Swath Synthetic Aperture Radar Research Articles

A Novel Time-Domain Frequency Diverse Array HRWS Imaging Scheme for Spotlight SAR

The frequency diverse array (FDA) technique is a novel scheme for high resolution wide swath synthetic aperture radar (SAR) imaging, which employs a frequency increment across the array elements. This introduces a range-angle-dependence to the transmission steering vector, which is exploited for range ambiguity resolution in strip-map SAR. Generally in spotlight mode, scatterers dispersively distributed in azimuth have different Doppler histories, in which the range ambiguity resolution for strip-map SAR fails. To extend the flexibility of FDA, in this paper a novel FDA imaging scheme for spotlight mode SAR is proposed. Exploiting the property of the same illuminated period of all scatterers in spotlight mode, the proposed scheme is carried out entirely in the azimuth time domain, which allows for higher processing efficiency and real-time implementations. Still, excessive Doppler history differences among scatterers deteriorate the scheme performance for azimuth-edge scatterers. Aiming at this situation, a residual angle phase compensation in time domain is considered for the cases of a large azimuth beam width, improving the applicability of the proposed scheme. Compared with existing methods, the proposed spotlight FDA-SAR offers the possibility of achieving simultaneously high azimuth resolution and wide swath performance with high efficiency. Simulations and analyses are performed to demonstrate the effectiveness of the proposed spotlight FDA-SAR scheme.

Method of High Signal-to-Noise Ratio and Wide Swath SAR Imaging Based on Continuous Pulse Coding

In synthetic aperture radar (SAR), increasing the pulsewidth of signal is an effective way to achieve high signal-to-noise ratio (HSNR) imaging. However, when the pulse repetition frequency (PRF) is fixed, increasing the pulsewidth will reduce the maximum unambiguous swath width of SAR. In order to solve this contradiction, a method based on continuous pulse coding (CPC) which increases the average transmit power by multiple pulses with varying high PRF is proposed in this article. Due to the small interval between pulses, the echo will have range ambiguity and occlusion problems. To obtain the complete echo, we first use the form of CPC signal and the swath width of radar to construct a linear equation set to model the ambiguous echo of each receiving window. Next, the established linear equations are split according to the distribution law of receiving window. Finally, the echo energy accumulation of multiple pulses is accomplished by solving the split sublinear equations. Therefore, the echo with both swath width corresponding to narrow pulse and HSNR corresponding to wide pulse is obtained to realize HSNR and wide swath SAR imaging. The experimental results have confirmed the effectiveness of the method proposed in this article.

Quantifying relationships between subsidence and longwall face advance using DInSAR

Surface subsidence that results from longwall mining can be large magnitude and can affect significant areas. Conventional methods for subsidence monitoring include leveling, global positioning system (GPS), and photogrammetric surveys. Remote sensing techniques including, aerial LiDAR, terrestrial laser scanning, and satellite-based Differential Interferometric Synthetic Aperture Radar (DInSAR), are also used to measure deformation associated with subsidence. DInSAR data are different than data from conventional subsidence surveys. Images capture data over large areas (hundreds of kilometers), and each pixel (data point) in an image quantifies the average displacement over an area of square meters. DInSAR data can have fairly high time resolution; imaging periods typically range from weeks to months. DInSAR data can be useful to monitor subsidence sequentially over short periods. Regularly monitoring subsidence may help define if caving is progressing normally and can establish relationships between surface deformation and longwall face advance, which has potential to help quantify possible risks to mine stability. In this study, subsidence at a longwall trona mine is monitored over short periods, typically 12 days, as the longwall face is advanced through a panel. C-band interferometric wide swath synthetic aperture radar (SAR) images from the sentinel satellites are used to quantify the subsidence. The onset of subsidence occurs close in time to the beginning of the longwall face advance, and overall, the development of subsidence closely follows the longwall face advance.

