Actual Synthetic Aperture Radar Data Research Articles

Robust Clutter Suppression and Radial Velocity Estimation for High-Resolution Wide-Swath SAR-GMTI

Moving targets are usually smeared and imaged at incorrect positions in synthetic aperture radar (SAR) images due to the target motions during the illumination time. Moreover, a moving target will cause multiple artifacts in the reconstructed image since pulse repetition frequency (PRF) operated in high-resolution wide-swath (HRWS) SAR is very low. In order to reliably indicate moving targets, a robust cancellation algorithm is derived in this paper for clutter suppression in multichannel HRWS SAR, which is free by velocity searching and covariance matrix estimation of clutter plus noise. The proposed multilayer channel-cancellation combined with the deramp processing is designed to sequentially suppress the seriously aliased clutter in HRWS SAR. Experimental results show that the proposed algorithm is efficient and robust in tough situations, and have a superior detection ability in weak targets and low-velocity targets. In addition, the radial velocity estimation algorithm combined with the channel cancellation is exploited to relocate moving targets. The effectiveness of the proposed algorithms is validated by actual spaceborne SAR data acquired by a coordination experiment with two controllable vehicles.

Near Real-Time InSAR Deformation Time Series Estimation With Modified Kalman Filter and Sequential Least Squares

The current and planned synthetic aperture radar (SAR) sensors mounted on satellite platforms will continue to operate over the coming years, providing unprecedented SAR data for monitoring wide-range surface deformations. The near real-time processing of SAR interferometry (InSAR) data for the retrieval of ground-deformation time series is urgently required in the current era of big data. The state-of-the-art Kalman filter (KF) and sequential least squares (SLS) algorithms have been proposed to update an InSAR-driven ground-deformation time series. As a contribution of this study, we customize the conventional KF and SLS for big InSAR data for near real-time processing. The development of an accurate prediction model for KF-based InSAR processing is a challenge owing to the large scale of the targets for surface monitoring. We developed a modified KF algorithm, abbreviated as npKF, that does not require any prediction information, abbreviated as npKF. In this context, to avoid occupying a large storage space in SLS-based InSAR processing, we developed a modified SLS algorithm with a truncated cofactor matrix, abbreviated as TSLS. Using both simulated and actual SAR data, we evaluated the performance of these methods under three different aspects: accuracy, computation, and storage performance. With big data, the proposed method can estimate the deformation time series in near real time. It will be a reliable and effective tool for producing near real-time InSAR deformation products in the coming era of processing big SAR data and will play a part in the geologic hazard routine monitoring and early warning system.

Improved Point Estimation for the Rayleigh Regression Model

The Rayleigh regression model was recently proposed for modeling amplitude values of synthetic aperture radar (SAR) image pixels. However, inferences from such model are based on the maximum likelihood estimators, which can be biased for small signal lengths. The Rayleigh regression model for SAR images often takes into account small pixel windows, which may lead to inaccurate results. In this letter, we introduce bias-adjusted estimators tailored for the Rayleigh regression model based on: (i) the Cox and Snell's method; (ii) the Firth's scheme; and (iii) the parametric bootstrap method. We present numerical experiments considering synthetic and actual SAR data sets. The bias-adjusted estimators yield nearly unbiased estimates and accurate modeling results.

An Efficient Preprocessing Approach for Airborne Hybrid SAR and ISAR Imaging of Ship Target Based on Kernel Distribution

The airborne hybrid synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging for the ship target is very important in the field of ocean surveillance, but it suffers from the problem of high computational complexity. In this paper, an efficient preprocessing approach for the airborne hybrid SAR and ISAR imaging based on the kernel distribution is proposed, which can reduce the computational complexity and preserve the image quality simultaneously. Firstly, the residual Doppler frequency is estimated. Then, a novel measurement method with good robustness is proposed for evaluating the stationarity of Doppler frequency from the aspect of statistics, which adopts the kernel distribution to estimate the probability distribution function (PDF) curve for the Doppler frequency. Afterwards, an effective preprocessing approach is addressed consequently to eliminate the incomplete illumination time and select the time interval with steady target motion. Results of simulated and actual SAR data verify the effectiveness of the novel algorithm proposed in this paper.

