Synthetic Aperture Radar Applications Research Articles

Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images.

Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA) and minimum entropy (ME). Although these methods are still widely used for various SAR applications, each method has drawbacks such as limited bandwidth of estimation, low convergence rate, huge computation burden, etc. In this paper, feature preserving autofocus (FPA) algorithm is newly proposed. The algorithm is based on the minimization of the cost function containing a regularization term. The algorithm is designed for postprocessing purpose, which is different from the existing regularization-based algorithms such as sparsity-driven autofocus (SDA). This difference makes the proposed method far more straightforward and efficient than those existing algorithms. The experimental results show that the proposed algorithm achieves better performance, convergence, and robustness than the existing postprocessing autofocus algorithms.

Adaptive Superpixel-Level CFAR Detector for SAR Inshore Dense Ship Detection

Ship monitoring is an important application of synthetic aperture radar (SAR). The constant false alarm rate (CFAR) methods are commonly used for ship detection. However, CFAR detectors usually face challenges for inshore dense ship detection. Due to the significant mixture of ship candidates and sea clutters within the clutter window, the detection threshold may be overestimated leading to many missed detections. To mitigate this issue, a superpixel-level CFAR detector is proposed. The main contribution contains two aspects. First, a labeling procedure is established for pure clutter superpixels and mixture superpixels discrimination in terms of unsupervised clustering. Second, a nonlocal topology strategy is proposed to adaptively determine a sufficient number of pure clutter superpixels for detection threshold estimation. In this vein, an adaptive superpixel-level CFAR approach is constructed and validated with Radarsat-2, Sentinel-1, and AIRSARShip-1 data sets. Comparison studies demonstrate the superiority of the proposed method. Compared with a traditional CFAR detector and two recent superpixel methods, the proposed method achieves clearly better performance for inshore dense ship regions.

Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy

The growing volume of synthetic aperture radar (SAR) imagery acquired by satellite constellations creates novel opportunities and opens new challenges for interferometric SAR (InSAR) applications to observe Earth’s surface processes and geohazards. In this paper, the Parallel Small BAseline Subset (P-SBAS) advanced InSAR processing chain running on the Geohazards Exploitation Platform (GEP) is trialed to process two unprecedentedly big stacks of Copernicus Sentinel-1 C-band SAR images acquired in 2014–2020 over a coastal study area in southern Italy, including 296 and 283 scenes in ascending and descending mode, respectively. Each stack was processed in the GEP in less than 3 days, from input SAR data retrieval via repositories, up to generation of the output P-SBAS datasets of coherent targets and their displacement histories. Use-cases of long-term monitoring of land subsidence at the Capo Colonna promontory (up −2.3 cm/year vertical and −1.0 cm/year east–west rate), slow-moving landslides and erosion landforms, and deformation at modern coastal protection infrastructure in the city of Crotone are used to: (i) showcase the type and precision of deformation products outputting from P-SBAS processing of big data, and the derivable key information to support value-adding and geological interpretation; and (ii) discuss potential and challenges of big data processing using cloud/grid infrastructure.

Prediction of Wide Range Two-Dimensional Refractivity Using an IDW Interpolation Method from High-Altitude Refractivity Data of Multiple Meteorological Observatories

