High-resolution Synthetic Aperture Radar Research Articles

Geolocation Accuracy Validation of High-Resolution SAR Satellite Images Based on the Xianning Validation Field

The geolocation accuracy of Synthetic Aperture Radar (SAR) images is crucial for their application in various industries. Five high-resolution SAR satellites, namely ALOS, TerraSAR-X, Cosmo-SkyMed, RadarSat-2, and Chinese YG-3, provide a vast amount of image data for research purposes, although their geometric accuracies differ despite similar resolutions. To evaluate and compare the geometric accuracy of these satellites under the same ground control reference, a validation field was established in Xianning, China. The rational function model (RFM) was used to analyze the geometric performance of the five satellites based on the Xianning validation field. The study showed that each image could achieve sub-pixel positioning accuracy in range and azimuth direction when four ground control points (GCPs) were placed in the corners, resulting in a root mean square error (RMSE) of 1.5 pixels. The study also highlighted the effectiveness of an automated GCP-matching approach to mitigate manual identification of points in SAR images, and results demonstrate that the five SAR satellite images can all achieve sub-pixel positioning accuracy in range and azimuth direction when four GCPs are used. Overall, the verification results provide a reference for SAR satellite systems’ designs, calibrations, and various remote sensing activities.

Segmented continuously varying pulse interval sequence design for extremely high-resolution spaceborne sliding spotlight SAR

ABSTRACT Synthetic aperture radar (SAR) is an active microwave remote sensing device used to obtain high-resolution SAR images.With the development of SAR, extremely high-resolution SAR (i.e. resolution reaching 0.1 m) plays an increasingly important role in applications such as target recognition. In extremely high-resolution spaceborne SAR, the variation of slant range history becomes extremely severe, resulting in echoes of interest overlapping with transmitted pulses and nadir echoes when transmitting the pulses at constant pulse intervals (PIs), further causing the loss of echo data. Although a continuously varying pulse interval (CVPI) system can expand the tolerance of slant range variation to some extent, overlap still occurs when the variation in slant range exceeds a certain threshold. Thus, this paper proposes segmented CVPI sequence design methods to solve the data acquisition problem in the situation of severe variation in slant range history. The timing diagram- and strip layering diagram-based segmented CVPI sequence design methods, which are suitable for the acquisition of spaceborne SAR data with any variation of slant range, are given. The characteristics of the two methods are analysed. Finally, several computer simulations indicate that the echoes generated by the proposed methods are not overlapped, which verifies the validity of the proposed methods.

Effects of Wind Wave Spectra, Non-Gaussianity, and Swell on the Prediction of Ocean Microwave Backscatter with Facet Two-Scale Model

The image intensity of high-resolution synthetic aperture radar (SAR) is closely related to the facet scattering distribution. In this paper, the effects of wind wave spectra, non-Gaussianity of the sea surface, and swell on the distribution of the facet normalized radar cross section (NRCS) simulated by the facet two-scale model (TSM) are analyzed by comparing the simulated results with the Sentinel-1 SAR data, the Advanced Scatterometer (ASCAT) data, and the geophysical model function (GMF) at the wind speed range of 3–16 m/s, the wind direction range of 0°–360°, and the incidence angle range of 30°–50° under VV and HH polarizations. The results show that the Apel spectrum achieves a more consistent mean NRCS and NRCS distribution with the reference data at low incidence angles, while the composite spectra perform better at high incidence angles under VV polarization. Under HH polarization, the Apel spectrum always has a better performance. The upwind–downwind asymmetry of backscattering can be predicted well by the modified TSM, which is constructed by incorporating bispectrum correction into the conventional TSM. The distribution of the scattering simulated by the modified TSM deviates from the Gaussian distribution significantly, which is in good agreement with the Sentinel-1 data. Additionally, the introduction of swell widens the spread of the NRCS distribution, and the fluctuation range of the NRCS profile considering swell is larger than that without swell. All these changes caused by the introduction of swell make the distribution of the facet scattering more consistent with the Sentinel-1 data. Moreover, the scattering image patterns and scattering image spectrum of the Sentinel-1 data can be successfully simulated at various sea states with the consideration of swell.