The compound truncated Poisson Cauchy model: A descriptor for multimodal data

Multimodal data are often present in synthetic aperture radar (SAR) image processing. Such images are often modeled by probability mixtures, but such solution may involve a large number of parameters and its inference becomes challenging. To address this issue, we proposed a probability distribution capable of describing multimodal data with only three parameters. The introduced model is defined by the sum of a random number, following the truncated Poisson law, of independent random variables with the Cauchy model, called compound truncated Poisson Cauchy (CTPC) distribution. We derived the characteristic function (cf), a distance measure between cfs, and provided estimators for the CTPC parameters: maximum likelihood estimators (MLEs) and quadratic distance estimators (QDEs). Furthermore, we derived a goodness-of-fit (GoF) distance based on the CTPC law and its empirical cf. To quantify the performance of the proposed estimators and GoF statistic, we employed a Monte Carlo simulation and the results suggest that QDEs outperform MLEs. Finally, we detail a numerical experiment with actual SAR data which describes a segment of SAR intensities with at least two types of textures. Our model could outperform six SAR intensities models: Weibull, gamma, generalized gamma, K, G0, and beta generalized normal.

Flood Detection in Built-Up Areas Using Interferometric Phase Statistics of PALSAR-2 Data

This study investigated a method of flood detection in built-up areas by L-band synthetic aperture radar (SAR) images, using interferometric phase statistics instead of the widely used coherence. Simulation and actual SAR data analysis showed that phase statistics are more accurate than coherence is, especially when there are many point-like scatterers. Accuracy was further improved by using an interferometric phase filter. The flood detection accuracies obtained by the phase statistics were $\kappa =0.535$ (0.304 by coherence) in the 2015 flood in Joso and $\kappa =0.679$ (0.609 by coherence) in the 2018 flood in Mabi. These results showed that coherence should be used with great care with point targets and that the phase-only statistics can be more robust in such cases. The proposed method is expected to apply to other types of disasters where coherence has been used previously.

SAR Speckle Dependence on Ocean Surface Wind Field

A novel physical paradigm is explored in this paper: synthetic-aperture radar (SAR) ocean speckle is informative. This paper experimentally analyzes the SAR ocean speckle intensity K-distribution model versus sea-surface wind field. It is shown that the normalized intensity moments of the K-distribution measured from actual C-band SAR data well fit the theoretical ones under different wind conditions, i.e., wind regime and relative SAR-wind azimuth direction. In addition, it is observed that the K-distribution shape parameter decreases as wind speed increases. A sensitivity of the K-distribution shape parameter on incidence angle and wind direction is also observed, where the largest variability is experienced at higher incidence angles and under crosswind relative azimuth angle.

Scheme for Characterizing Clutter Statistics in SAR Amplitude Images by Combining Two Parametric Models

Accurate knowledge of the statistical properties of synthetic aperture radar (SAR) amplitude data is essential for the processing and interpretation of SAR images. During the last few decades, parametric models have been extensively investigated for use in characterizing the statistics of SAR amplitude data. Among them, the generalized gamma distribution ( $\text{G}\Gamma \text{D}$ ) has been widely applied in many fields of SAR image processing, as it has been demonstrated to be appropriate for describing the statistical behaviors of both SAR land and sea clutter. However, the $\text{G}\Gamma \text{D}$ is not effective when the third-order sample log-cumulant of SAR data is close to zero as it cannot match the distribution of actual data. In this case, the recently proposed ${\mathcal{ G}}_{\textrm {AO}} $ model shows good performance for fitting the distribution of the actual SAR data. In this paper, we theoretically derive the analytical conditions of the ${\mathcal{ G}}_{\textrm {AO}} $ model applicability. Based on the derivations, a scheme describing the statistical behaviors of SAR pixel amplitudes is given by combining the $\text{G}\Gamma \text{D}$ and the ${\mathcal{ G}}_{\textrm {AO}} $ model. Experiments performed on C-band RADARSAT-2 and L-band ALOS-PALSAR SAR data verify the effectiveness of the proposed scheme. The usefulness of the ${\mathcal{ G}}_{\textrm {AO}} $ model for extremely heterogeneous land clutter, for example, from urban regions, is also demonstrated.