This article proposes a method for the prediction of wide range two-dimensional refractivity for synthetic aperture radar (SAR) applications, using an inverse distance weighted (IDW) interpolation of high-altitude radio refractivity data from multiple meteorological observatories. The radio refractivity is extracted from an atmospheric data set of twenty meteorological observatories around the Korean Peninsula along a given altitude. Then, from the sparse refractive data, the two-dimensional regional radio refractivity of the entire Korean Peninsula is derived using the IDW interpolation, in consideration of the curvature of the Earth. The refractivities of the four seasons in 2019 are derived at the locations of seven meteorological observatories within the Korean Peninsula, using the refractivity data from the other nineteen observatories. The atmospheric refractivities on 15 February 2019 are then evaluated across the entire Korean Peninsula, using the atmospheric data collected from the twenty meteorological observatories. We found that the proposed IDW interpolation has the lowest average, the lowest average root-mean-square error (RMSE) of ∇M (gradient of M), and more continuous results than other methods. To compare the resulting IDW refractivity interpolation for airborne SAR applications, all the propagation path losses across Pohang and Heuksando are obtained using the standard atmospheric condition of ∇M = 118 and the observation-based interpolated atmospheric conditions on 15 February 2019. On the terrain surface ranging from 90 km to 190 km, the average path losses in the standard and derived conditions are 179.7 dB and 182.1 dB, respectively. Finally, based on the air-to-ground scenario in the SAR application, two-dimensional illuminated field intensities on the terrain surface are illustrated.

Multi-view classification with semi-supervised learning for SAR target recognition

A large number of labeled samples are required for convolutional neural network (CNN) to train a deep network model with satisfactory generalization ability. However, it is fairly expensive to obtain sufficient labeled samples in most synthetic aperture radar (SAR) applications. To deal with the problem, in this paper, we propose a novel multi-view classification method with semi-supervised learning for SAR target recognition, which mainly contains a CNN model with the label propagation ability (CNN-LP) and a new expectation maximization (EM) based multi-view fusion strategy. In the training phase, an initial CNN model is trained with limited labeled samples, which is further used to assign pseudo labels for unlabeled samples by the label propagation. Then we can obtain a robust CNN-LP model by alternately updating the model and propagating labels. In the testing phase, the CNN-LP model is used to generate the classification probabilities. To further alleviate the sensitivity of the model towards large depression angle variations, we construct a multi-view label set (MLS) by selecting possible labels adaptively according to the predicted probabilities. Finally, a new EM-based strategy is designed to give the predicted labels. Unlike most of existing multi-view methods which have strict constraints on the angle interval among multiple views, the proposed strategy is free from the aspect interval limitation. Experiments conducted on different datasets all demonstrate the robustness and effectiveness of the proposed method for SAR target recognition.

CG-Net: Conditional GIS-Aware Network for Individual Building Segmentation in VHR SAR Images

Object retrieval and reconstruction from very high resolution (VHR) synthetic aperture radar (SAR) images are of great importance for urban SAR applications, yet highly challenging owing to the complexity of SAR data. This paper addresses the issue of individual building segmentation from a single VHR SAR image in large-scale urban areas. To achieve this, we introduce building footprints from GIS data as complementary information and propose a novel conditional GIS-aware network (CG-Net). The proposed model learns multi-level visual features and employs building footprints to normalize the features for predicting building masks in the SAR image. We validate our method using a high resolution spotlight TerraSAR-X image collected over Berlin. Experimental results show that the proposed CG-Net effectively brings improvements with variant backbones. We further compare two representations of building footprints, namely complete building footprints and sensor-visible footprint segments, for our task, and conclude that the use of the former leads to better segmentation results. Moreover, we investigate the impact of inaccurate GIS data on our CG-Net, and this study shows that CG-Net is robust against positioning errors in GIS data. In addition, we propose an approach of ground truth generation of buildings from an accurate digital elevation model (DEM), which can be used to generate large-scale SAR image datasets. The segmentation results can be applied to reconstruct 3D building models at level-of-detail (LoD) 1, which is demonstrated in our experiments.

