Full-Scale Aggregated MobileUNet: An Improved U-Net Architecture for SAR Oil Spill Detection.

Oil spills are a major threat to marine and coastal environments. Their unique radar backscatter intensity can be captured by synthetic aperture radar (SAR), resulting in dark regions in the images. However, many marine phenomena can lead to erroneous detections of oil spills. In addition, SAR images of the ocean include multiple targets, such as sea surface, land, ships, and oil spills and their look-alikes. The training of a multi-category classifier will encounter significant challenges due to the inherent class imbalance. Addressing this issue requires extracting target features more effectively. In this study, a lightweight U-Net-based model, Full-Scale Aggregated MobileUNet (FA-MobileUNet), was proposed to improve the detection performance for oil spills using SAR images. First, a lightweight MobileNetv3 model was used as the backbone of the U-Net encoder for feature extraction. Next, atrous spatial pyramid pooling (ASPP) and a convolutional block attention module (CBAM) were used to improve the capacity of the network to extract multi-scale features and to increase the speed of module calculation. Finally, full-scale features from the encoder were aggregated to enhance the network's competence in extracting features. The proposed modified network enhanced the extraction and integration of features at different scales to improve the accuracy of detecting diverse marine targets. The experimental results showed that the mean intersection over union (mIoU) of the proposed model reached more than 80% for the detection of five types of marine targets including sea surface, land, ships, and oil spills and their look-alikes. In addition, the IoU of the proposed model reached 75.85 and 72.67% for oil spill and look-alike detection, which was 18.94% and 25.55% higher than that of the original U-Net model, respectively. Compared with other segmentation models, the proposed network can more accurately classify the black regions in SAR images into oil spills and their look-alikes. Furthermore, the detection performance and computational efficiency of the proposed model were also validated against other semantic segmentation models.

Accurate Range Modeling for High-Resolution Spaceborne Synthetic Aperture Radar.

Spaceborne synthetic aperture radar (SAR) is an advanced microwave imaging technology that provides all-weather and all-day target information. However, as spaceborne SAR resolution improves, traditional echo signal models based on airborne SAR design become inadequate due to the curved orbit, Earth rotation, and increased propagation distance. In this study, we propose an accurate range model for high-resolution spaceborne SAR by analyzing motion trajectory and Doppler parameters from the perspective of the space geometry of spaceborne SAR. We evaluate the accuracy of existing range models and propose an advanced equivalent squint range model (AESRM) that accurately fits the actual range history and compensates for high-order term errors by introducing third-order and fourth-order error terms while maintaining the simplicity of the traditional model. The proposed AESRM's concise two-dimensional frequency spectrum form facilitates the design of imaging algorithms. Point target simulations confirm the effectiveness of the proposed AESRM, demonstrating significant improvements in fitting accuracy for range histories characterized by nonlinear trajectories. The developed AESRM provides a robust foundation for designing imaging algorithms and enables higher resolution and more accurate radar imaging.

Sub-Nyquist SAR Imaging and Error Correction Via an Optimization-Based Algorithm.

Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time-space modulation has been proposed to increase the swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by an optimization-based algorithm. However, these methods suffer from some issues, e.g., manually tuning difficulty and the pre-definition of optimization parameters, and a low signal-noise ratio (SNR) resistance. To address these issues, a reweighted optimization algorithm, named pseudo-ℒ0-norm optimization algorithm, is proposed for the sub-Nyquist SAR system in this paper. A modified regularization model is first built by applying the scene prior information to nearly acquire the number of nonzero elements based on Bayesian estimation, and then this model is solved by the Cauchy-Newton method. Additionally, an error correction method combined with our proposed pseudo-ℒ0-norm optimization algorithm is also present to eliminate defocusing in the motion-induced model. Finally, experiments with simulated signals and strip-map TerraSAR-X images are carried out to demonstrate the effectiveness and superiority of our proposed algorithm.

A SAR Ship Detection Method Based on Adversarial Training.

SAR (synthetic aperture radar) ship detection is a hot topic due to the breadth of its application. However, limited by the volume of the SAR image, the generalization ability of the detector is low, which makes it difficult to adapt to new scenes. Although many data augmentation methods-for example, clipping, pasting, and mixing-are used, the accuracy is improved little. In order to solve this problem, the adversarial training is used for data generation in this paper. Perturbation is added to the SAR image to generate new samples for training, and it can make the detector learn more abundant features and promote the robustness of the detector. By separating batch normalization between clean samples and disturbed images, the performance degradation on clean samples is avoided. By simultaneously perturbing and selecting large losses of classification and location, it can keep the detector adaptable to more confrontational samples. The optimization efficiency and results are improved through K-step average perturbation and one-step gradient descent. The experiments on different detectors show that the proposed method achieves 8%, 10%, and 17% AP (Average Precision) improvement on the SSDD, SAR-Ship-Dataset, and AIR-SARShip, compared to the traditional data augmentation methods.