High-Resolution mmWave SAR Imagery for Automotive Parking Assistance

Benefiting from the characteristics of low-cost, small-size, and high-resolution, the millimeter-wave (mmWave) radar has been gradually applied to automotive parking assistance. In this article, a novel algorithm of automotive synthetic aperture radar (SAR) imaging is proposed for the mapping of parking places. To deal with the motion error from the inaccurate speed of the radar platform, a parametric method of sparse Bayesian learning (SBL) is presented for well-focused and high-resolution SAR imaging. Then, a watershed-based SAR image segmentation algorithm is applied to detect the vehicles, which can indicate the locations of free parking spaces. Finally, the experimental analysis using 77-GHz automotive radar data is performed to confirm the effectiveness of the proposal.

Multi-Channel Back-Projection Algorithm for mmWave Automotive MIMO SAR Imaging With Doppler-Division Multiplexing

Due to the low-cost and compact size, millimeter-wave radar is gaining increasing attention in automotive applications. Compared with the array imaging technique, synthetic aperture radar (SAR) takes advantage of high resolution, low system complexity, and is an important powerful tool in automotive radar imaging. In this paper, a high-resolution MIMO SAR imaging algorithm is proposed in the framework of Doppler-division multiplexing (DDM), where a novel multi-channel back-projection (BP) approach is presented for coherent multi-channel image integration. In the scheme, the DDM is applied on transmitter and Doppler filtering is employed to separate echoes of different transmitters on one receiver. Then the proposed multi-channel BP algorithm is divided into two parts. The first step corresponds to the BP SAR imaging on each channel, effectively dealing with the problem of near-field and wide-angle focusing. As follows, digital beamforming (DBF) and beampattern synthesis based on alternating direction method of multipliers (ADMM) is used for multi-channel SAR image coherent fusion. Experiments using real-measured data from an automotive radar system with a two-transmitter-eight-receiver array validate the effectiveness of the proposed algorithm.

Toward improved urban flood detection using Sentinel-1: dependence of the ratio of post- to preflood double scattering cross sections on building orientation

High-resolution synthetic aperture radar (SAR) sensors are now commonly used for flood detection. Automated detection tends to be limited to rural areas owing to the complicated backscattering mechanisms occurring in urban areas. Flooding can be identified in urban areas by searching for increased SAR backscatter in a postflood image due to double scattering between water and adjacent buildings, compared with a preflood image where double scattering is between unflooded ground and buildings. For co-polarized data, if φ is the angle between the building wall and the satellite direction of travel, double scattering is strongest for φ = 0 deg and falls off as φ increases. Theoretical studies estimating the ratio of flooded-to-unflooded double scatterer (DS) radar cross section (RCS) using X-band SAR data, found that the ratio was high at φ = 0 deg but only small at φ > 10 deg. Ostensibly, this implies that few DSs might be detected in an urban area. However, experiments on real images have called into question the veracity of the modeling. We describe an empirical study to examine the relationship between the flooded-to-unflooded DS RCS ratio and φ in Sentinel-1 (S-1) C-band data. We use high-resolution light detection and ranging and aerial photographs so that φ can be measured accurately and is based on S-1 images from flood events that occurred in the United Kingdom during the storms of winter 2019 to 2020. Results indicate that vertical–vertical polarization is better than vertical–horizontal at distinguishing flooded from unflooded DS; that the theoretical model used underestimates the number of DS with high RCS ratios in the φ range 10 deg to 30 deg; and that sufficient DS ground heights can be determined to estimate an accurate local average flood level, although in high density housing there are less of these due to the presence of adjacent buildings.

