SAR Automatic Target Recognition Research Articles

Retracted: Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN

Retracted: Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN

Active Multiview Fusion Framework for SAR Automatic Target Recognition

Active Multiview Fusion Framework for SAR Automatic Target Recognition

Multiscale Complex-Valued Feature Attention Convolutional Neural Network for SAR Automatic Target Recognition

Multiscale Complex-Valued Feature Attention Convolutional Neural Network for SAR Automatic Target Recognition

Black-Box Universal Adversarial Attack for DNN-Based Models of SAR Automatic Target Recognition

Black-Box Universal Adversarial Attack for DNN-Based Models of SAR Automatic Target Recognition

Improving SAR Automatic Target Recognition via Trusted Knowledge Distillation From Simulated Data

Improving SAR Automatic Target Recognition via Trusted Knowledge Distillation From Simulated Data

Semantics-Assisted Multiview Fusion for SAR Automatic Target Recognition

Semantics-Assisted Multiview Fusion for SAR Automatic Target Recognition

Improving Pre-Training and Fine-Tuning for Few-Shot SAR Automatic Target Recognition

SAR-ATR (synthetic aperture radar-automatic target recognition) is a hot topic in remote sensing. This work suggests a few-shot target recognition approach (FTL) based on the concept of transfer learning to accomplish accurate target recognition of SAR images in a few-shot scenario since the classic SAR ATR method has significant data reliance. At the same time, the strategy introduces a model distillation method to improve the model’s performance further. This method is composed of three parts. First, the data engine, which uses the style conversion model and optical image data to generate image data similar to SAR style and realize cross-domain conversion, can effectively solve the problem of insufficient training data of the SAR image classification model. Second is model training, which uses SAR image data sets to pre-train the model. Here, we introduce the deep Brownian distance covariance (Deep BDC) pooling layer to optimize the image feature representation so that the model can learn the image representation by measuring the difference between the joint feature function of the embedded feature and the edge product. Third, model fine-tuning, which freezes the model structure, except the classifier, and fine-tunes it by using a small amount of novel data. The knowledge distillation approach is also introduced simultaneously to train the model repeatedly, sharpen the knowledge, and enhance model performance. According to experimental results on the MSTAR benchmark dataset, the proposed method is demonstrably better than the SOTA method in the few-shot SAR ATR issue. The recognition accuracy is about 80% in the case of 10-way 10-shot.

Target Recognition in SAR Images by Deep Learning with Training Data Augmentation.

Mass production of high-quality synthetic SAR training imagery is essential for boosting the performance of deep-learning (DL)-based SAR automatic target recognition (ATR) algorithms in an open-world environment. To address this problem, we exploit both the widely used Moving and Stationary Target Acquisition and Recognition (MSTAR) SAR dataset and the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset, which consists of selected samples from the MSTAR dataset and their computer-generated synthetic counterparts. A series of data augmentation experiments are carried out. First, the sparsity of the scattering centers of the targets is exploited for new target pose synthesis. Additionally, training data with various clutter backgrounds are synthesized via clutter transfer, so that the neural networks are better prepared to cope with background changes in the test samples. To effectively augment the synthetic SAR imagery in the SAMPLE dataset, a novel contrast-based data augmentation technique is proposed. To improve the robustness of neural networks against out-of-distribution (OOD) samples, the SAR images of ground military vehicles collected by the self-developed MiniSAR system are used as the training data for the adversarial outlier exposure procedure. Simulation results show that the proposed data augmentation methods are effective in improving both the target classification accuracy and the OOD detection performance. The purpose of this work is to establish the foundation for large-scale, open-field implementation of DL-based SAR-ATR systems, which is not only of great value in the sense of theoretical research, but is also potentially meaningful in the aspect of military application.