Extended convolutional capsule network with application on SAR automatic target recognition

Deep learning-based algorithms have a wide range of applications in synthetic aperture radar (SAR) automatic target recognition (ATR). However, most deep learning-based ATR algorithms perform poorly under extended operation conditions (EOCs), such as large depression variation, noise corruption, limited training samples, partial occlusion, and etc. This paper presents a novel deep neural network named extended convolutional capsule network to achieve robust SAR target recognition under EOCs. The proposed method is composed of an encoder network and a decoder network. Specifically, to mitigate the impact of noise on recognition, we first adopt multiple dilated convolutions to extract multi-scale features in the encoder network. Secondly, a feature refinement module is embedded in the multi-scale channels, which aims to extract discriminative and robust features by adaptively highlighting informative features and suppressing useless ones. Finally, we deploy capsule unit-based feature pose preserving layers to retain the spatial relationship among different features in the top-level network layer of the encoder network, which is very useful for improving the recognition performance under limited training samples. The decoder network is formed by stacking transposed convolutional layers to encourage the encoder network to learn discriminative image features. Experiments on the moving and stationary target acquisition and recognition (MSTAR) data demonstrate the effectiveness of the proposed method.

SAR Image Change Detection Based on Semisupervised Learning and Two-Step Training

Change detection, as an essential part of synthetic aperture radar (SAR) automatic target recognition (ATR) systems, remains a challenging problem. The lack of labeled data seriously impedes the development of deep learning-based methods in SAR applications. In this letter, we propose a patch-based semisupervised method to detect changed pixels from limited training data in the field of SAR. The complete approach includes the unsupervised pretraining stage and the iterative discrimination stage. The states of pixels are determined by comparing the corresponding image patches. First, to reduce the impact of insufficient data, we train a trapezium U-Net-like structure to extract representative and generalized features by unsupervised training on all pixels. Feature maps contain detailed and semantic information due to repetitive feature fusions. A designed feature activation module is utilized to recalibrate fused features. Finally, the discrimination is finished by a simple classification convolutional neural network (CNN) following a two-step training strategy. Ablation studies indicate the function provided by the proposed modifications. Experiments on real SAR images demonstrate that the proposed method can improve the detection accuracy by more than 1.2% compared with other state-of-the-art deep learning-based methods.

High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

High‐Resolution Water Vapor Maps Obtained by Merging Interferometric Synthetic Aperture Radar and GPS Measurements

AbstractPrevious research has demonstrated several successful meteorological applications of interferometric synthetic aperture radar (InSAR) by mapping high‐resolution precipitable water vapor (PWV) over large areas with satisfactory precision (e.g., 1–2 mm). InSAR, however, can only measure temporal changes in the PWV, that is, the differential PWV (ΔPWV) between two SAR acquisition epochs. The construction of absolute PWV maps using InSAR observations remains an intractable problem. We present here a new method for constructing high‐resolution PWV maps by fusing InSAR and global positioning system (GPS) measurements. Our method involves: (1) extracting temporal differences in the zenith wet delays (ΔZWD) from InSAR observations with the assistance of synchronous GPS measurements, and (2) using a constrained minimum variance estimator (CMVE) to construct the high‐resolution maps of the absolute ZWDs by fusing the InSAR‐ΔZWD and the GPS‐ZWD samples, and finally (3) transforming the high‐resolution ZWDs to PWVs using an elevation‐dependent proportionality. Only a single interferogram was needed in our method, so we did not use any temporal (i.e., time‐series) hypotheses, for example, we did not assume that the average value of the temporal turbulent ZWDs was equal to zero. We validated the new method over the Southern California region using four Sentinel‐1 interferograms related to eight SAR images acquired in different seasons and in different years. We also used synchronous GPS measurements from the Southern California integrated GPS network. Eight high‐resolution maps of PWVs related to the eight SAR acquisitions were generated, covering ∼250 km in area at a ∼160 m spatial resolution. Comparisons between the CMVE method and the other two methods (GPS‐only‐based interpolation and time‐series InSAR stacking) were conducted. Experimental results showed that the CMVE method performed significantly better than conventional methods for constructing high‐resolution PWV maps, which is of great interest to a wide community of geophysicists and meteorologists.