High-Resolution and Wide-Swath SAR Imaging With Sub-Band Frequency Diverse Array

High-resolution and wide-swath imaging serves as an important task for synthetic aperture radar (SAR). However, the tradeoff between high azimuth resolution and wide unambiguous swath coverage has not been well optimized in traditional SAR system. Meanwhile, the ultrawideband signal is required to obtain ultrahigh range resolution imaging. It is not easy to increase the bandwidth directly due to the difficulty of system design. To this end, this article makes notable contribution on a sub-band frequency diverse array framework to realize range ambiguous echoes separation and wideband signal synthesis from narrowband signals. By introducing a small frequency increment across the elements within each subarray, the transmit steering vector within each subarray is range-angle-dependent, making it feasible to resolve range ambiguity in the spatial frequency. In addition, the transmitted waveforms of different subarrays occupy different frequency bands in range frequency domain, which play a pivotal role in achieving high-resolution imaging. With phase compensation and sub-band frequency spectrum splicing technique, the wideband signal can be obtained. Simulation results have verified the effectiveness of the proposed approach.

A Spatial Cross-Scale Attention Network and Global Average Accuracy Loss for SAR Ship Detection

A neural network-based object detection algorithm has the advantages of high accuracy and end-to-end processing, and it has been widely used in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation of ship targets, the complex background of near-shore scenes, and the dense arrangement of some ships make it difficult to improve detection accuracy. To solve the above problem, in this paper, a spatial cross-scale attention network (SCSA-Net) for SAR image ship detection is proposed, which includes a novel spatial cross-scale attention (SCSA) module for eliminating the interference of land background. The SCSA module uses the features at each scale output from the backbone to calculate where the network needs attention in space and enhances the features of the feature pyramid network (FPN) output to eliminate interference from noise, and land complex backgrounds. In addition, this paper analyzes the reasons for the “score shift” problem caused by average precision loss (AP loss) and proposes the global average precision loss (GAP loss) to solve the “score shift” problem. GAP loss enables the network to distinguish positive samples and negative samples faster than focal loss and AP loss, and achieve higher accuracy. Finally, we validate and illustrate the effectiveness of the proposed method by performing it on SAR Ship Detection Dataset (SSDD), SAR-ship-dataset, and High-Resolution SAR Images Dataset (HRSID). The experimental results show that the proposed method can significantly reduce the interference of background noise on the ship detection results, improve the detection accuracy, and achieve superior results to the existing methods.

Blind Super-Resolution for SAR Images with Speckle Noise Based on Deep Learning Probabilistic Degradation Model and SAR Priors

As an active microwave coherent imaging technology, synthetic aperture radar (SAR) images suffer from severe speckle noise and low-resolution problems due to the limitations of the imaging system, which cause difficulties in image interpretation and target detection. However, the existing SAR super-resolution (SR) methods usually reconstruct the images by a determined degradation model and hardly consider multiplicative speckle noise, meanwhile, most SR models are trained with synthetic datasets in which the low-resolution (LR) images are down-sampled from their high-resolution (HR) counterparts. These constraints cause a serious domain gap between the synthetic and real SAR images. To solve the above problems, this paper proposes an unsupervised blind SR method for SAR images by introducing SAR priors in a cycle-GAN framework. First, a learnable probabilistic degradation model combined with SAR noise priors was presented to satisfy various SAR images produced from different platforms. Then, a degradation model and a SR model in a unified cycle-GAN framework were trained simultaneously to learn the intrinsic relationship between HR–LR domains. The model was trained with real LR and HR SAR images instead of synthetic paired images to conquer the domain gap. Finally, experimental results on both synthetic and real SAR images demonstrated the high performance of the proposed method in terms of image quality and visual perception. Additionally, we found the proposed SR method demonstrates the tremendous potential for target detection tasks by reducing missed detection and false alarms significantly.

A Novel Lightweight Attention-Discarding Transformer for High-Resolution SAR Image Classification

Vision Transformer (ViT) has been introduced in high-resolution synthetic aperture radar (HR SAR) image classification due to its excellent global feature extraction ability. However, small samples of SAR images make it difficult to fit the ViT with excessive trainable parameters, which easily results in over-fitting in training. Meanwhile, poor capability in capturing local features of ViT limits its accuracy in SAR image classification. To solve these problems, this letter proposes a new Lightweight Attention-Discarding Transformer (LAD Transformer) for the classification of HR SAR images. In the proposed model, the backbone of the advanced Swin transformer is used to model global information and extract hierarchical features. Moreover, the vital feature extraction part of the LAD Transformer completely discards the self-attention mechanism and extracts local features of SAR images by introducing lighter group convolution and channel shuffle (GC-CS Block). In addition, to address the estimation shift caused by consecutive batch normalization (BN) layers, a new composite normalization method consisting of Batch normalization and Layer Normalization (BLN) in GC-CS Block is proposed. The experiments show that the proposed network has fewer parameters and higher classification accuracy on two real HR SAR data.