Application of DInSAR for short period monitoring of initial subsidence due to longwall mining in the mountain west United States

Differential Interferometric Synthetic Aperture Radar (DInSAR), a satellite-based remote sensing technique, has application for monitoring subsidence with high resolution over short periods. DInSAR uses radar images to measure centimeter-level surface displacements. In the images, ground resolution can be relatively high, with each data point (pixel) representing the average displacement over an area of several square meters. The image data are acquired regularly which allows subsidence to be monitored sequentially over short periods; imaging periods typically range from weeks to months. Monitoring subsidence over short periods with high spatial resolution has potential to provide insight into the dynamics of subsidence and into relationships between mine advance and subsidence. In this study, for three longwall mines in the western United States, initial subsidence occurring at the start of longwall advance is quantified over short periods (12–72 days). C-band interferometric wide swath Synthetic Aperture Radar (SAR) images from the Sentinel satellites are used to quantify the subsidence. Overall, the data show initial development of subsidence, expansion of the subsidence trough, and the advance of subsidence in the direction of mining.

The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment

We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.

Imaging algorithm for multi‐channel scan synthetic aperture radar with channel error estimation and compensation

The wide swath synthetic aperture radar (SAR) imaging with a fine resolution is fundamentally a contradiction in conventional SAR imaging. To meet the demand of high-resolution width swath (HRWS) imaging, multi-channel scan mode has received a great deal of attention as a promising concept to realise HRWS observation. In this article, based on extended Chirp Scaling algorithm, an imaging algorithm for multi-channel scan SAR is proposed. Furthermore, channel error estimation and compensation are the key point in multi-channel scan imaging and are integrated into the proposed imaging algorithm. The computer simulation is shown to verify the presented imaging algorithm.

Pre-processing of Sentinel-1 C-band SAR images based on incidence angle correction for dark target detection

ABSTRACTWide swath synthetic aperture radar (SAR) has been commonly used for oceanographic applications, such as maritime safety, fisheries management and coastal monitoring, including dark target detection. However, the incidence angle (θ) effect on these data makes a direct visualization and classification of oceanographic targets troublesome. The recent availability of free-of-charge Sentinel-1 C-band data have the potential to assess methods for correcting the θ in wide swath SAR images, an issue that still remains to be undertaken. The goal of this work was to test two methods for correcting the θ effect of two Sentinel-1 SAR imaging modes and assess their implications for dark target detection in a sector of the South Atlantic Ocean. We assessed the empirical modelling and square cosine-based methods for correcting the θ effect on co and cross-polarization from the Interferometric Wide Swath (IW) and Extra Wide Swath (EW) imaging modes. The results showed that the empirical modelling method improved the visualization and classification of dark targets for the co-polarized bands from both the imaging modes. However, further studies are necessary to test the potential of empirical modelling for correcting the θ for other different oceanic environments and SAR frequencies.

Validation of Sentinel-1A and AW3D30 DSMs for the Metropolitan Area of Istanbul, Turkey

In space-borne remote sensing, one of the most significant tasks is the three-dimensional (3D) surface modelling performance of optical and synthetic aperture radar (SAR) satellite missions. In this study, the quality of Sentinel-1A (S-1A) and Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30) Digital Surface Models (DSMs) was comprehensively analysed. In addition, 1 arcsec Shuttle Radar Topography Mission (SRTM) C-band DSM was validated for better interpretation of the results. A project area including various land cover classes was selected in Istanbul, where a reference airborne laser scanning DSM is available. Optimal S-1A interferometric wide (IW) swath SAR pairs were determined and a 15 m gridded DSM was generated by interferometric processing. AW3D30 and SRTM DSMs were obtained from JAXA and NASA. In the analysis, the absolute and relative vertical accuracies of the DSMs were validated for all land cover classes with a model-to-model comparison to the reference. In addition, the influence of terrain tilt was investigated using a threshold terrain inclination of tan−1 0.1 (5.7°). Height differences from the reference were visualized by colour-coded height error maps. Vertical profiles and contour lines showed the morphologic character of the DSMs. The results demonstrated that the absolute vertical accuracies and morphologic details of AW3D30 are superior to those of S-1A and SRTM.

