Limited by the scarcity of synthetic aperture radar (SAR) systems, image augmentation is of great significance to SAR image detection, target recognition, and other application fields. However, traditional image augmentation methods rarely consider the SAR imaging mechanism, resulting in the inability to accurately reflect the anisotropic characteristics of target scattering. This paper introduces a novel SAR image augmentation method based on rebooting auxiliary classifier generative adversarial networks (Re-ACGAN), named IAM-ACGAN (Integrating Attention Mechanism with ACGAN). In this scheme, IAM-ACGAN integrates two attention mechanisms, channel attention (CA) and spatial attention (SA), into the discriminator of the GAN backbone to enhance classification accuracy. These two mechanisms can enhance the channel and spatial features of the input SAR images respectively. A self-constructed simulation ship dataset and a MSTAR real dataset both demonstrate the effectiveness of IAM-ACGAN. Compared with ACGAN and Re-ACGAN augmentation methods, IAM-ACGAN can provide higher image generation accuracy.

Video synthetic aperture radar (SAR) has been found to be very valuable for detecting and tracking moving targets and observing areas of interest. Shadows produced by target motion in sequential radar images can be used to detect targets themselves. Since existing deep learning shadow detection methods often require many hand-designed components, in this paper, we propose a shadow detection method for video SAR moving target based on transformer, which is named Deformable Shadow-DETR. Deformable Shadow-DETR can better extract shadow features, and use the transformer encoder-decoder network to treat shadow detection as a direct set prediction problem, eliminating the need for cumbersome hand-designed components. Experiments on the real video SAR data published by the Sandia National Laboratories show that our proposed moving target shadow detection method can achieve excellent performance.

Due to the existence of system error or the difference in reference coordinates for imaging, there is a significantly spatial mismatch among video SAR frames, which will affect the performance of target localization and tracking greatly. To solve this problem, a PCE-RPM-Net that can achieve video SAR inter-frame images registration is proposed in this paper. PCE-RPM-Net is mainly composed of U-Net and RPM-Net, in which U-Net is adopted to extract features of SAR images to generate point clouds and RPM-Net is used to align the point clouds. Experimental results show that the PCE-RPM-Net has higher registration accuracy and faster speed compared with the classical traditional algorithm.

The wide applications of 3-D synthetic aperture radar (SAR) imaging bring higher requirements for resolution and computational efficiency. The stepped frequency linear frequency modulated signal achieves high range resolution imaging by fusing multiple sub-pulses. However, its wide sub-pulse bandwidth results in a large amount of echo data to be processed, which results in a significant increase in the time consumption of the bandwidth synthesis algorithm. To achieve fast high range resolution 3-D SAR imaging, we propose an inter-array frequency-hopping linear frequency modulated signal model and a 3-D variable carrier frequency back projection algorithm. The proposed signal model transmits only one narrow bandwidth sub-pulse with hopping carrier frequency in each array element, which allows the receiver to sample the echo at a lower frequency. The lower sampling frequency and number of sub-pulse significantly reduce the amount of echo data. The proposed algorithm not only focuses the along-track direction and the cross-track direction of SAR image, but also fuses the low range resolution imaging results obtained by each sub-pulse into a high range resolution imaging result. Benefiting from the fusion of bandwidth synthesis algorithm and imaging algorithm, the computational efficiency is greatly improved. The experimental results demonstrate that the proposed algorithm achieves the comparable resolution and imaging quality as the ideal 3-D back projection (BP) algorithm with a large bandwidth chirp signal. Moreover, the time consumption of the proposed algorithm has been reduced to only 2.25% to 3.02% of that of the advanced high range resolution 3-D BP algorithm.