Extended Polar Format Algorithm for Large-Scene High-Resolution WAS-SAR imaging

The polar format algorithm (PFA) is a frequency-domain algorithm with high computational efficiency especially for the high resolution imaging applications in wide-angle staring synthetic aperture radar (WAS-SAR). Because of the image distortion and defocus caused by plane wave assumption, the effective imaging scene is limited to a small region which is defined as the defocus-negligible region. In this article, a novel sub-block imaging algorithm which first applied to WAS-SAR is proposed. This method combines sub-block imaging and spatially variant post-filtering (SVPF) which can not only effectively extend the depth of focus of PFA but also greatly reduces the computational burden. What's more, the method of mosaic image is proposed in this article which can eliminate discontinuity between adjacent sub-blocks. First, the large scene is segmented into multiple regions along range direction and the size of each region is still larger than that of the defocus-negligible region. Second, refocusing is implemented by a phase compensation of the already de-chirped signal with the center of sub-blocks, respectively, and each sub-block image is formed by PFA. Third, the region in each sub-block that exceeds the defocus-negligible region is corrected by SVPF and the image distortion can be corrected by 2-D interpolation. Finally, the mosaic image method is applied to get the large-scene image with high quality. The algorithm proposed in this article is efficient and solves the problems in the process of SAR image mosaic such as dislocation and grayscale differences. The simulated and experimental data results demonstrate the effectiveness and practicability of the proposed algorithm.

Performance Analysis of 77 GHz mmWave Radar Based Object Behavior

In this paper, performance analysis of the object behavior using mmwave radar is proposed. The AWR1642 mmwave radar with frequency 77 GHz that has advantages over the other sensors, especially in penetrate materials and high accuracy, is utilized in this research. The paper presents an analysis of object behavior in the experimental method in the indoor environment. The radar will detect an object by sending a chirp signal and receiving it again after it is reflected. The mmwave radar shows the performances in the distance, number of objects, radiation pattern, and velocity. The measurement results show that the object can be detected up to 3 m in the indoor environment with a high level of accuracy and stability. Then, the radar can detect multiple objects in the Line-of-Sight (LOS) condition, where the received power level would be attenuated by about 10 dB after penetrating the first object. The research results showed the beamwidth of the radar is 140 degrees with a directional radiation pattern from 20 degrees to 160 degrees. In this system, radar has been able to identify the velocity of the object accurately. It appears that increasing the speed will affect the Central Processing Unit (CPU) usage on the radar too. The proposed system showed excellent performance in object behavior analysis, and it can be utilized in Synthetic Aperture Radar (SAR) applications.

Complex-Valued Reservoir Computing for Interferometric SAR Applications With Low Computational Cost and High Resolution

Synthetic aperture radar (SAR) is widely used for ground surface classification since it utilizes information on vegetation and soil unavailable in optical observation. Image classification often employs convolutional neural networks. However, they have serious problems such as long learning time and resolution degradation in their convolution and pooling processes. In this paper, we propose complex-valued reservoir computing (CVRC) to deal with complex-valued images in interferometric SAR (InSAR). We classify InSAR image data by using CVRC successfully with a higher resolution and a lower computational cost, i.e., one-hundredth learning time and one-fifth classification time, than convolutional neural networks. We also conduct experiments on slope angle estimation. CVRC is found applicable to quantitative tasks dealing with continuous values as well as discrete classification tasks with a higher accuracy.

Ku-band 500W Amplitude-phase Coherence Pulsed MPM for Ocean Exploration

The spatial high-resolution synthetic aperture radar (SAR) for ocean exploration puts forward higher requirement for output power and amplitude-phase consistency of the pulsed TWT amplifier. The kW class pulsed TWT amplifier based on broadband power combining is the key technology for space SAR applications. In this paper, the factors affecting the amplitude and phase consistency of MPM are studied, and a phase consistency compensation circuit was designed to improve the phase consistency of multiple sets of products. A microwave power model (MPM) with dual TWTs output integrated architecture was proposed which consists of two short travelling wave tubes (TWT), two solid-state power amplifiers (SSPA), and an electronic power conditioner (EPC). More than 45% efficiency is achieved in 15% duty cycle within 600 MHz bandwidth, and the phase consistency was less than ± 5 degrees.With the improvement of synthesis efficiency, higher synthesis power and higher sensitivity of ocean wide scanning and detection can be obtained

Speckle-Free SAR Image Ship Detection.