Ship Detection in Synthetic Aperture Radar Images under Complex Geographical Environments, Based on Deep Learning and Morphological Networks.

Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result in false positives or missed detections. This article introduces a ship detection method for SAR images, which employs deep learning and morphological networks. Initially, adaptive preprocessing is carried out by a morphological network to enhance the edge features of ships and suppress background noise, thereby increasing detection accuracy. Subsequently, a coordinate channel attention module is integrated into the feature extraction network to improve the spatial awareness of the network toward ships, thus reducing the incidence of missed detections. Finally, a four-layer bidirectional feature pyramid network is designed, incorporating large-scale feature maps to capture detailed characteristics of ships, to enhance the detection capabilities of the network in complex geographic environments. Experiments were conducted using the publicly available SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID). Compared with the baseline model YOLOX, the proposed method increased the recall by 3.11% and 0.22% for the SSDD and HRSID, respectively. Additionally, the mean Average Precision (mAP) improved by 0.7% and 0.36%, reaching 98.47% and 91.71% on these datasets. These results demonstrate the outstanding detection performance of our method.

SAR Image Registration: The Combination of Nonlinear Diffusion Filtering, Hessian Features and Edge Points.

Synthetic aperture radar (SAR) image registration is an important process in many applications, such as image stitching and remote sensing surveillance. The registration accuracy is commonly affected by the presence of speckle noise in SAR images. When speckle noise is intense, the number of image features acquired by single-feature-based methods is insufficient. An SAR image registration method that combines nonlinear diffusion filtering, Hessian features and edge points is proposed in this paper to reduce speckle noise and obtain more image features. The proposed method uses the infinite symmetric exponential filter (ISEF) for image pre-processing and nonlinear diffusion filtering for scale-space construction. These measures can remove speckle noise from SAR images while preserving image edges. Hessian features and edge points are also employed as image features to optimize the utilization of feature information. Experiments with different noise levels, geometric transformations and image scenes demonstrate that the proposed method effectively improves the accuracy of SAR image registration compared with the SIFT-OCT, SAR-SIFT, Harris-SIFT, NF-Hessian and KAZE-SAR algorithms.

Attribute Feature Perturbation-Based Augmentation of SAR Target Data.

Large-scale, diverse, and high-quality data are the basis and key to achieving a good generalization of target detection and recognition algorithms based on deep learning. However, the existing methods for the intelligent augmentation of synthetic aperture radar (SAR) images are confronted with several issues, including training instability, inferior image quality, lack of physical interpretability, etc. To solve the above problems, this paper proposes a feature-level SAR target-data augmentation method. First, an enhanced capsule neural network (CapsNet) is proposed and employed for feature extraction, decoupling the attribute information of input data. Moreover, an attention mechanism-based attribute decoupling framework is used, which is beneficial for achieving a more effective representation of features. After that, the decoupled attribute feature, including amplitude, elevation angle, azimuth angle, and shape, can be perturbed to increase the diversity of features. On this basis, the augmentation of SAR target images is realized by reconstructing the perturbed features. In contrast to the augmentation methods using random noise as input, the proposed method realizes the mapping from the input of known distribution to the change in unknown distribution. This mapping method reduces the correlation distance between the input signal and the augmented data, therefore diminishing the demand for training data. In addition, we combine pixel loss and perceptual loss in the reconstruction process, which improves the quality of the augmented SAR data. The evaluation of the real and augmented images is conducted using four assessment metrics. The images generated by this method achieve a peak signal-to-noise ratio (PSNR) of 21.6845, radiometric resolution (RL) of 3.7114, and dynamic range (DR) of 24.0654. The experimental results demonstrate the superior performance of the proposed method.

A Sparse Recovery Algorithm for Suppressing Multiple Linear Frequency Modulation Interference in the Synthetic Aperture Radar Image Domain.

In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method.

Range-Dependent Channel Calibration for High-Resolution Wide-Swath Synthetic Aperture Radar Imagery.

