Spatially variant incoherence trimming for improved bistatic SAR CCD

This tutorial presents a state-of-the-art survey of advanced applications of Synthetic Aperture Radar (SAR). Applications include: the detection and recognition of targets in SAR imagery, and the detection of targets under trees (and in the open) using change detection (coherent change detection, full-polarization change detection, etc). A fully polarimetric SAR sensor is described. Models of clutter and targets are developed from analysis of full-polarization SAR imagery. Algorithms for optimum (GLRT) processing of full-polarization data are derived, including the polarimetric whitening filter (PWF) and the polarimetric matched filter (PMF). Polarization-based discrimination features (polarization entropy, anisotropy, etc.) are developed and applied to SAR image terrain classification. Model-based and template-based target recognition systems are described and classifier performance results versus resolution and polarization are summarized for 10-and 20-target classifiers. Super-resolution algorithms are applied to SAR imagery and target recognition performance improvements demonstrated. Multi-polarization and multi-pass change detection algorithms are developed and demonstrated using UHF SAR imagery of targets under trees; fusion of multi-polarization coherent and non-coherent change detection algorithms is demonstrated. The effect of SAR image compression on change detection performance is quantified. Change detection performance using multi-pass VHF SAR imagery of targets under trees is demonstrated using data gathered by the Swedish CARABAS SAR system.

Many types of dark regions occur naturally or artificially in Synthetic Aperture Radar (SAR) and Coherent Change Detection (CCD) products. Occluded regions in SAR imagery, known as shadows, are created when incident radar energy is obstructed by a target with height from illuminating resolution cells immediately behind the target in the ground plane. No return areas are also created from objects or terrain that produce little scattering in the direction of the receiver, such as still water or flat plates for monostatic systems. Depending on the size of the dark region, additive and multiplicative noise levels are commonly measured for SAR performance testing. However, techniques for radar performance testing of CCD using dark regions are not common in the literature. While dark regions in SAR imagery also produce dark regions in CCD products, additional dark regions in CCD may further arise from decorrelation of bright regions in SAR imagery due to clutter or terrain that has poor wide-sense stationarity (such as foliage in wind), man-made disturbances of the scene, or unintended artifacts introduced by the radar and image processing. By comparing dark regions in CCD imagery over multiple passes, one can identify unintended decorrelation introduced by poor radar performance rather than phenomenology. This paper addresses select dark region automated measurement techniques for the evaluation of radar performance during SAR and CCD field testing.

This work aims to clarify the potential of incoherent and coherent change detection (CD) approaches for detecting and monitoring ground surface changes using sequences of synthetic aperture radar (SAR) images. Nowadays, the growing availability of remotely sensed data collected by the twin Sentinel-1A/B sensors of the European (EU) Copernicus constellation allows fast mapping of damage after a disastrous event using radar data. In this research, we address the role of SAR (amplitude) backscattered signal variations for CD analyses when a natural (e.g., a fire, a flash flood, etc.) or a human-induced (disastrous) event occurs. Then, we consider the additional pieces of information that can be recovered by comparing interferometric coherence maps related to couples of SAR images collected between a principal disastrous event date. This work is mainly concerned with investigating the capability of different coherent/incoherent change detection indices (CDIs) and their mutual interactions for the rapid mapping of “changed” areas. In this context, artificial intelligence (AI) algorithms have been demonstrated to be beneficial for handling the different information coming from coherent/incoherent CDIs in a unique corpus. Specifically, we used CDIs that synthetically describe ground surface changes associated with a disaster event (i.e., the pre-, cross-, and post-disaster phases), based on the generation of sigma nought and InSAR coherence maps. Then, we trained a random forest (RF) to produce CD maps and study the impact on the final binary decision (changed/unchanged) of the different layers representing the available synthetic CDIs. The proposed strategy was effective for quickly assessing damage using SAR data and can be applied in several contexts. Experiments were conducted to monitor wildfire’s effects in the 2021 summer season in Italy, considering two case studies in Sardinia and Sicily. Another experiment was also carried out on the coastal city of Houston, Texas, the US, which was affected by a large flood in 2017; thus, demonstrating the validity of the proposed integrated method for fast mapping of flooded zones using SAR data.