DDFormer: A Dual-Domain Transformer for Building Damage Detection Using High-Resolution SAR Imagery

Earthquakes are catastrophic in terms of damage to buildings. Synthetic aperture radar (SAR) has emerged as an effective tool to respond to seismic hazards. However, pre- and post-event high-resolution data are not always available for the affected areas, and the complex geometric properties of buildings pose a challenge to building damage detection. Therefore, this letter proposes the Dual-Domain Transformer (DDFormer) semantic segmentation model for damaged buildings detection using a single post-earthquake high-resolution SAR image. The difference between intact and collapsed building features is enhanced by adaptive frequency and spatial modules. Taking the 2023 Turkey earthquake as an example, the experiments are conducted on two high-resolution co-polarized SAR data (Capella and GF-3). The DDFormer achieves optimal detection accuracy with mean IOU (mIOU) and F-Score of 81.81% and 90%, respectively. In addition, our results are in high consistent with the Turkey Earthquake Report published by Microsoft with a correlation coefficient of 0.626. The above experiments demonstrate the robustness and effectiveness of DDFormer.

DJSPNet: Deep Joint Statistical–Spatial Pooling Network for High-Resolution SAR Image Classification

The previous approaches based on statistical features or spatial features have achieved promising performance on pixel-wise high-resolution (HR) synthetic aperture radar (SAR) image classification, but these methods always can not capture local spatial features and global statistical properties efficiently because of the complex spatial structural patterns and statistical nature in SAR patches. Inspired by this, we propose a deep joint statistical-spatial pooling network, named DJSPNet, for HR SAR image classification, which combines a group second-order statistical feature learning (GSFL) block and an efficient feature-fusion style (EFS) into an end-to-end feature learning block. GSFL block is designed with a group second-order feature learning method in two steps, where the first step divides convolutional channels into several semantic groups; The second step collects second-order feature statistics through calculating pairwise feature interactions within each group. EFS models second-order attentional statistics between statistical characteristics and spatial features by polynomial kernel approximation and guides the discriminative feature activations in SAR patches. More specifically, both GSFL and EFS are stacked and plugged into the encoder stage of conventional U-Net for distinguishable feature learning. Experimental results suggest that the proposed DJSPNet gives better classification performance compared with related deep feature learning networks on a real TerraSAR-X dataset.

Radargrammetry DEM Generation Using High-Resolution SAR Imagery Over La Palma During the 2021 Cumbre Vieja Volcanic Eruption

This letter aims at investigating the potential of high-resolution (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.7\times0.5\,\,\text{m}^{2}$ </tex-math></inline-formula> ) synthetic aperture radar (SAR) images in generating digital elevation models (DEMs) using the radargrammetry technique. In this work, we process two SAR images recorded by the Capella Space X-band satellite-borne radar sensor on two consecutive days, October 2 and 3, 2021, over La Palma (Canary Islands, Spain) during the Cumbre Vieja volcanic eruption. We adopt an iterative point-aggregation algorithm to identify matching pixels between the two images; then, the height estimation is performed using a distance minimization routine over the previously identified pairs of matching points. The resultant radargrammetric DEM is validated against a lidar-based DEM for various land cover (LC) classes, showing a good agreement in the areas less affected by lava flow. An estimation of the lava thickness is performed, yielding profiles of the cone area, which are compared to the photogrammetry estimates obtained from the Pléiades mission data.