Automatic registered back‐projection approach based on object orientation for airborne repeat‐track interferometric SAR

The requirements of the correction of motion errors and high accurate registration are difficult to satisfy for the independence of the aperture paths in airborne repeat-track interferometry. Here, according to the principle of the independence between the imaging scene and synthetic aperture radar (SAR) viewing geometry, the authors propose a new SAR imaging approach based on the idea of object orientation and back-projection (BP), where the complex motion compensation is effectively avoided and the image formation and registration are achieved simultaneously with high accuracy. In contrast to the standard BP algorithm, it greatly reduces the difficulty of registration brought by the irregular deviations. Moreover, it further successfully overcomes the influence introduced by the centre-beam approximation compared with frequency-domain algorithms. Experimental results both on simulated and actual SAR data are used to validate the proposed algorithm.

Detection and Discrimination of Ship Targets in Complex Background From Spaceborne ALOS-2 SAR Images

This paper proposes a novel method for ship detection and discrimination in complex background from synthetic aperture radar (SAR) images. It first implements a pixel-level land–sea segmentation with the aid of a global 250-m water mask. Then, an efficient multiscale constant false alarm rate (CFAR) detector with generalized Gamma distribution clutter model is designed to detect candidate targets in the sea. At last, eigenellipse discrimination and maximum-likelihood (ML) discrimination are designed to further exclude false alarm nonship objects in nearshore and harbor area. The proposed land–sea segmentation method is compared with multilevel Otsu method. The proposed multiscale ship detector is compared with conventional CFAR detectors. These contrast experiments show the good performance of our method. Finally, experiments undertaken on actual ALOS-2 SAR data show the efficacy of the proposed approach in detecting nearshore ship targets in a complex coastal environment.

Clutter Suppression via Hankel Rank Reduction for DFrFT-Based Vibrometry Applied to SAR

Hankel rank reduction (HRR) is a method that, by prearranging the data in a Hankel matrix and performing rank reduction via singular value decomposition, suppresses the noise of a time-history vector comprised of the superposition of a finite number of sinusoids. In this letter, the HRR method is studied for performing clutter suppression in synthetic aperture radar (SAR)-based vibrometry. Specifically, three different applications of the HRR method are presented. First, resembling the SAR slow-time signal model, the HRR method is utilized for separating a chirp signal immersed in a sinusoidal clutter. Second, using simulated airborne SAR data with 10 dB of signal-to-clutter ratio, the HRR method is applied to perform target isolation and to improve the results of an SAR-based vibration estimation algorithm. Finally, the vibrometry approach combined with the HRR method is validated using actual airborne SAR data.

On the sensitivity of polarimetric SAR measurements to vegetation cover: the Coiba National Park, Panama

ABSTRACTIn this study, the sensitivity of multi-polarization synthetic aperture radar (SAR) features to vegetation cover is investigated over a test case of environmental importance: the Coiba National Park, Panama. Single-polarization intensity features and polarimetric features derived from the eigenvalue/eigenvector decomposition are analysed and their classification performance, evaluated against a reference land-cover map using a simple clustering algorithm, is contrasted with conventional optical features.Experiments, undertaken using actual L-band full-polarimetric SAR and Landsat data, show that (a) polarimetric information plays a key role in improving the classification accuracy with some polarimetric features performing better than single-polarization and optical ones, (b) classification performance of radar features is significantly affected by incidence angles, and (c) a joint use of different radar features is expected to increase classification accuracy.