Modern high-performance Synthetic Aperture Radar (SAR) systems have evolved into highly versatile, robust, and reliable tactical sensors, offering images and information not available from other sensor systems. For example, real-time images are routinely formed by the Sandia-designed General Atomics (AN/APY-8) Lynx SAR yielding 4-inch resolution at 25 km range (representing better than arc-second resolutions) in clouds, smoke, and rain. Sandia's Real-Time Visualization (RTV) program operates an Interferometric SAR (IFSAR) system that forms three-dimensional (3D) topographic maps in near real-time with National Imagery and Mapping Agency (NIMA) Digital Terrain Elevation Data (DTED) level 4 performance (3 meter post spacing with 0.8-meter height accuracy) or better. When exported to 3-D rendering software, this data allows remarkable interactive fly-through experiences. Coherent Change Detection (CCD) allows detecting tire tracks on dirt roads, foot-prints, and other minor, otherwise indiscernible ground disturbances long after their originators have left the scene. Ground Moving Target Indicator (GMTI) radar modes allow detecting and tracking moving vehicles. A Sandia program known as "MiniSAR" is developing technologies that are expected to culminate in a fully functioning, high-performance, real-time SAR that weighs less than 20 lbs. The purpose of this paper is to provide an overview of recent technology developments, as well as current on-going research and development efforts at Sandia National Laboratories.

In past research, the problem of maritime targets detection and motion parameter estimation has been tackled. This letter aims to contribute to preventing illegal immigration, solving the problem of obtaining reliable paths detection of targets in terms of temporal decorrelation observed on coherent change detection (CCD) maps occurring between two or more complex-valued synthetic aperture radar (SAR) images. Most detection problems are related to terrain clutter and platform motion instabilities which make the paths structure detection and reconnaissance difficult. This letter presents a complete procedure called low-rank plus sparse decomposition radon transform (RT) CCD for automatic estimation and tracking of target paths by evaluating the generated temporal decorrelation CCD-SAR images, observed at the desert environments. The algorithm consists of evaluating a dual-stage low-rank plus sparse decomposition (LRSD) assisted by RT for clutter reduction, sparse object detection, and precise path inclination estimation. The algorithm is based on the robust principal component analysis (RPCA) implemented by convex programming. The LRSD algorithm permits the extrapolation of sparse objects of interest consisting of the incoherent patterns generated by targets from the unchanging low-rank and more coherent background. This dual-stage RPCA and RT applied to SAR-CCD surveillance permits fast detection and enhanced parameter estimation of terrain target paths.

An investigation was made into the feasibility of compressing complex Synthetic Aperture Radar (SAR) images using MatrixViewTM 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 g 0.9. The same investigation approach using equivalent sample data sets was performed using an emerging technology and product called MatrixViewTM. 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 g0.9: a 300-400% improvement over SPIHT.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Abstract. Synthetic aperture radar (SAR) coherent change detection (CCD) often utilizes the degree of coherence to detect changes that have occurred between two data collections. Although having shown some performances in change detection, many existing coherence estimators are still relatively limited because the change areas do not stand out well from all decorrelation areas due to the low cluster-to-noise ratio (CNR) and volume scattering. Moreover, many estimators require the equal-variance assumption between two SAR images of the same scene. However, the assumption is less likely to be met in regions of significant differences in intensity, such as the change areas. To address these problems, we proposed an improved coherence estimator that introduces the parameters about the true-variance ratio as the weights. Since these parameters are closely related to the ratio-change statistic in intensity-based change detection algorithms, their introduction frees the estimator from the need for the equal-variance assumption and enables detection results to largely combine the advantages of intensity-based and CCD methods. Experiments on simulated and real SAR image pairs demonstrate the effectiveness of the proposed estimator in highlighting the change, obviously improving the contrast between the change and the background.

