High-resolution Synthetic Aperture Radar Images Research Articles

DAFCNN: A Dual-Channel Feature Extraction and Attention Feature Fusion Convolution Neural Network for SAR Image and MS Image Fusion

In the field of image fusion, spatial detail blurring and color distortion appear in synthetic aperture radar (SAR) images and multispectral (MS) during the traditional fusion process due to the difference in sensor imaging mechanisms. To solve this problem, this paper proposes a fusion method for SAR images and MS images based on a convolutional neural network. In order to make use of the spatial information and different scale feature information of high-resolution SAR image, a dual-channel feature extraction module is constructed to obtain a SAR image feature map. In addition, different from the common direct addition strategy, an attention-based feature fusion module is designed to achieve spectral fidelity of the fused images. In order to obtain better spectral and spatial retention ability of the network, an unsupervised joint loss function is designed to train the network. In this paper, the Sentinel 1 SAR images and Landsat 8 MS images are used as datasets for experiments. The experimental results show that the proposed algorithm has better performance in quantitative and visual representation when compared with traditional fusion methods and deep learning algorithms.

PPA-Net: Pyramid Pooling Attention Network for Multi-Scale Ship Detection in SAR Images

In light of recent advances in deep learning and Synthetic Aperture Radar (SAR) technology, there has been a growing adoption of ship detection models that are based on deep learning methodologies. However, the efficiency of SAR ship detection models is significantly impacted by complex backgrounds, noise, and multi-scale ships (the number of pixels occupied by ships in SAR images varies significantly). To address the aforementioned issues, this research proposes a Pyramid Pooling Attention Network (PPA-Net) for SAR multi-scale ship detection. Firstly, a Pyramid Pooled Attention Module (PPAM) is designed to alleviate the influence of background noise on ship detection while its parallel component favors the processing of multiple ship sizes. Different from the previous attention module, the PPAM module can better suppress the background noise in SAR images because it considers the saliency of ships in SAR images. Secondly, an Adaptive Feature Balancing Module (AFBM) is developed, which can automatically balance the conflict between ship semantic information and location information. Finally, the detection capabilities of the ship detection model for multi-scale ships are further improved by introducing the Atrous Spatial Pyramid Pooling (ASPP) module. This innovative module enhances the detection model’s ability to detect ships of varying scales by extracting features from multiple scales using atrous convolutions and spatial pyramid pooling. PPA-Net achieved detection accuracies of 95.19% and 89.27% on the High-Resolution SAR Images Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD), respectively. The experimental results demonstrate that PPA-Net outperforms other ship detection models.

Cross-scene target detection based on feature adaptation and uncertainty-aware pseudo-label learning for high resolution SAR images

The characteristics of synthetic aperture radar (SAR) images are easily affected by factors such as sensor parameters, imaging scenes, etc., which may lead to data distributional discrepancies between the ideal training phase and the practical application phase, resulting in generalization performance degradation of deep learning based detectors in practice. In this paper, an accurate and fast domain adaptive detection method is proposed to address the performance degradation problem of cross-scene detection without requiring additional manual annotations. The cross-scene detection performance is gradually improved by confidence-induced unsupervised feature adaptation (CUFA) and uncertainty-aware adaptive pseudo-label learning (UAPL). In CUFA, image-level and instance-level feature adaptation are progressively implemented to bridge the distribution discrepancies between the source and the target domain in the latent feature space. Then, UAPL is performed to adaptively introduce accurate and stable supervisions of target domain according to the learning status of the detector, avoiding error accumulations and further improving the cross-scene target detection performance. The proposed cross-scene detection method is validated on airborne and spaceborne SAR image datasets of different imaging scenes. The experimental results show that by combining both CUFA and UAPL, compared with the second-best comparison method, the proposed method significantly improves the cross-scene target detection performance in SAR images (at least + 6.5% AP50 and F1 in miniSAR and FARAD cross-scene tasks, and at least + 7.5% AP50 and F1 in Gaofen-3 and Sentinel-1 cross-scene tasks), which facilitates application of the deep learning based detectors in the realistic scenarios.