The success of deep learning in synthetic aperture radar (SAR) automatic target recognition (ATR) relies on a large number of labeled samples; however, there are often wrong (noisy) labels in a large-scale dataset. In this article, we propose a loss curve-fitting-based method, which can identify the noisy labels and train the classification network effectively. We propose to model label noise by unsupervised clustering via fitting loss curve to identify whether the sample’s label is clean or noisy. Then, we train the network using augmented samples with clean labels to correct noisy labels further. The experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset prove that our proposed method can deal with the situation when training a network with different ratios of noisy labels and correct noisy labels effectively. When the noise ratio is small (40%) in the training dataset, our method can correct 97.9% of noisy labels and train the classification network with 98.8% classification accuracy. While the noise ratio is large (80%), our method can correct 78.1% of noisy labels and train the classification network with 79.6% classification accuracy.

This article extends the shadow tracking for video-synthetic aperture radar (SAR) from a single-target framework to a multitarget framework, which is crucial for SAR ground moving targets’ identification. Inspired by FairMOT, the multitarget tracking framework for SAR shadow tracking is improved by using the triplet attention (TriAtt) mechanism and the lightweight multiscale network. By employing the ability to fuse spatial and feature dimensions of TriAtt and combining the lightweight network optimized by multiscale encoder–decoder and dilated convolution, a fast multiscale feature extraction module (FMsFEM) embedded with TriAtt is proposed for better tracking efficiency and performance. Experiments on the Sandiego video-SAR dataset validate that the TriAtt mechanism can improve the tracking performance of deep layer aggregation (DLA)-34, DLA-18, and FMsFEM significantly. FMsFEM with embedded TriAtt outperforms the state-of-the-art network (FairMOT with backbones of DLA-34 and DLA-18) with much faster frame rates. The average frame rates of FMsFEM and FMsFEM-TriAtt reach 60.32 and 56.13 fps for datasets with an image size of$1088\times 608$, which are about three times higher than the frame rates of others.

This article proposes a novel framework by combining a modified real-time recurrent regression (mRe³) network and a newly designed trajectory smoothing long short-term memory (LSTM) network for refocusing the ground moving target (GMT) in the synthetic aperture radar (SAR) image. The mRe^3 network that consists of a convolutional neural network (CNN) backbone and two LSTM modules is designed to track the GMT's shadow in an SAR video. Furthermore, we find that the complex trajectory obtained by the tracking network cannot directly be used for refocusing the GMT because of the estimation error. To address the abovementioned problem, a β-order total variation loss-based smoothing LSTM (TVβ-LSTM) is proposed to recover the GMT's trajectory to meet the requirement of refocusing. Besides, the effect of TVβ on the performance of smoothing LSTM is analyzed. By the experiments on simulated and real SAR videos, we find that the mRe^3 has stronger robustness and a better trajectory reconstruction precision compared with the existing tracking methods, especially for the strong interference cases. In addition, the smoothing LSTM can recover the trajectory of the GMT with higher precision and better smoothness. When β is set to 3, with the TVβ-LSTM, the center distance error of a recovered complex trajectory can be reduced from 0.82 to 0.782, while its fluctuation can be suppressed from 6 to 1 mm. By using our framework, the focused GMT with bountiful geometrical features can be obtained even for the K_a-band SAR.

Convolutional neural network (CNN) has become the mainstream method in the field of image recognition for its excellent ability to feature extraction. Most of the CNNs increase the classification accuracy for the rotational objects by imposing the network with rotation invariance or equivariance property, which causes the loss of the target's orientation information. In this work, a rotation-mapping network (RM-Net) that can achieve objects recognition and angle or orientation estimation simultaneously without additional network training is constructed. Besides, an octagona convolutional kernel is introduced to improve the network's performance. The experiments on the simulation SAR datasets show that the proposed RM-CNN can achieve state-of-the-art results in target recognition and angle estimation.

Deep neural networks have been widely applied and researched in synthetic aperture radar (SAR) object detection and achieved a great success. However, deep supervised networks heavily rely on a large amount of labeled data, while the annotation is difficult and time-consuming to obtain. But the unlabeled data are comparably easier to get. Considering that, we introduce a semi-supervised learning framework for SAR object detection, which is built via label propagation and consistent augmentation. The experiments on a SAR ship dataset prove that the introduced semi-supervised training framework can achieve higher detection performance with utilizing the unlabeled data compared with the corresponding supervised object detection network.