Abstract. This study presents a semi-automatic algorithm for mapping floods. Both Optical and Synthetic Aperture Radar (SAR) data are used to observe the flood that hit the Cukurova region of Adana (Turkey) in 2019. The performance of the interferometric coherence in complementing intensity component of SAR data is investigated for mapping the floods occurred in agricultural and urban environments. There was no ground truth data available from the flooded area, thus classification result of optical satellite image is used as a seed for the region growing algorithm that defines the classes according to a threshold value. The advantage of using both intensity and coherence change detection is verified with the results. The results have been evaluated through very high-resolution SPOT-6 optical image which acquired simultaneously with Sentinel-1B SAR image. The comparison with the SPOT-6 data results shows that the proposed approach can map flooded areas with acceptable accuracy using the SAR data from Sentinel-1 satellite mission. Highly affected agricultural areas along with the river line could be mapped both by optical and SAR analysis. Comparison of results from VV and VH polarization provided that cross-polarization VH has a very little effect on flood mapping. The proposed algorithm successfully distinguishes the classes among the affected region, especially in urban areas.

Coherent change detection is a technology that utilizes phase information in complex-valued synthetic aperture radar images. It is mostly used to detect subtle changes that cannot be detected by amplitude images on the ground, and it also has excellent detection performance when it comes to low-coherence typical changed areas. However, due to its high sensitivity to changes, this technology will falsely detect areas of natural change such as vegetation disturbance, river flow, and low signal-to-noise ratio areas (e.g., uninteresting areas) as changes, resulting in false-alarm interference areas. In order to tackle this problem, this paper studies a coherent change detection method based on multi-scale analysis to extract typical changed areas in complicated scenes. The method uses an equal variance coherence estimator to calculate the coherence value, separates the interference areas and the typical changed areas using a multi-scale method, and then extracts a binary image of the typical changed areas through noise filtering and threshold segmentation. The method in this paper is experimentally verified with publicly available Airbus spaceborne SAR data, and ESAR airborne data, which is provided by the ESA. The experimental results are visualized and quantitatively evaluated. Through the results, by calculating the probability of correct classification and false-positive and other performance parameters, as well as drawing the receiver operating characteristic curve and the kappa coefficient curve of different threshold values, we find that the method has the capability to suppress the interference areas and the high detection performance of the typical changed areas. The experimental data are complicated scenes that include various types of ground object changes. The results show that the method is effective and universal and can provide reference value for the application of coherent change detection.

A radome, or radar dome, protects a radar system from exposure to the elements. Unfortunately, radomes can affect the radiation pattern of the enclosed antenna. The co-design of a platform's radome and radar is ideal to mitigate any deleterious effects of the radome. However, maintaining structural integrity and other platform flight requirements, particularly when integrating a new radar onto an existing platform, often limits radome electrical design choices. Radars that rely heavily on phase measurements such as monopulse, interferometric, or coherent change detection (CCD) systems require particular attention be paid to components, such as the radome, that might introduce loss and phase variations as a function of the antenna scan angle. Material properties, radome wall construction, overall dimensions, and shape characteristics of a radome can impact insertion loss and phase delay, antenna beamwidth and sidelobe level, polarization, and ultimately the impulse response of the radar, among other things, over the desired radar operating parameters. The precision-guided munitions literature has analyzed radome effects on monopulse systems for well over half a century. However, to the best of our knowledge, radome-induced errors on CCD performance have not been described. The impact of radome material and wall construction, shape, dimensions, and antenna characteristics on CCD is examined herein for select radar and radome examples using electromagnetic simulations.

This paper presents proof-of-concept methods and results on a passive synthetic aperture radar for surface change monitoring. The bistatic topology comprises navigation satellites (such as Global Positioning System, GLObal Navigation Satellite System (GLONASS), Galileo, or Beidou) as transmitters of opportunity and a fixed receiver on the ground. Surface monitoring is to be provided through coherent change detection. An experimental test bed built to confirm the concept of the system is described, and the appropriate experimental program is presented. Preliminary signal processing algorithms for change detection are derived. Experimentally measured changes are compared to the theoretical predictions, and the obtained results are discussed.