A Wavelet-Driven Subspace Basis Learning Network for High-Resolution Synthetic Aperture Radar Image Classification

The feature learning strategy of convolutional neural networks (CNNs) learns the deep spatial features from high-resolution (HR) synthetic aperture radar (SAR) images while ignoring the speckle noise based on SAR imaging mechanism. In the feature learning module, the noise reduction by feature-adaptive projection guided by a powerful embedded wavelet feature reconstruction mechanism can effectively learn the deep feature statistics. In this paper, we present a Wavelet Driven Subspace Basis Learning Network (WDSBLN), following an encoder-decoder architecture, for HR SAR image classification. The powerful wavelet module (PWM), including wavelet decomposition and reconstruction, is employed for keeping the structures of learned features well under speckle noise. Specifically, a compact second-order feature enhancement mechanism is designed for improving the contour and edge information of low-frequency components in the feature decomposition stage, and a local feature attention module based on point-wise convolutional layer is adopted to aggregate the contextual information of local channel and reserves detail information in the high-frequency components. Then the reconstructed feature map is employed as a guided standard in the subspace basis learning (SBL) module. The SBL module, including basis generation (generating the subspace basis vectors) and subspace projection (transforming deep feature maps into a signal subspace), maintains the local structure of HR SAR image patches and acquires the robust feature statistics. We conduct evaluations on three real HR SAR image classification datasets, achieve superior performances as compared to other related networks.

Validation of the Effective Resolution of SMAP Enhanced Resolution Backscatter Products

NASA's Soil Moisture Active Passive (SMAP) mission originally included both passive and active L-band measurement capability. It was the first satellite instrument to provide global L-band radar observations of normalized radar cross section ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma ^{o}$</tex-math></inline-formula> ) at multiple resolutions. The SMAP radar collected high resolution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 1–3 km) synthetic aperture radar (SAR) measurements over most of the Earth's land mass. It simultaneously collected low resolution 6×30 km “slice” and full-footprint 29×35 km measurements. The SMAP radar operated for 83 days from day of the year 103 to 186 in 2015, before the transmitter failed. The SMAP radar was designed to make vegetation roughness measurements in support of the SMAP primary mission to measure soil moisture, but the radar data is useful for a variety of applications, particularly in the polar regions. Unfortunately, limitations in the data download volume precluded downlink of high resolution data over Antarctica, sea ice in the polar regions, and various islands. Nonetheless, low resolution slice and footprint data were collected and downlinked over these areas. To better exploit this low resolution data, this paper employs image reconstruction techniques to create twice-daily enhanced resolution SMAP radar images from the slice and footprint measurements. To validate the resolution enhancement, the enhanced resolution data are compared to SAR results over Greenland and South America. The new data set is provided to the science community to support cryosphere and climate studies.

Optical and SAR Image Dense Registration Using a Robust Deep Optical Flow Framework

The coregistration of optical and synthetic aperture radar (SAR) imageries is the bottleneck in exploring the complementary information from the two multimodal datasets. The difficulties lie in not only the complex radiometric relationship between them, but also the distinct geometrical models of the optical and SAR imaging systems, which cause it nontrivial to explicitly depict the spatial relationship between the corresponding image regions when elevation fluctuations exist. This article aims to investigate the optical flow technique for the pixelwise dense registration of the high-resolution optical and SAR images, so as to get rid of the outlier removal and geometric mapping procedures, which have to be conducted in the classical image registration approaches that are based on sparse feature point matching. Herein, a deep optical flow framework is designed. First, a dilated feature concatenation method is proposed to enhance the discriminability of the pixelwise features for similarity measurement. An effective network training strategy is used, based on a smoothed flow loss, and also a training dataset that contains simulated elevation fluctuations. Second, we propose a self-supervised optical flow fine-tuning strategy. It incorporates the strength of the blockwise matching approach, which produces better matching precision, into the proposed pixelwise matching procedure. In this way, the accuracy of the optical-SAR dense registration is substantially improved. Extensive experiments conducted on the 1-m resolution optical-SAR image pairs of different land-cover types and distinct topographic conditions indicate that the proposed optical-SAR optical flow network -Ft framework is quite robust, and has the potential to perform the optical-SAR image dense registration in practical applications. The Python code of the proposed deep optical flow network will be made available.