Polarimetric Analysis of Compact-Polarimetry SAR Architectures for Sea Oil Slick Observation

In this paper, a theoretical and experimental analysis of polarimetric synthetic aperture radar (SAR) architectures is undertaken for sea oil slick observation purposes. Reference is made to the conventional full-polarimetric (FP) SAR that is here contrasted with new-generation polarimetric SAR architectures, known as compact-polarimetric (CP) SAR. Two CP modes are considered, i.e., the hybrid-polarity and π/4 modes, whose measurements are emulated from actual L- and C-band FP SAR data. Polarimetric sea surface scattering is predicted according to an extended version of the Bragg scattering model (X-Bragg) in order to point out the differences exhibited between FP and CP SAR architectures and among CP SAR modes. Theoretical predictions are then contrasted with experiments undertaken on actual polarimetric SAR data collected over well-known oil slicks and weak-damping surfactants. Results confirm model prediction, showing that differences mainly apply when polarimetric features are estimated over slick-free sea surface using different SAR architectures, with the π/4 mode behaving closer to FP SAR. Although CP SAR architectures measure only a subset of the FP information content, they represent an interesting operational alternative for both detecting oil slicks and discriminating them from weak-damping surfactants.

Analysis of Full and Compact Polarimetric SAR Features Over the Sea Surface

In this letter, fully polarimetric (FP) and compact polarimetric (CP) synthetic aperture radar (SAR) features are investigated over the sea surface. While exhibiting similar trends, subtle differences are observed and investigated here using a new model-based approach. The model indicates that differences between the FP and CP architectures, and among the CP modes, are due to the difference in mapping between the polarimetric observables and the eigenvalues. This is verified using actual FP SAR data and emulated CP SAR measurements.

On the Capability of Hybrid-Polarity Features to Observe Metallic Targets at Sea

In this paper, target at sea detection is addressed using hybrid-polarity (HP) synthetic aperture radar (SAR) architecture. Wave polarimetry concepts are used to define HP features that are used to observe targets at sea. The sensitivity of HP features to both targets and the surrounding environment is analyzed through a novel objective norm, namely the Relative Sensitivity for Polarimetric Features (RSPolF) index. Detection performance is evaluated by the novel Dependency of Sea state and Target (DoST) surface characteristics metric, and by the well-known Figure of Merit (FoM). Experiments undertaken on HP measurements emulated from Radarsat-2 and ALOS-PALSAR full-polarimetric actual SAR data demonstrate the effectiveness of the proposed HP approach and the different sensitivity of HP features to targets and background sea characteristics. Following those results, a ranking of the HP features performance is proposed which mainly highlights that HP features complement each other in the detection process. Finally, a constant false alarm rate (CFAR) approach is proposed to exploit two HP features for target detection in an unsupervised way.

A SAR Autofocus Technique With MUSIC and Golden Section Search for Range Bins With Multiple Point Scatterers

Many synthetic aperture radar (SAR) autofocus techniques use range bins containing a single dominant point scatterer to estimate the phase error by maximizing or minimizing an objective function. We analytically show that some widely used objective functions do not give accurate phase error estimates if the objective function is constructed using a range bin containing multiple strong point scatterers (SPSs). Multiple SPSs are often observed in the range bins extracted from SAR images of urban areas containing many bright man-made objects. Such multiple SPSs do not allow us to obtain accurate estimates due to the interference between SPSs. To overcome this multiple scatterer problem, we propose the use of a combined entropy objective function with the local magnitudes of SPSs, along with the multiple-signal classification algorithm. Our experiment with actual SAR data confirms the superiority of the proposed approach.