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging with azimuth multi-channel always suffers from channel phase and amplitude errors. Compared with spatial-invariant error, the range-dependent channel phase error is intractable due to its spatial dependency characteristic. This paper proposes a novel parameterized channel equalization approach to reconstruct the unambiguous SAR imagery. First, a linear model is established for the range-dependent channel phase error, and the sharpness of the reconstructed Doppler spectrum is used to measure the unambiguity quality. Furthermore, the intrinsic relationship between the channel phase errors and the sharpness is revealed, which allows us to estimate the optimal parameters by maximizing the sharpness of the reconstructed Doppler spectrum. Finally, the results from real-measured data show that the suggested method performs exceptionally for ambiguity suppression in HRWS SAR imaging.

The Process Analysis Method of SAR Target Recognition in Pre-Trained CNN Models.

Recently, attention has been paid to the convolutional neural network (CNN) based synthetic aperture radar (SAR) target recognition method. Because of its advantages of automatic feature extraction and the preservation of translation invariance, the recognition accuracies are stronger than traditional methods. However, similar to other deep learning models, CNN is a "black-box" model, whose working process is vague. It is difficult to locate the decision reasons. Because of this, we focus on the process analysis of a pre-trained CNN model. The role of the processing to feature extraction and final recognition decision is discussed. The discussed components of CNN models are convolution, activation function, and full connection. Here, the convolution processing can be deemed as image filtering. The activation function provides a nonlinear element of processing. Moreover, the fully connected layers can also further extract features. In the experiment, four classical CNN models, i.e., AlexNet, VGG16, GoogLeNet, and ResNet-50, are trained by public MSTAR data, which can realize ten-category SAR target recognition. These pre-trained CNN models are processing objects of the proposed process analysis method. After the analysis, the content of the SAR image target features concerned by these pre-trained CNN models is further clarified. In summary, we provide a paradigm to process the analysis of pre-trained CNN models used for SAR target recognition in this paper. To some degree, the adaptability of these models to SAR images is verified.

FCCD-SAR: A Lightweight SAR ATR Algorithm Based on FasterNet.

In recent times, the realm of remote sensing has witnessed a remarkable surge in the area of deep learning, specifically in the domain of target recognition within synthetic aperture radar (SAR) images. However, prevailing deep learning models have often placed undue emphasis on network depth and width while disregarding the imperative requirement for a harmonious equilibrium between accuracy and speed. To address this concern, this paper presents FCCD-SAR, a SAR target recognition algorithm based on the lightweight FasterNet network. Initially, a lightweight and SAR-specific feature extraction backbone is meticulously crafted to better align with SAR image data. Subsequently, an agile upsampling operator named CARAFE is introduced, augmenting the extraction of scattering information and fortifying target recognition precision. Moreover, the inclusion of a rapid, lightweight module, denoted as C3-Faster, serves to heighten both recognition accuracy and computational efficiency. Finally, in cognizance of the diverse scales and vast variations exhibited by SAR targets, a detection head employing DyHead's attention mechanism is implemented to adeptly capture feature information across multiple scales, elevating recognition performance on SAR targets. Exhaustive experimentation on the MSTAR dataset unequivocally demonstrates the exceptional prowess of our FCCD-SAR algorithm, boasting a mere 2.72 M parameters and 6.11 G FLOPs, culminating in an awe-inspiring 99.5% mean Average Precision (mAP) and epitomizing its unparalleled proficiency.

Two-Dimensional Barrage Jamming against SAR Using a Frequency Diverse Array Jammer.

Due to the modulation of tiny frequency offset on the array elements, a frequency diverse array (FDA) jammer can generate multiple range-dimension point false targets, and many deception jamming methods against SAR using an FDA jammer have been studied. However, the potential of the FDA jammer to generate barrage jamming has rarely been reported. In this paper, a barrage jamming method against SAR using an FDA jammer is proposed. To achieve two-dimensional (2-D) barrage effect, the stepped frequency offset of FDA is introduced to generate range-dimensional barrage patches, and the micro-motion modulation is employed to increase the extent of barrage patches along the azimuth direction. Mathematical derivations and simulation results demonstrate the validity of the proposed method in generating flexible and controllable barrage jamming.

SAR Image Simulations of Ocean Scenes Based on the Improved Facet TSM.

The facet-based two scale model (FTSM) is widely applied in SAR image simulations of the anisotropic ocean surface. However, this model is sensitive to the cutoff parameter and facet size, and the choice of these two parameters is arbitrary. We propose to make an approximation of the cutoff invariant two scale model (CITSM) to improve the simulation efficiency while remaining the robustness to cutoff wavenumbers. Meanwhile, the robustness to facet sizes is obtained by correcting the geometrical optics (GO) solution, taking into account the slope probability density function (PDF) correction induced by the spectrum within an individual facet. The new FTSM, with less dependence on cutoff parameters and facet sizes, is proved to be reasonable in the comparisons with advanced analytical models and experimental data. Finally, SAR images of the ocean surface and ship wakes with various facet sizes are provided to prove the operability and applicability of our model.

