometry which can lead to incoherent energy noise entering the CCD, which reduces the detectability of tracks. When sensing flat terrain, the incoherence due to collection geometry difference can be removed through a conventional Fourier image support trimming process. However, it has been found that when the terrain contains non-flat topography, the optimal trimming process is substantially more involved, so much so that a new per-pixel SAR back-projection imaging algorithm has been developed. This algorithm trims off incoherent energy on a per-pixel basis according to the local topography. In order to validate the bistatic SAR generalization to the monostatic per-pixel formalism and algorithm, bistatic change detection measurements were conducted with the GB-SAR system, and these are reported here.

We have employed the Arecibo Observatory Planetary Radar (AO) transmitter and the Mini-RF radar onboard NASA's Lunar Reconnaissance Orbiter (LRO) as a receiver to collect bistatic data of the lunar surface. In this paper, we demonstrate the ability to form bistatic polarimetric imagery with spatial resolution on the order of 50m, and to create polarimetric maps that could potentially reveal the presence of ice in lunar permanently shadowed craters. We discuss the details of the signal processing techniques that are required to allow these products to be formed.

In this paper we present a method for imaging ground moving targets using passive synthetic aperture radar. A passive radar imaging system uses small, mobile receivers that do not radiate any energy. For these reasons, passive imaging systems result in signicant cost, manufacturing, and stealth advantages. The received signals are obtained by multiple airborne receivers collecting scattered waves due to illuminating sources of opportunity such as commercial television, radio, and cell phone towers. We describe a novel forward model and a corresponding ltered-backprojection type image reconstruction method combined with entropy optimization. Our method determines the location and velocity of multiple targets moving at dierent velocities. Furthermore, it can accommodate arbitrary imaging geometries. we present numerical simulations to verify the imaging method.

This paper describes a method for accurately geo-locating moving targets using three-channel SAR-based GMTI interferometry. The main goals in GMTI processing are moving target detection and geo-location. In a 2011 SPIE paper we showed that reliable target detection is possible using two-channel interferometry, even in the presence of main-beam clutter. Unfortunately, accurate geo-location is problematic when using two-channel interferometry, since azimuth estimation is corrupted by interfering clutter. However, we show here that by performing three-channel processing in an appropriate sequence, clutter effects can be diminished and significant improvement can be obtained in geo-location accuracy. The method described here is similar to an existing technique known as Clutter Suppression Interferometry (CSI), although there are new aspects of our implementation. The main contribution of this paper is the mathematical discussion, which explains in a straightforward manner why three-channel CSI outperforms standard two-channel interferometry when target signatures are embedded in main-beam clutter. Also, to our knowledge this paper presents the first results of CSI applied to the Gotcha Challange data set, collected using an X-band circular SAR system in an urban environment.

Tracking prominent scatterers provides a mechanism for scene-derived motion compensation of Synthetic Aperture Radar (SAR) data. Such a process is useful in environments where GPS is unavailable and a lack of precise sensor position data makes standard motion compensation difficult. Our approach to sensor positioning estimates range histories of multiple isolated scatterers with high accuracy, then performs a geometric inversion to locate the scatterers in three dimensions and estimate the platform's motion. For high-accuracy scatterer range tracking, we first detect prominent scatterers using a CFAR criterion automatic algorithm and then track them with a two-input Kalman Filter (KF) operation. These two steps provide accurate range estimates of multiple scatterers over a sequence of SAR pulses. The KF state space is range and range-rate. We derive data inputs to the KF algorithm from multiple SAR pulses, divided into Coherent Processing Intervals (CPI). Within each CPI, individual scatterer peak amplitudes and phases are available to the algorithm. Our approach to scene-derived motion compensation combines the high accuracy range history estimates with a novel three-dimensional geometric inversion. This geometric inversion uses the range histories to estimate both 3D scatterer location and 3D relative motions of the radar. We illustrate our KF-based approach to high-accuracy tracking and demonstrate its application to estimating scene scatterer locations on synthetic and real collected SAR data.