Precise Focusing of Airborne SAR Data With Wide Apertures Large Trajectory Deviations: A Chirp Modulated Back-Projection Approach

In the area of airborne synthetic aperture radar (SAR), motion compensation (MOCO) is a crucial technique employed to correct the SAR data affected by nonlinear platform trajectory during data acquisition. Due to range-azimuth coupling and computational burden consideration, some approximations, which are valid for SAR systems of moderate aperture length, are usually adopted in commonly used MOCO approaches. However, a much more accurate SAR data processing approach is appealing to process the low-frequency SAR systems with large aperture length, such as P-band. In this paper, a new MOCO approach with high precision and high efficiency is proposed. After the ω - κ processing and the range-dependent MOCO, the analytical expression of a 2-D spectrum of a partially focused SAR image is given. Afterward, aperture reduction is achieved by a chirp modulation technique. Finally, with high precision and less computation cost, back projection along the new built short apertures (affected by the residual motion errors) is employed to yield a fairly well-focused SAR image. Experimental results on simulated and actual P-band SAR data are presented to verify the performance of the proposed approach.

Utilizing higher moments to detect time-varying target in radar echo with non-stationary background

Detecting time-varying target in non-stationary background is difficult and attractive problem. Time-varying movement exists widely in radar and communication systems. The non-linear processing with higher moments is discussed in the situation. Firstly the signal model of time-varying target with fix acceleration is analyzed. Then the radar echoes from synthetic aperture radar (SAR) are processed with higher moments. It not only restrains Gaussian noise automatically, but also suppresses non-stationary noise. Time-varying targets are different from the non-stationary background clutters. Moreover, the influences of the shadows about time-varying targets are reduced in the algorithm of higher moments. The proposal mentioned above utilizes higher moments to avoid analyzing the complex electromagnetic wave propagation and scatter theory. The differences of detection probability are compared between the chip with time-varying target and the clutter chip. The performances among different number order moments are compared by processing lots of actual SAR data added non-stationary noise. Lastly, the suitable number order moments are suggested by comparing the results from processing the actual radar echoes.

Performance Analysis and Validation of Waterline Extraction Approaches Using Single- and Dual-Polarimetric SAR Data

In this study, the performance of two waterline extraction approaches is analyzed using dual-polarization Cosmo-SkyMed (CSK) Synthetic Aperture Radar (SAR) data and ancillary ground truth information. The single-polarization approach is based on multiscale normalized cuts segmentation; while, the dual-polarization one exploits the inherent peculiarities of the CSK PING PONG incoherent dual-polarimetric imaging mode together with a tailored scattering model to perform land/sea discrimination. The two approaches are applied to the actual CSK SAR data collected over the coastal area of Shanghai, China. To provide a detailed and complete validation of the two approaches, we carried out several field surveys collecting in situ ancillary information including Global Positioning System (GPS) data and tidal information. Experimental results show that 1) both approaches provide satisfactory results in extracting waterline from CSK SAR data in the intertidal flat under low-to-moderate wind conditions and under a very broad range of incidence angles; 2) the accuracy of the waterline extracted by both approaches decreases in case of water within the intertidal flat; 3) the single-polarization approach is unsupervised when the land/sea contrast ratio is high. However, it needs manual supervision to correct the extracted waterline when the land/sea contrast is low or in complex areas. A typical CSK scene is processed in about 25 min; 4) the dual-polarization approach is unsupervised and very effective: a typical CSK SAR scene is processed in seconds.

A Multipolarization Analysis of Coastline Extraction Using X-Band COSMO-SkyMed SAR Data

In this study, COSMO-SkyMed (CSK) Synthetic Aperture Radar (SAR) data, collected by the single-polarization stripmap Himage mode, are exploited for coastline extraction purposes. First, a multipolarization analysis of sea surface backscattering is undertaken using the Improved Integral Equation Method (IIEM). Then, a simple two-step approach is proposed to extract the continuous coastline from co- and cross-polarized Himage CSK SAR data. The approach is framed into two steps: 1) obtaining a binary output from the SAR image using a global threshold Constant False Alarm Rate (CFAR) detector; and 2) extracting the continuous one-pixel coastline from the binary output with a conventional Sobel edge detector. The proposed approach is verified against actual CSK SAR data and it is shown to be both effective and accurate when low-to-moderate wind conditions apply.