Implementation of RISAT-1 Hybrid Polarimetric Decomposition Techniques and Analysis Using Corner Reflector Data

With recent advances in polarimetry, Synthetic Aperture Radar (SAR) with Hybrid–polarity architecture, a demonstration of compact polarimetry enabled larger swath coverage, reduced PRF and SAR system complexity as compared to fully polarimetric systems. The first Hybrid Polarimetric Space-borne SAR in Earth Observation orbit, India’s Radar Imaging Satellite (RISAT-1) is a new-fangled gateway to remote sensing user community for land and oceanic applications. In response to a right-circular polarized transmitted signal, based on the derived stokes vectors, Stokes parameters are estimated to produce several useful quantitative measures for generating polarimetric decomposed image. m-delta, m-chi and m-alpha polarimetric decomposition methods along with suitable weighting functions in terms of three principal components are implemented which maps Stokes parameters to RGB image space for representing odd bounce, even bounce and volume scattering targets. Various RISAT-1 Hybrid Fine Resolution Stripmap Single-Look Complex SAR datasets acquired over deployed corner reflectors at calibration site, Shadnagar have been considered over which different hybrid polarimetric decomposition techniques are implemented using in-house developed software. Further analysis produced encouraging results with standard point targets like dihedral and trihedral corner reflectors against distributed targets in the same scene to demonstrate the scattering mechanisms as per their characteristics when interacted with a polarized signal were presented in this paper.

AN ADAPTIVE SHIP DETECTION ALGORITHM FOR HRWS SAR IMAGES UNDER COMPLEX BACKGROUND: APPLICATION TO SENTINEL1A DATA

Abstract. Real-time ship detection using synthetic aperture radar (SAR) plays a vital role in disaster emergency and marine security. Especially the high resolution and wide swath (HRWS) SAR images, provides the advantages of high resolution and wide swath synchronously, significantly promotes the wide area ocean surveillance performance. In this study, a novel method is developed for ship target detection by using the HRWS SAR images. Firstly, an adaptive sliding window is developed to propose the suspected ship target areas, based upon the analysis of SAR backscattering intensity images. Then, backscattering intensity and texture features extracted from the training samples of manually selected ship and non-ship slice images, are used to train a support vector machine (SVM) to classify the proposed ship slice images. The approach is verified by using the Sentinl1A data working in interferometric wide swath mode. The results demonstrate the improvement performance of the proposed method over the constant false alarm rate (CFAR) method, where the classification accuracy improved from 88.5 % to 96.4 % and the false alarm rate mitigated from 11.5 % to 3.6 % compared with CFAR respectively.

Multichannel High Resolution Wide Swath SAR Imaging for Hypersonic Air Vehicle with Curved Trajectory.

Synthetic aperture radar (SAR) equipped on the hypersonic air vehicle in near space has many advantages over the conventional airborne SAR. However, its high-speed maneuvering characteristics with curved trajectory result in serious range migration, and exacerbate the contradiction between the high resolution and wide swath. To solve this problem, this paper establishes the imaging geometrical model matched with the flight trajectory of the hypersonic platform and the multichannel azimuth sampling model based on the displaced phase center antenna (DPCA) technology. Furthermore, based on the multichannel signal reconstruction theory, a more efficient spectrum reconstruction model using discrete Fourier transform is proposed to obtain the azimuth uniform sampling data. Due to the high complexity of the slant range model, it is difficult to deduce the processing algorithm for SAR imaging. Thus, an approximate range model is derived based on the minimax criterion, and the optimal second-order approximate coefficients of cosine function are obtained using the two-population coevolutionary algorithm. On this basis, aiming at the problem that the traditional Omega-K algorithm cannot compensate the residual phase with the difficulty of Stolt mapping along the range frequency axis, this paper proposes an Exact Transfer Function (ETF) algorithm for SAR imaging, and presents a method of range division to achieve wide swath imaging. Simulation results verify the effectiveness of the ETF imaging algorithm.

A novel Scan SAR imaging method for maritime surveillance via Lp regularization

ABSTRACTIn this article, a novel Scan mode synthetic aperture radar (SAR) imaging method for maritime surveillance is presented. Conventional Scan SAR is generally operated with severe azimuth resolution loss in order to cover a large area. The proposed imaging method changes the way Scan SAR illuminates sub-scenes and presents a new radar illuminating strategy based on ships’ spatial distribution in each sub-scene. To gain ships’ spatial distribution, a scene sensing algorithm based on radar range profiles together with a peak-seeking and clustering algorithm is introduced. After that, a Markov transfer-probability matrix is generated to make sure that radar illuminates each sub-scene randomly under the probability we calculated before. Finally, an imaging algorithm within the Lp (0 < p ≤ 1) regularization framework is utilized to reconstruct each sub-scene; the regularization problem is solved by an improved iterative thresholding algorithm. The whole wide swath image is joined by putting all the sub-scenes together. Experimental results support that the proposed imaging method can perform high-resolution wide swath SAR imaging effectively and efficiently without reducing the image resolution.

Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection

Synthetic aperture radar (SAR) ship detection has been playing an increasingly essential role in marine monitoring in recent years. The lack of detailed information about ships in wide swath SAR imagery poses difficulty for traditional methods in exploring effective features for ship discrimination. Being capable of feature representation, deep neural networks have achieved dramatic progress in object detection recently. However, most of them suffer from the missing detection of small-sized targets, which means that few of them are able to be employed directly in SAR ship detection tasks. This paper discloses an elaborately designed deep hierarchical network, namely a contextual region-based convolutional neural network with multilayer fusion, for SAR ship detection, which is composed of a region proposal network (RPN) with high network resolution and an object detection network with contextual features. Instead of using low-resolution feature maps from a single layer for proposal generation in a RPN, the proposed method employs an intermediate layer combined with a downscaled shallow layer and an up-sampled deep layer to produce region proposals. In the object detection network, the region proposals are projected onto multiple layers with region of interest (ROI) pooling to extract the corresponding ROI features and contextual features around the ROI. After normalization and rescaling, they are subsequently concatenated into an integrated feature vector for final outputs. The proposed framework fuses the deep semantic and shallow high-resolution features, improving the detection performance for small-sized ships. The additional contextual features provide complementary information for classification and help to rule out false alarms. Experiments based on the Sentinel-1 dataset, which contains twenty-seven SAR images with 7986 labeled ships, verify that the proposed method achieves an excellent performance in SAR ship detection.

Incidence angle normalization of Wide Swath SAR data for oceanographic applications

AbstractA backscattering trend in the range direction of the signal received by Synthetic Aperture Radar (SAR) in Wide Swath (WS) mode results in a progressive reduction of brightness over images from near to far range, which affects the detection and classification of sea surface features on wide swath SAR images. The aim of the present paper is to investigate methods for limiting the issue of Normalized Radar Cross-Section (NRCS or

SAR signal reconstruction from multi-channel non-uniform sampling near singular points

The technique of multiple apertures in azimuth enables a wide swath synthetic aperture radar (SAR) with a high azimuth resolution. In a traditional multi-channel SAR system, if the pulse repetition frequency (PRF) is not an optimum value it will result in a non-uniform sampling. To overcome this limitation, a reconstruction algorithm has been designed to recover the non-uniform signal. In this letter, we claim that while the system PRF is around some specific values, singular points exist in the reconstruction algorithm and therefore the reconstruction cannot get correct result. To avoid the influence of these singular points, a novel method is presented in this letter which can also improve the system signal-to-noise ratio. Furthermore, simulation results are presented to evaluate the performance of this method. At the end of this letter, the real multi-channel SAR data are processed to demonstrate the effectiveness of the proposed method experimentally.

Sea State Primitive Object Creation from SAR Data

Wide swath Synthetic Aperture Radar (SAR) images acquired over sea areas contain a variety of information regarding small scale and mesoscale phenomena in the ocean and marine boundary layer e.g. spills, slicks, surface or internal waves, eddies, oceanic fronts. One of most challenging processing step is to create image objects describing these phenomena on SAR images. The most significant problem in the wide swath images is the backscattering trend at the range direction, which results a progressive brightness reduction over images from near to far range. This reduction affects the detection and classification of sea surface features on wide swath SAR images and a normalization step is needed in a certain incidence angle for compensating the brightness reduction. The aim of the present paper is to investigate the result of image normalization to a set of Wide Swath Mode SAR images. Dark areas were initially detected in SAR images using thresholds, adapted or not. Afterwards, SAR images were normalized and a global threshold was calculated for each image. Images were segmented and objects were created for each dark area. The results were compared to a reference dataset created from theoretical modeled values and extracted in a GIS environment. Results clearly indicate that overall accuracy of the detected dark areas has been increased after normalization. On the contrary, local thresholds were insufficient in producing acceptable results. The proposed normalization can be used as a pre-processing step in image classification.