We present a novel method for ground moving target detection and imaging using a SAR system transmitting ultra-narrowband continuous waveforms. We develop a new forward model that relates the velocity as well as reflectivity information at each location to a correlated received signal. We reconstruct moving target images by a filtered-backprojection method. We use the image contrast as a metric to detect moving targets and to determine their velocities. The method results in well-focused reflectivity images of moving targets and their velocity estimates regardless of the target location, speed, and velocity direction. We present numerical experiments to verify our method.

When vibrating objects are present in a Synthetic Aperture Radar image they induce a modulation in the pulse-to-pulse Doppler collected. At higher frequencies (up to a sampling limit dictated by half the PRF) the modulation is low amplitude due to physical limits of vibrating structures and swamped by the Doppler from static objects (clutter). This paper presents an orthogonal subspace transform that separates the modulation of a vibrating object from the static clutter. After the transformation the major frequencies of the vibration are estimated with asymptotically (as the number of pulses increases) decreasing variance and bias. Although the e¤ects and SAR image artifacts from vibrating objects are widely known their utility has been limited to high signal-to-noise, low frequency vibrating objects. The method presented here lowers the minimum required signal-to-noise ratio of the vibrating object over other methods. Additionally vibrations over the full (azimuth- sampled) frequency range from one over the aperture time to the pulse repetition frequency (PRF) are equally measured with respect to the noise level at each speci…c frequency. After separation of the vibrating and static object signal sub-spaces any of the many spectral estimation methods can be applied to estimate the vibration spectrum.

This paper proposes a hierarchical Bayesian model for multiple-pass, multiple antenna synthetic aperture radar (SAR) systems with the goal of adaptive change detection. We model the SAR phenomenology directly, including antenna and spatial dependencies, speckle and specular noise, and stationary clutter. We extend previous work1 by estimating the antenna covariance matrix directly, leading to improved performance in high clutter regions. The proposed SAR model is also shown to be easily generalizable when additional prior information is available, such as locations of roads/intersections or smoothness priors on the target motion. The performance of our posterior inference algorithm is analyzed over a large set of measured SAR imagery. It is shown that the proposed algorithm provides competitive or better results to common change detection algorithms with additional benefits such as few tuning parameters and a characterization of the posterior distribution.

This document defines the three pieces of the challenge hierarchy: a challenge area, a data set, and a challenge problem. The purpose of a challenge problem is to address a technical research and development area of interest while promoting quantitative comparison between approaches. This paper brings together nine challenge problem papers written for SAR exploitation and describes them in terms of the challenge hierarchy.

An airborne circular synthetic aperture radar system captured data for a 5 km diameter area over 31 orbits. For this challenge problem, the phase history for 56 targets was extracted from the larger data set and placed on a DVD for public release. The targets include 33 civilian vehicles of which many are repeated models, facilitating training and classification experiments. The remaining targets include an open area and 22 reflectors for scattering and calibration research. The circular synthetic aperture radar provides 360 degrees of azimuth around each target. For increased elevation content, the collection contains two nine-orbit volumetric series, where the sensor reduces altitude between each orbit. Researchers are challenged to further the art of focusing, 3D imaging, and target discrimination for circular synthetic aperture radar.

Edge detection algorithms applied to Synthetic Aperture Radar (SAR) images have many applications. Detecting edges is an important task in processing images in order to see objects from SAR data. In this work, the received data is rst ltered and then backprojected. The edges are detected in both the x and y directions and results shown. SAR segmented images generated using this technique, are provided from a publicly available SAR dataset. The authors of this technique had applied it to synthetic data; in this work the process is applied on real SAR data with signicant results.

Automatic target detection (ATD) methods for synthetic aperture radar (SAR) imagery are sensitive to image resolution, target size, clutter complexity, and speckle noise level. However, a robust ATD method needs to be less sensitive to the above factors. In this study, a constant false alarm rate (CFAR) based method is proposed which can perform target detection independent of image resolution and target size even in heterogeneous background clutter. The proposed method is computationally efficient since clutter statistics are calculated only for candidate target regions and a single execution of the method is sufficient for different types of targets having different shapes and sizes. Computational efficiency is further increased by parallelizing the algorithm using OpenMP and NVidia CUDA implementations.