Ship detection is one of important applications for synthetic aperture radar (SAR). Speckle effects usually make SAR image understanding difficult and speckle reduction becomes a necessary pre-processing step for majority SAR applications. This work examines different speckle reduction methods on SAR ship detection performances. It is found out that the influences of different speckle filters are significant which can be positive or negative. However, how to select a suitable combination of speckle filters and ship detectors is lack of theoretical basis and is also data-orientated. To overcome this limitation, a speckle-free SAR ship detection approach is proposed. A similar pixel number (SPN) indicator which can effectively identify salient target is derived, during the similar pixel selection procedure with the context covariance matrix (CCM) similarity test. The underlying principle lies in that ship and sea clutter candidates show different properties of homogeneity within a moving window and the SPN indicator can clearly reflect their differences. The sensitivity and efficiency of the SPN indicator is examined and demonstrated. Then, a speckle-free SAR ship detection approach is established based on the SPN indicator. The detection flowchart is also given. Experimental and comparison studies are carried out with three kinds of spaceborne SAR datasets in terms of different polarizations. The proposed method achieves the best SAR ship detection performances with the highest figures of merits (FoM) of 97.14%, 90.32% and 93.75% for the used Radarsat-2, GaoFen-3 and Sentinel-1 datasets, accordingly.

Hybrid‐fed shared aperture antenna array for X/K‐band airborne synthetic aperture radar applications

The design of X/K-band shared aperture antenna (SAA) single-linear polarized K-band, dual-polarized (linear/circular) X-band 36-elements (6×6) planar array antenna with high gain, and low cost for airborne synthetic aperture radar systems is presented. To reduce the complexity and size of the feed networks, the concept of series-feed centre-fed open stub array with Chebyshev amplitude distribution of −25 dB side-lobe levels (SLL) are employed for K-band design. Two typical frequencies, X-band and K-band of 9.65 and 21 GHz, are chosen (frequency ratio of 1:2.176) in this proposed SAA design on a single-layer printed circuit board. For K-band design, SLL can be controlled by Chebyshev distribution. The amplitude is controlled by the width of the individual patch in the linear array with left and right symmetries. A 50-Ω microstrip line with 0.7λ spacing is used in between the patches for the ±25° scan angle requirement. The measured results are consistent with the simulations and provide a matching impedance bandwidth of 10.36/1.45% at X/K-bands, respectively. The array also has a high aperture efficiency of more than 85%, SLL of (X/K-bands: −18.9/−25 dB), and gain of (X/K-bands: 24.2/17.4 dBi).

A Phase Filtering Method with Scale Recurrent Networks for InSAR

Phase filtering is a key issue in interferometric synthetic aperture radar (InSAR) applications, such as deformation monitoring and topographic mapping. The accuracy of the deformation and terrain height is highly dependent on the quality of phase filtering. Researchers are committed to continuously improving the accuracy and efficiency of phase filtering. Inspired by the successful application of neural networks in SAR image denoising, in this paper we propose a phase filtering method that is based on deep learning to efficiently filter out the noise in the interferometric phase. In this method, the real and imaginary parts of the interferometric phase are filtered while using a scale recurrent network, which includes three single scale subnetworks based on the encoder-decoder architecture. The network can utilize the global structural phase information contained in the different-scaled feature maps, because RNN units are used to connect the three different-scaled subnetworks and transmit current state information among different subnetworks. The encoder part is used for extracting the phase features, and the decoder part restores detailed information from the encoded feature maps and makes the size of the output image the same as that of the input image. Experiments on simulated and real InSAR data prove that the proposed method is superior to three widely-used phase filtering methods by qualitative and quantitative comparisons. In addition, on the same simulated data set, the overall performance of the proposed method is better than another deep learning-based method (DeepInSAR). The runtime of the proposed method is only about 0.043s for an image with a size of 1024×1024 pixels, which has the significant advantage of computational efficiency in practical applications that require real-time processing.