SAR Image Aircraft Target Recognition Based on Improved YOLOv5

Synthetic aperture radar (SAR) is an active ground-surveillance radar system, which can observe targets regardless of time and weather. Passenger aircrafts are important targets for SAR, as it is of great importance for accurately recognizing the type of aircraft. SAR can provide dynamic monitoring of aircraft flights in civil aviation, which is helpful for the efficient management of airports. Due to the unique imaging characteristics of SAR, traditional target-detection algorithms have poor generalization ability, low detection accuracy, and a cumbersome recognition process. Target detection in high-resolution SAR images based on deep-learning methods is currently a major research hotspot. You Only Look Once v5 (YOLOv5) has the problems of missed detection and false alarms. In this study, we propose an improved version of YOLOv5. A multiscale feature adaptive fusion module is proposed to adaptively assign different weights to each scale of the feature layers, which can extract richer semantic and textural information. The SIOU loss function is proposed to replace the original CIOU loss function to speed up the convergence of the algorithm. The improved Ghost structure is proposed to optimize the YOLOv5 network to decrease the parameters of the model and the amount of computation. A coordinate attention (CA) module is incorporated into the backbone section to help extract useful information. The experimental results demonstrate that the improved YOLOv5 performs better in terms of detection without affecting calculation speed. The mean average precision (mAP) value of the improved YOLOv5 increased by 5.8% compared with the original YOLOv5.

High-Resolution and Wide-Swath SAR Imaging with Space–Time Coding Array

To achieve high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) images, this paper focuses on resolving the problem of separating range-ambiguous echoes with the space–time coding (STC) array. At the modeling stage, the transmit elements and pulses of the STC array are configured with time delay and phase coding modulation, which introduces extra degrees of freedom (DOFs) in the transmit domain. To separate the echoes corresponding to different range-ambiguity regions, the equivalent transmit beamforming is performed in the two-dimensional space–frequency domain. Moreover, in order to compensate for the loss of range resolution during the beamforming progress, the frequency splicing method is proposed. At the analysis stage, the distributed target simulation is provided to demonstrate the effectiveness of obtaining HRWS SAR images in the STC radar. Additionally, the performance of resolving range ambiguity is compared with the traditional radar in terms of the range-ambiguity-to-signal ratio (RASR).

An Effective Multi-Layer Attention Network for SAR Ship Detection

The use of deep learning-based techniques has improved the performance of synthetic aperture radar (SAR) image-based applications, such as ship detection. However, all existing methods have limited object detection performance under the conditions of varying ship sizes and complex background noise, to the best of our knowledge. In this paper, to solve both the multi-scale problem and the noisy background issues, we propose a multi-layer attention approach based on the thorough analysis of both location and semantic information. The solution works by exploring the richness of spatial information of the low-level feature maps generated by a backbone and the richness of semantic information of the high-level feature maps created by the same method. Additionally, we integrate an attention mechanism into the network to exclusively extract useful features from the input maps. Tests involving multiple SAR datasets show that our proposed solution enables significant improvements to the accuracy of ship detection regardless of vessel size and background complexity. Particularly for the widely-adopted High-Resolution SAR Images Dataset (HRSID), the new method provides a 1.3% improvement in the average precision for detection. The proposed new method can be potentially used in other feature-extraction-based classification, detection, and segmentation.

Two-Way Generation of High-Resolution EO and SAR Images via Dual Distortion-Adaptive GANs

Synthetic aperture radar (SAR) provides an all-weather and all-time imaging platform, which is more reliable than electro-optical (EO) remote sensing imagery under extreme weather/lighting conditions. While many large-scale EO-based remote sensing datasets have been released for computer vision tasks, there are few publicly available SAR image datasets due to the high costs associated with acquisition and labeling. Recent works have applied deep learning methods for image translation between SAR and EO. However, the effectiveness of those techniques on high-resolution images has been hindered by a common limitation. Non-linear geometric distortions, induced by different imaging principles of optical and radar sensors, have caused insufficient pixel-wise correspondence between an EO-SAR patch pair. Such a phenomenon is not prominent in low-resolution EO-SAR datasets, e.g., SEN1-2, one of the most frequently used datasets, and thus has been seldom discussed. To address this issue, a new dataset SN6-SAROPT with sub-meter resolution is introduced, and a novel image translation algorithm designed to tackle geometric distortions adaptively is proposed in this paper. Extensive experiments have been conducted to evaluate the proposed algorithm, and the results have validated its superiority over other methods for both SAR to EO (S2E) and EO to SAR (E2S) tasks, especially for urban areas in high-resolution images.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.