SAR Target Recognition with Limited Training Samples in Open Set Conditions.

It is difficult to collect training samples for all types of synthetic aperture radar (SAR) targets. A realistic problem comes when unseen categories exist that are not included in training and benchmark data at the time of recognition, which is defined as open set recognition (OSR). Without the aid of side-information, generalized OSR methods used on ordinary optical images are usually not suitable for SAR images. In addition, OSR methods that require a large number of samples to participate in training are also not suitable for SAR images with the realistic situation of collection difficulty. In this regard, a task-oriented OSR method for SAR is proposed by distribution construction and relation measures to recognize targets of seen and unseen categories with limited training samples, and without any other simulation information. The method can judge category similarity to explain the unseen category. Distribution construction is realized by the graph convolutional network. The experimental results on the MSTAR dataset show that this method has a good recognition effect for the targets of both seen and unseen categories and excellent interpretation ability for unseen targets. Specifically, while recognition accuracy for seen targets remains above 95%, the recognition accuracy for unseen targets reaches 67% for the three-type classification problem, and 53% for the five-type classification problem.

Multi-Annual Evaluation of Time Series of Sentinel-1 Interferometric Coherence as a Tool for Crop Monitoring.

Interferometric coherence from SAR data is a tool used in a variety of Earth observation applications. In the context of crop monitoring, vegetation indices are commonly used to describe crop dynamics. The most frequently used vegetation indices based on radar data are constructed using the backscattered intensity at different polarimetric channels. As coherence is sensitive to the changes in the scene caused by vegetation and its evolution, it may potentially be used as an alternative tool in this context. The objective of this work is to evaluate the potential of using Sentinel-1 interferometric coherence for this purpose. The study area is an agricultural region in Sevilla, Spain, mainly covered by 18 different crops. Time series of different backscatter-based radar vegetation indices and the coherence amplitude for both VV and VH channels from Sentinel-1 were compared to the NDVI derived from Sentinel-2 imagery for a 5-year period, from 2017 to 2021. The correlations between the series were studied both during and outside the growing season of the crops. Additionally, the use of the ratio of the two coherences measured at both polarimetric channels was explored. The results show that the coherence is generally well correlated with the NDVI across all seasons. The ratio between coherences at each channel is a potential alternative to the separate channels when the analysis is not restricted to the growing season of the crop, as its year-long temporal evolution more closely resembles that of the NDVI. Coherence and backscatter can be used as complementary sources of information, as backscatter-based indices describe the evolution of certain crops better than coherence.

LPDNet: A Lightweight Network for SAR Ship Detection Based on Multi-Level Laplacian Denoising.

Intelligent ship detection based on synthetic aperture radar (SAR) is vital in maritime situational awareness. Deep learning methods have great advantages in SAR ship detection. However, the methods do not strike a balance between lightweight and accuracy. In this article, we propose an end-to-end lightweight SAR target detection algorithm, multi-level Laplacian pyramid denoising network (LPDNet). Firstly, an intelligent denoising method based on the multi-level Laplacian transform is proposed. Through Convolutional Neural Network (CNN)-based threshold suppression, the denoising becomes adaptive to every SAR image via back-propagation and makes the denoising processing supervised. Secondly, channel modeling is proposed to combine the spatial domain and frequency domain information. Multi-dimensional information enhances the detection effect. Thirdly, the Convolutional Block Attention Module (CBAM) is introduced into the feature fusion module of the basic framework (Yolox-tiny) so that different weights are given to each pixel of the feature map to highlight the effective features. Experiments on SSDD and AIR SARShip-1.0 demonstrate that the proposed method achieves 97.14% AP with a speed of 24.68FPS and 92.19% AP with a speed of 23.42FPS, respectively, with only 5.1 M parameters, which verifies the accuracy, efficiency, and lightweight of the proposed method.

System Design and Echo Preprocessing of Spaceborne Squinted Two-Dimensional Beam Scanning Synthetic Aperture Radar.