Shadows of ground moving targets in video synthetic aperture radar (SAR) has been found very useful in ground moving target indication (GMTI) for they can indicate the real positions of moving targets at different times, which is significant for SAR reconnaissance and surveillance. However, nearly all the shadow-based SAR GMTI methods only focused on detecting shadows in every separate frame and failed to make full use of the continuous observation ability of video SAR. In this paper, we propose to apply a deep learning-based multi-target tracking method to solve this problem and find that the FairMOT network which jointly detects and re-identifies objects in sequential frames is suitable for this task. To verify its performance, video SAR datasets that contain shadows of ground moving targets are obtained by simulation. The experiments on the simulation datasets show that the introduced network in this work can achieve a state-of-the-art result, for instance, the multiple object tracking accuracy (MOTA) can reach 83.4%.

In synthetic aperture radar (SAR) automatic target recognition, it is expensive and time-consuming to annotate the targets. Thus, training a network with a few labeled data and plenty of unlabeled data attracts attention of many researchers. In this article, we design a semisupervised learning framework including self-consistent augmentation rule, mixup-based mixture, and weighted loss, which allows a classification network to utilize unlabeled data during training and ultimately alleviates the demand of labeled data. The proposed self-consistent augmentation rule forces the samples before and after augmentation to share the same labels to utilize the unlabeled data, which can ensure the prominent effect of supervised learning part of the framework for training by balancing amounts of labeled and unlabeled samples in a minibatch, and makes the network achieve better performance. Then, a mixture method is introduced to mix the labeled, unlabeled, and augmented samples for the better involvement of label information in the mixed samples. By using cross-entropy loss for the mixed-labeled mixtures and mean-squared error loss for the mixed-unlabeled mixtures, the total loss is defined as the weighted sum of them. The experiments on the MSTAR data set and OpenSARShip data set show that the performance of the method is not only far better than the state of the art among current semisupervised-based classifiers but also near to the state of the art among the supervised learning-based networks.

Convolutional Neural Networks (CNNs) have excellent ability in image recognition, however, the requirement of a large amount of labeled dataset limits its application in the field of synthetic aperture radar (SAR) image processing. In this paper, a kernel rotational network (KR-Net) for SAR target recognition is constructed. When the labeled dataset is small, the KR-net can achieve higher classification rate than standard CNNs benefit from its inherent rotational convolution units. Also, weights sharing strategy is introduced to increase network capacity without multiplying the number of weights parameters. Meanwhile, a simple and feasible multi-branch feature converging method for the KR-Net is proposed to fuse features of rotational convolution units. Experimental results show that our network can achieve state-of-art result in the MSTAR dataset, especially when the training set is small.

Deep neural networks have been widely applied and researched in remote sensing image scene classification and achieved a great success. However, deep supervised network heavily relies on a large amount of labeled data. The annotation is difficult and time-consuming to obtain but the unlabeled data are comparably easier to get. Considering that, we introduce a semi-supervised learning framework for remote sensing image scene classification. The network is trained by a novel adaptive perturbation training method. The experiments on NWPU-RESISC45 dataset prove that the introduced semi-supervised classification method can achieve higher classification accuracy with unlabeled data compared with the corresponding supervised classifier, and the designed adaptive perturbation training can further improve the performance of the semi-supervised learning-based classification network.

In this paper, we propose to apply a deep multi-scale encoder-decoder neural network (MsEN-Net) for SAR images enhancement method based on scale-recurrent network (SRN), which consists of encoder and decoder modules trained by the coarse to fine strategy. Simulation with fixed speed errors and experiments with real data are implemented and evaluated by peak signal to noise ratio and structural similarity. Experimental results with both visual and quantitative analysis demonstrate the competitive performance of our proposed method.