A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition

In real-world scenarios, it may not always be possible to collect hundreds of labeled samples per class for training deep learning-based SAR Automatic Target Recognition (ATR) models. This work specifically tackles the few-shot SAR ATR problem, where only a handful of labeled samples may be available to support the task of interest. Our approach is composed of two stages. In the first, a global representation model is trained via self-supervised learning on a large pool of diverse and unlabeled SAR data. In the second stage, the global model is used as a fixed feature extractor and a classifier is trained to partition the feature space given the few-shot support samples, while simultaneously being calibrated to detect anomalous inputs. Unlike competing approaches which require a pristine labeled dataset for pretraining via meta-learning, our approach learns highly transferable features from unlabeled data that have little-to-no relation to the downstream task. We evaluate our method in standard and extended MSTAR operating conditions and find it to achieve high accuracy and robust out-of-distribution detection in many different few-shot settings. Our results are particularly significant because they show the merit of a global model approach to SAR ATR, which makes minimal assumptions, and provides many axes for extendability.

A Novel SAR Automatic Target Recognition Method Based on Fully Complex-Valued Networks

A Novel SAR Automatic Target Recognition Method Based on Fully Complex-Valued Networks

Optronic convolutional neural network for SAR automatic target recognition

Deep learning technology has been widely used in synthetic aperture radar automatic target recognition (SAR ATR) tasks due to its good performance and high efficiency. However, the development of semiconductor technology cannot completely satisfy the demand for electronic hardware with high computational capability to conduct deep neural networks. To solve this dilemma, we propose an optronic convolutional neural network (OPCNN) that can perform SAR ATR tasks directly in optical platform. In OPCNN, all computational operations are implemented in optics with high speed and low energy cost. Electronic platforms are used only to control devices and transmit data information without a massive computational burden. As in digital CNNs, the convolutional layer, downsampling layer, nonlinear activation layer, and fully connected layer are all contained in OPCNN. The simulations demonstrate the feasibility of our OPCNN in solving SAR ATR problems. The good performance in experiments, which achieves 87.4% and 93.8% recognition accuracy on original and denoised moving and stationary target acquisition and recognition dataset, validates the application ability of OPCNN in practical scenario. In one-time recognition tasks, the processing time of our OPCNN is only 0.26 s with the speed of light and the power consumption of our prototype is also far less than digital processer, which is 954 W. Through analysis, our OPCNN obtains the higher processing speed and lower energy cost than digital CNNs with the same structure due to the advantages of optical technology. Also, the scalability of optical structure contributes to build more complex networks to solve complicated dataset without the demand of advanced electronic hardware.

Incremental Learning Based on Anchored Class Centers for SAR Automatic Target Recognition

Although deep learning methods have achieved great success in synthetic aperture radar automatic target recognition (SAR ATR), their accuracies decline sharply as new classes are learned, which is known as catastrophic forgetting. The overlapping or confusion between the representations of new and old classes in the feature space is the main cause of catastrophic forgetting. In this paper, the Incremental Class Anchor Clustering (ICAC) is proposed to address this issue. ICAC solves this problem from three perspectives: first, how to learn the new classes; second, how to enable the model to recognize and classify the old classes; third, how to solve the imbalance between old classes and new classes. To learn the new classes, ICAC adaptively adds new anchored class centers for new classes, and the features of each new class will be clustered around the corresponding anchored class center. To enable the model to recognize and classify the old classes, ICAC stores some exemplars for the old classes to ensure the classification ability of the old classes without losing the old class centers in the feature space. At the same time, ICAC adopts knowledge distillation to further alleviate catastrophic forgetting. To solve the imbalance between old classes and new classes, ICAC proposes a learning strategy named Separable Learning (SL), which computes the losses of the old and new exemplars separately and then adds the two losses to make a gradient descent. Experiments on the MSTAR dataset and OpenSARShip dataset demonstrate the effectiveness of this method in SAR automatic targets recognition.

Incremental SAR Automatic Target Recognition With Error Correction and High Plasticity

Synthetic aperture radar automatic target recognition (SAR ATR) uses computer processing capabilities to infer the classes of the targets without human intervention. For SAR ATR, deep learning gradually emerges as a powerful tool and achieves promising performance. However, it faces serious challenges of how to deal with incremental recognition scenarios. The existing deep learning-based SAR ATR methods usually predefine the total number of recognition classes. In realistic applications, the new tasks/classes will be added continuously. If all old data are stored and mixed with newly added data to update the model, the storage pressure and time consumption make the application infeasible. In this article, the high plastic error correction incremental learning (HPecIL) is proposed to address the model degradation and plasticity decline in the incremental scenario. Multiple optimal models trained on old tasks are used to correct accumulative errors and alleviate model degradation. Moreover, the sharp data distribution shift due to newly added data can also result in the model underperforming. A class-balanced training batch is constructed to deal with the issue of unbalanced data distribution. To make a tradeoff between model stability and model plasticity, low-effect nodes in the model are removed to boost the efficiency of model update. The proposed HPecIL outperforms the other state-of-the-art methods in incremental recognition scenarios. The experimental results demonstrate the effectiveness of the proposed method.