Conventional squinted sliding spotlight synthetic aperture radar (SAR) imaging suffers from substantial swath width reduction and complex processing requirements due to the continuous variation in the squint angle and the large range cell migration (RCM) throughout the data acquisition interval. A novel two-dimensional (2D) beam scanning mode for high-resolution wide swath (HRWS) imaging is proposed. The key to the novel imaging mode lies in the synchronous scanning of azimuth and range beams, allowing for a broader and more flexible imaging swath with a high geometric resolution. Azimuth beam scanning from fore to aft was used to improve the azimuth resolution, while range beam scanning was adopted to illuminate the oblique wide swath to avoid the large RCM and the serious swath width reduction. Compared with the conventional sliding spotlight mode, both the swath width and swath length could be extended. According to the echo model of this imaging mode, an echo signal preprocessing approach is proposed. The key points of this approach are range data extension and azimuth data upsampling. A designed system example with a resolution of 0.5 m, swath width of 60 km, and azimuth coverage length of 134 km is presented. Furthermore, a simulation experiment on point targets was carried out. Both the presented system example and imaging results of point targets validated the proposed imaging mode.

Integrating Target and Shadow Features for SAR Target Recognition.

Synthetic aperture radar (SAR) sensor often produces a shadow in pairs with the target due to its slant-viewing imaging. As a result, shadows in SAR images can provide critical discriminative features for classifiers, such as target contours and relative positions. However, shadows possess unique properties that differ from targets, such as low intensity and sensitivity to depression angles, making it challenging to extract depth features from shadows directly using convolutional neural networks (CNN). In this paper, we propose a new SAR image-classification framework to utilize target and shadow information comprehensively. First, we design a SAR image segmentation method to extract target regions and shadow masks. Second, based on SAR projection geometry, we propose a data-augmentation method to compensate for the geometric distortion of shadows due to differences in depression angles. Finally, we introduce a feature-enhancement module (FEM) based on depthwise separable convolution (DSC) and convolutional block attention module (CBAM), enabling deep networks to fuse target and shadow features adaptively. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that when only using target and shadow information, the published deep-learning models can still achieve state-of-the-art performance after embedding the FEM.

A Modified Keystone-Based Forward-Looking Arc Array Synthetic Aperture Radar 3D Imaging Method.

An arc array synthetic aperture radar (AA-SAR) is a new type of omnidirectional observation and imaging system. Based on linear array 3D imaging, this paper introduces a keystone algorithm combined with the arc array SAR 2D imaging method and proposes a modified 3D imaging algorithm based on keystone transformation. The first step is to discuss the target azimuth angle, retain the far-field approximation method of the first-order term, analyze the influence of the forward motion of the platform on the along-track position, and realize the two-dimensional focusing of the target slant range-azimuth direction. The second step is to redefine a new azimuth angle variable in the slant-range along-track imaging and use the keystone-based processing algorithm in the range frequency domain to eliminate the coupling term generated by the array angle and the slant-range time. The corrected data are used to perform along-track pulse compression to obtain the focused image of the target and realize the three-dimensional imaging of the target. Finally, in this article, the spatial resolution of the AA-SAR system in the forward-looking state is analyzed in detail, and the change in the spatial resolution of the system and the effectiveness of the algorithm are verified through simulation.

FPGA Implementation of the Chirp-Scaling Algorithm for Real-Time Synthetic Aperture Radar Imaging.

Synthetic aperture radar (SAR), which can generate images of regions or objects, is an important research area of radar. The chirp scaling algorithm (CSA) is a representative SAR imaging algorithm. The CSA has a simple structure comprising phase compensation and fast Fourier transform (FFT) operations by replacing interpolation for range cell migration correction (RCMC) with phase compensation. However, real-time processing still requires many computations and a long execution time. Therefore, it is necessary to develop a hardware accelerator to improve the speed of algorithm processing. In addition, the demand for a small SAR system that can be mounted on a small aircraft or drone and that satisfies the constraints of area and power consumption is increasing. In this study, we proposed a CSA-based SAR processor that supports FFT and phase compensation operations and presents field-programmable gate array (FPGA)-based implementation results. We also proposed a modified CSA flow that simplifies the traditional CSA flow by changing the order in which the transpose operation occurs. Therefore, the proposed CSA-based SAR processor was designed to be suitable for modified CSA flow. We designed the multiplier for FFT to be shared for phase compensation, thereby achieving area efficiency and simplifying the data flow. The proposed CSA-based SAR processor was implemented on a Xilinx UltraScale+ MPSoC FPGA device and designed using Verilog-HDL. After comparing the execution times of the proposed SAR processor and the ARM cortex-A53 microprocessor, we observed a 136.2-fold increase in speed for the 4096 × 4096-pixel image.