Identification and tracking of individual sea ice floes from ENVISAT wide swath SAR images: a case study from Fram Strait

Observations from satellites play an important role in tracking sea ice movement. Synthetic aperture radar (SAR) images are preferred because they are neither weather nor daylight dependent. Tracking the movement of individual objects (icebergs or sea ice floes) that may threaten ships or installations is of particular interest. The Ice Tracking from SAR Images (ITSARI) algorithm was originally developed to track large icebergs in the Antarctic. An adaptation of this algorithm to identify and track the movement of sea ice floes in Arctic conditions is presented, using ENVISAT wide swath SAR images of the Fram Strait acquired at 1–3-day intervals during the month of February 2008. It is shown that the algorithm can be successfully adapted to gain a general sense of the direction of movement of the ice due to both winds and ocean currents and, significantly, to identify and track specific objects of interest over a series of images.

A range-dependent autofocus algorithm based on the Tikhonov regularization method

Phase Gradient Autofocus (PGA) is an algorithm developed to estimate phase errors in synthetic aperture radar (SAR) images. It is widely used in SAR image processing and has been shown to be very robust on a variety of imagery and phase error functions. Original PGA makes a narrow beam assumption, which is valid for most SAR systems. But it is known that the narrow beam assumption is not valid for large swath or low-altitude mode SAR, where range-dependent phase errors need to be compensated. Some PGA-based range-dependent autofocus algorithms were proposed to deal with this problem, but during raw data processing, we found that there are ill-posed problems in the linear motion error estimation model that will affect the accuracy of the estimation. In this article, combining the Tikhonov regularization (TR) method with phase gradient estimation, a novel autofocus algorithm is proposed to deal with the ill-posed problem in estimation. Real data processing demonstrates that the new method avoids the effect of parameter errors, estimates the range-dependent phase errors more precisely and finally leads to better focusing quality.

Synthesis Technique of Array-Fed Shaped-Reflector Antenna for DBF-SAR Application

This letter presents a new approach to synthesize patterns of a phased-array-fed shaped-reflector antenna for digital beamforming (DBF) synthetic aperture radar (SAR). This type of reflector antenna is capable of radiating different broad transmitting beams under phase-only control and can generate narrow receiving beams using DBF technique in the elevation direction, which is applicable to a high-resolution wide swath (HRWS) SAR system. The linear array feed locates at the defocused area of the reflector, which can enhance the beam reconfiguring ability. The elevation patterns are optimized mainly by adjusting the excitation coefficients of the array feed using improved Projection Matrix Algorithm (PMA). The azimuth patterns are optimized mainly by adjusting the reflector shape using Differential Evolution method with Global and Local neighborhood (DEGL). The patterns of the shaped reflector antenna fed by a defocused feed array are effectively synthesized, including scanning and reconfiguring beams. A numerical analysis is performed that validates the effectiveness of the approach. The beam direction, beamwidth, and sidelobe level requirements are all met.

Yushu earthquake synergic analysis using multimodal SAR datasets

The large earthquake measuring 7.1 on the Richter scale that occurred in Yushu County, Qinghai Province, China, on April 14, 2010, caused a huge loss of life and major damage to people’s properties. Using three modal synthetic-aperture radar (SAR) datasets, wide-swath SAR, interferometric SAR and polarimetric SAR, the following studies were conducted: (1) From Radarsat-2 wide swath SAR data, the regional geological structures and lithologic distribution characteristics of the Yushu earthquake were analyzed. (2) Using advanced land observing satellite/phased array L-band interferometric SAR data, the coseismic deformation field of the Yushu earthquake was obtained, and the size and spatial distribution of deformation were studied combining with the result of regional geological background analysis. (3) From Radarsat-2 polarimetric SAR data, the H-α-ρ method was used to extract the spatial distribution of building collapse caused by the earthquake in the Yushu urban area, and the reasons for the severe earthquake damage to buildings are also discussed and assessed. The study results demonstrate the effectiveness and important potential of using multimodal SAR data for synergic analysis and assessment of earthquake disasters.