Satellite radar interferometry for assessing coseismic liquefaction in Portoviejo city, induced by the Mw 7.8 2016 Pedernales, Ecuador earthquake

The Portoviejo city, located in the central part of the Ecuadorian Pacific Coast (province of Manabi), was severely affected during the Pedernales Mw 7.8 earthquake of April 16, 2016, accompanied by coseismic liquefaction phenomena that induced processes of ground subsidence, lateral spreading, sinkhole, and sand boils. The present study proposes the detection and delimitation of the areas affected by the relief deformation, associated liquefaction processes, which occurred in the urban area of Portoviejo city and especially in the Zero Zone of greater destruction after the earthquake, through the application of Differential Interferometry Synthetic Aperture Radar (INSAR) methodology, using Sentinel 1A satellite images, plus the support of geological–geotechnical data obtained from boreholes and standard penetration tests to evaluate the liquefaction potential of the layers of the subsoil based on the Potential Liquefaction Index, considering the seismic scenario of amax 0.5 g for the urban area and the Zero Zone. This procedure allowed the delimitation of areas of high seismic risk for the proper coordination and management of the construction and reconstruction processes in the city of Portoviejo and specifically in the Ground Zero.

Evaluation of Common Building Wall in See-Through-Wall Application of Ultra-wideband Synthetic Aperture Radar

This paper presents the reproduced images of various objects from synthetic aperture radar (SAR) technique with ultra-wideband (UWB) signals to evaluate the performance of common building walls in the see-through-wall capability regarding security applications, object along with human detection. The walls were designed from four materials, i.e., drywall, wood, insulation, and brick, which are used in real environments. The images of the objects behind the walls were performed using the commercial UWB radar with 2.2 GHz frequency bandwidth, 2 ns of pulse width, 10 MHz of internal repetition frequency, as well as an image reconstruction algorithm. From the measurement result, the insulated wall degraded the SAR image quality. Consequently, a material that enhance the UWB signal as Frequency Selective Surface should be considered to enable SAR for disaster applications.

ARU-Net: Reduction of Atmospheric Phase Screen in SAR Interferometry Using Attention-Based Deep Residual U-Net

Atmospheric phase screen (APS) is a very critical issue for the application of interferometric synthetic aperture radar (InSAR) techniques. The spatial-temporal variations of APS are the dominant error source in interferograms and may completely mask displacement signals. Many external meteorological data-based methods and phase-based methods have been developed in the past decades, but all have their inherent limitations. In this article, we propose a deep learning-based method, which is based on an attention-based deep residual U-shaped network (ARU-Net), to mitigate atmospheric artifacts. With this approach, APS patches and clean interferogram patches are sampled from InSAR interferograms to train the network. After training, the network can be used to mitigate the APS for individual interferograms. Compared with the generic atmospheric correction model (GACOS) and the advanced time-series InSAR method distributed scatterer interferometry (DSI), the key advantage of our method is that atmospheric delay can be effectively learned and removed from individual high-resolution interferometric phase itself without external data. Accuracy was validated by using individual and stacked interferograms from TerraSAR-X data over the Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP) sites. The results showed that our method consistently delivered greater standard deviation (SD) reduction after APS correction than the GACOS method. Moreover, the time-series results were in agreement with the DSI and leveling measurements. The effectiveness of the proposed ARU-Net to remove APS effects from interferograms shows great potential for the development of a new set of deep learning-based APS reduction methods.