A High-Resolution Airborne SAR Autofocusing Approach Based on SR-PGA and Subimage Resampling With Precise Hyperbolic Model

High-resolution airborne synthetic aperture radar (SAR) imaging requires long synthetic aperture time (LSAT). However, the LSAT invalidates the Fresnel approximation in the traditional autofocusing method, leaving a residual signal phase in the motion error. To solve the problem, a signal-reconstruction-based phase gradient autofocus (SR-PGA) and a sub-image resampling (SIR) methods are proposed. They are developed on the hyperbolic model. First, a sub-aperture division strategy divides the full-aperture high-order error into multiple low-order sub-aperture errors. Then, the SR-PGA is developed to estimate the sub-aperture error (SPE), in which the precise deramping is reconstructed to eliminate the residual signal phase in the SPE. Third, the SIR is proposed to eliminate residual Doppler-variant shift of the adjacent sub-images, improving the accuracy of the SPE combination. Finally, simulation and actual data processing verify the effectiveness and validity of the algorithm.

Frequency-Adaptive Learning for SAR Ship Detection in Clutter Scenes

Convolutional neural networks (CNNs) have been widely applied in the context of ship detection in synthetic aperture radar (SAR) images, but the detection performance is still not ideal in scenarios with clutter interference. Mining frequency-domain information to suppress the sea clutter in SAR ship detection has attracted wide attention. However, existing frequency-domain ship detection methods do not process frequency-domain information adaptively, which results in the degradation of ship detection performance. To overcome this problem, this article proposes a novel deep learning network called YOLO-FA. YOLO-FA contains the proposed Frequency Attention Module (FAM), which can process frequency-domain information of SAR images adaptively. The proposed method can suppress the sea clutter in the SAR images with the help of frequency-domain information. We evaluate the proposed method YOLO-FA on two datasets, i.e., the High-Resolution SAR images dataset (HRSID) and SAR Ship Detection Dataset (SSDD). Compared with the baseline method YOLOv5 and existing commonly used methods, YOLO-FA achieves state-of-the-art detection performance on both datasets.

A Stretched Deramping Radar Technique for High-Resolution SAR Processing in Ka-Band Using the Extended Integration Time

To increase the image quality of Synthetic Aperture Radar (SAR) is one of the hot-issue for high-performance detection/reconnaissance. With this interest in the SAR technique, it generates various techniques according to the purposed platforms (Auto-SAR, ViSAR, InSAR, PolSAR, etc.). In this paper, we introduce high efficiency and useful techniques, which can be applied to various SAR applications for generating high-quality images in Ka-band. We propose stretched de-ramping hardware system applicable in Ka-band, and design the Range-Doppler algorithm which well matches the proposed radar system with the modified signal parameters. Thus, we can extend the integration time of the convolution process in slow-time and it leads to the improvement of the SAR image quality. To verify the performance of our proposed technique, we process the practical experiments at conventional outdoor fields. Furthermore, the proposed method is operated on Range Doppler Algorithm (RDA), which is mostly used for standard SAR.

ST-Net: Scattering Topology Network for Aircraft Classification in High-Resolution SAR Images

Aircraft classification in synthetic aperture radar (SAR) images plays a considerable role in global region management and surveillance. Recently, deep learning has been applied to solve the classification problem and made significant progress. Due to the imaging variability at different angles and component scattering discreteness in SAR images, previous works have had difficulty in achieving desirable classification results. To address these issues, we study the positional and semantic relationship between the scattering points and propose an innovative scattering topology network (ST-Net) in this article. First, considering the diversity of imaging results caused by different target attitude angles, we extract and transform the scattering cluster centers to update the information of various categories. It can guide the model to strengthen the discriminative features and mitigate the impact of imaging variability on classification performance. Second, a novel scattering topology module (STM) is introduced to model the spatial relationships and semantic information interaction of discrete scattering points. In this process, the topology relations and scattering characteristics are enhanced for further accurate classification. Third, context attention excitation (CAE) is designed to capture significant global and semantic information, which is conducive to suppressing background interference and reducing category confusion. In conclusion, the ST-Net is presented with the SAR imaging mechanism and the topology geometric representation of aircraft. We construct the SAR aircraft category dataset (SAR-ACD) and conduct extensive experiments on it to show the effectiveness of ST-Net, which illustrates that our method achieves superior classification performance.