Long range surveillance of infrastructure is a critical need in numerous security applications, both civilian and military. Synthetic aperture radar (SAR) continues to provide high resolution radar images in all weather conditions from remote distances. As well, Interferometric SAR (InSAR) and Differential Interferometric SAR (D-InSAR) have become powerful tools adding high resolution elevation and change detection measurements. State of the art SAR systems based on dual-use satellites are capable of providing ground resolutions of one meter; while their airborne counterparts obtain resolutions of 10 cm. D-InSAR products based on these systems could produce cm-scale vertical resolution image products. Deformation monitoring of railways, roads, buildings, cellular antennas, power structures (i.e., power lines, wind turbines, dams, or nuclear plants) would benefit from improved resolution, both in the ground plane and vertical direction. The ultimate limitation to the achievable resolution of any imaging system is its wavelength. State-of-the art SAR systems are approaching this limit. The natural extension to improve resolution is to thus decrease the wavelength, i.e. design a synthetic aperture system in a different wavelength regime. One such system offering the potential for vastly improved resolution is Synthetic Aperture Ladar (SAL). This system operates at infrared wavelengths, ten thousand times smaller than radar wavelengths. This paper presents a laboratory demonstration of a scaled-down infrastructure deformation monitoring with an Interferometric Synthetic Aperture Ladar (IFSAL) system operating at 1.5 μm. Results show sub-millimeter precision on the deformation applied to the target.

Another way to detect slow-moving tactical targets is by means of HRM coupled with some form of change detection between multiple images. The change detection methods are coherent change detection (CCD) and shadow detection, noncoherent change detection (NCD), video SAR, inverse SAR (ISAR) and synthetic monopulse. Each of these will be discussed but first an understanding of conventional SAR is required. Often, fast(er) moving targets and slow-moving targets and SAR maps are acquired by the same waveform as described at the end of Chapter 6. The separate target types are usually separated by filtering.

Interference and interference mitigation techniques degrade synthetic aperture radar (SAR) coherent data products. Radars utilizing stretch processing present a unique challenge for many mitigation techniques because the interference signal itself is modified through stretch processing from its original signal characteristics. Many sources of interference, including constant tones, are only present within the fast-time sample data for a limited number of samples, depending on the radar and interference bandwidth. Adaptive filtering algorithms to estimate and remove the interference signal that rely upon assuming stationary interference signal characteristics can be ineffective. An effective mitigation method, called notching, forces the value of the data samples containing interference to zero. However, as the number of data samples set to zero increases, image distortion and loss of resolution degrade both the image product and any second order image products. Techniques to repair image distortions, 1 are effective for point-like targets. However, these techniques are not designed to model and repair distortions in SAR image terrain. Good terrain coherence is important for SAR second order image products because terrain occupies the majority of many scenes. For the case of coherent change detection it is the terrain coherence itself that determines the quality of the change detection image. This paper proposes an unique equalization technique that improves coherence over existing notching techniques. First, the proposed algorithm limits mitigation to only the samples containing interference, unlike adaptive filtering algorithms, so the remaining samples are not modified. Additionally, the mitigation adapts to changing interference power such that the resulting correction equalizes the power across the data samples. The result is reduced distortion and improved coherence for the terrain. SAR data demonstrates improved coherence from the proposed equalization correction over existing notching methods for chirped interference sources.

In this paper we present a new generalized family of change detection algorithms for SAR imagery that includes traditional non-coherent and coherent processing as special cases. The parameterized family of algorithms, referred to as partially coherent change detection (PCCD), allows the user to select the level of coherence desired in the change detection algorithm. This and other settings of the algorithm enable one to specify the types of changes that are significant, thereby reducing the number of false alarms due to insignificant changes—such as foliage motion. Algorithm settings may also be applied spatially in order to support spatially varying levels of coherence based on scene content. Examples from synthetic and measured imagery demonstrate the efficacy of the new family of algorithms.