High value target tracking and identification (ID) performance is impacted by sensor, target, and environmental conditions. Radar sensors are preferred since they provide sensor capabilities over a wide range of weather conditions. Sensor management provides some control, such as adjustment of the collection geometry. However, ground target dynamics and the collection environment can't be controlled and degrade tracking and identification performance. Some examples are when the target maneuvers into dense traffic, stops at intersections, or travels in a cluttered environment and is obscured by vegetation or buildings. Target identification algorithms using high range resolution (HRR) profiles formed from moving target data and range profiles formed from synthetic aperture radar (SAR) data have been demonstrated. Feature aided tracking (FAT) exploits the features derived from HRR data to improve target tracking. Identifying the dominant features which can be reliably exploited when a target is either moving or stationary that can then be used to maintain track and ID the target is expected to enhance algorithm performance in realistic scenarios. A simultaneous tracking and recognition (STAR) performance model is developed and applied to realistic scenarios to provide performance gain estimates based on the number of exploited features and operating conditions. This paper presents performance results for simultaneous target tracking and identification using HRR and SAR sensor data.

We consider two problems in this paper. The rst problem is to construct a dictionary of elements without using synthetic data or a subset of the data collection; the second problem is to estimate the orientation of the vehicle, independent of the elevation angle. These problems are important to the SAR community because it will alleviate the cost to create the dictionary and reduce the number of elements in the dictionary needed for classication. In order to accomplish these tasks, we utilize the glint phenomenology, which is usually viewed as a hindrance in most algorithms but is valuable information in our research. One way to capitalize on the glint information is to predict the location of the int by using geometry of the single and double bounce phenomenology. After qualitative examination of the results, we were able to deduce that the geometry information was sucient for accurately predicting the location of the glint. Another way that we exploited the glint characteristics was by using it to extract the angle feature which we will use to do the pose estimation. Using this technique we were able to predict the cardinal heading of the vehicle within ±2° with 96:6% having 0° error. Now this research will have an impact on the classication of SAR images because the geometric prediction will reduce the cost and time to develop and maintain the database for SAR ATR systems and the pose estimation will reduce the computational time and improve accuracy of vehicle classication.

In this paper we present an overview of the National Image Interprability Rating Scale (NIIRS) for SAR im- agery. We map basic SAR image formation parameters into the NIIRS via an information theoretic framework. Preliminary results obtained from a pilot study are presented for human interpretablity of various SAR im- ages. Extensions to this work which include sensor exploitation algorithms and integration within the Pursuer environment are outlined .

Since the launch of Terrasar-X, Radarsat 2 and the Cosmo-Skymed constellation, spaceborne SAR data with a high spatial resolution have become more readily available, allowing to monitor areas with a high level of human activity independent of weather circumstances. The current paper investigates the use of such data for geospatial intelligence applications in an harbor environment. The applications of interest are change detection and activity monitoring. For the analysis a set of more than twenty datasets from the three above mentioned satellite systems, acquired over a period of 30 days over the sea harbor of Zeebrugge in Belgium is available. Most datasets are high-resolution spotlight mode, but some scansar and full-polarimetric data have also been acquired. In the current paper HiRes spotlight data from the Cosmo-Skymed constellation are used for change detection and activity monitoring in the port.

Change detection is a important problem which plays a crucial role in many applications like environmental monitoring and city planning. The goal of change detection is to detects changes in specific features within certain time intervals. In this paper, we develop an automated method for detecting changes in urban areas over a period of time using lines and colors as features. Our proposed algorithm consists of two steps. In the first step, we detect corresponding lines between two images taken over different periods of time and we match them using our search algorithm. To be specific, first we use the Hough transform to detect lines. In the second step, we use colors to detect the changes over static and dyanmic objects. In a test of the method using aerial images over the our university campus area, we obtained reasonably good pose recovery and detection of scene changes.