SAR Automatic Target Recognition Method Based on Multi-Stream Complex-Valued Networks

In synthetic aperture radar automatic target recognition (SAR-ATR), target information is usually propagated and reserved in complex-valued form, namely magnitude information and phase information. However, most of the existing SAR target recognition methods only focus on real-valued (magnitude information) calculations and ignore the phase information of targets, yielding poor recognition performance. To overcome this limitation, this paper proposes a multi-stream feature fusion SAR target recognition method based on complex-valued operations, called MS-CVNets, to utilize the phase information of the target effectively. First of all, a series of complex-valued operation blocks are constructed to satisfy the network training in the complex field, such as complex convolution, complex batch normalization, complex activation, complex pooling, complex full connection, etc. Besides, a multi-stream structure is employed by applying different convolution kernels to extract multi-scale information of targets, further enhancing the representation ability of the model. Experimental results on the MSTAR dataset illustrate that, compared with current state-of-the-art real-valued based models, MS-CVNets can achieve better recognition results under both standard operating conditions (SOC) and extended operating conditions (EOC), validating the effectiveness and superiority of the proposed method.

Erratum to “Convolutional Neural Network With Attention Mechanism for SAR Automatic Target Recognition”

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SAR Automatic Target Recognition Based on Supervised Deep Variational Autoencoding Model

SAR Automatic Target Recognition Based on Supervised Deep Variational Autoencoding Model

Multiview Attention CNN-LSTM Network for SAR Automatic Target Recognition

Multiview Attention CNN-LSTM Network for SAR Automatic Target Recognition

Convolutional Neural Network With Attention Mechanism for SAR Automatic Target Recognition

Synthetic aperture radar automatic target recognition (SAR ATR) is a key technique of remote-sensing image recognition, which has many potential applications in the fields of military surveillance, national defense, civil application, and so on. With the development of science and technology, deep convolutional neural network (DCNN) has been widely applied for SAR ATR. However, it is difficult to use deep learning to train models with limited ray SAR images. To resolve this problem, we proposed an effectively lightweight attention mechanism CNN (AM-CNN) model for SAR ATR. Extensive experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set illustrate that the AM-CNN model can achieve a superior recognition performance, and the average recognition accuracy can reach 99.35% on the classification of 10 class targets. Compared with the traditional CNN and the state-of-the-art method, our model is significantly superior to improve performance and efficiency.

SAR Automatic Target Recognition Based on Deep Convolutional Neural Networks

Deep convolutional neural networks (CNN) have recently proven extremely competitive in challenging visible light image and speech recognition tasks. The goal of the present study is to explore the application of automatically learned convolutional network features to radar target recognition. Specifically, a two-stage convolutional-pooling network architecture is designed and error back-propagation algorithm with momentum acceleration strategy is used to learn the network weights in a supervised fashion. The effectiveness of the proposed method is assessed by SAR image classification tasks on the standard benchmark of MSTAR (Moving and Stationary Target Acquisition and recognition) database. Our experiments show the presented method has achieved encouraging results with a correct recognition rate of 95.64% for three classes of targets and 92.86% for ten classes of targets.

Hierarchical Recognition System for Target Recognition from Sparse Representations

A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be obtained, and then they are fed to classifier directly. To obtain more natural sparse feature representation, the Constrained RBM (CRBM) is proposed with solving a generalized optimization problem. Three RBM variants,L1-RNM,L2-RBM, andL1/2-RBM, are presented and introduced to HRS in this paper. The experiments on MSTAR public dataset show that the performance of the proposed HRS with CRBM outperforms current pattern recognition methods in SAR ATR, like PCA + SVM, LDA + SVM, and NMF + SVM.