Complex Background SAR Target Recognition Based on Convolution Neural Network

In order to improve synthetic aperture radar (SAR) target recognition performance, this paper proposes a novel method using multi-level deep features. The multi-level deep features are learned by the convolutional neural network (CNN), which are capable of describing the target characteristics from different aspects. In order to make full use of the discrimination contained in the multi-level deep features, the joint sparse representation (JSR) is used as the basic classifier, which performs the multi-task learning to jointly classify the multi-level deep features. It could not only represent each feature properly but also consider the correlations between different levels of features. Based on the solutions from JSR, the target label is classified as the training class with the minimum reconstruction error. By fully exploiting the discriminative information contained in the multi-level deep features, the proposed method could effectively enhance SAR target recognition performance. The moving and stationary target acquisition and recognition (MSTAR) dataset is employed in the experiments. The results show that the proposed method could achieve a significantly high recognition rate of 99.38% for classifying 10 classes of targets under the standard operating condition (SOC), which is higher than those from some reference methods drawn from current literatures. Under different types of extended operating conditions (EOCs), the overall performance of the proposed method keeps superior over the reference methods. In addition, the outlier rejection capability of the proposed method is also better than the compared methods. All these experimental results validate the high effectiveness of the proposed method.

Synthetic Aperture Radar (SAR) target classification is an important branch of SAR image interpretation. The deep learning based SAR target classification algorithms have made remarkable achievements. But the acquisition and annotation of SAR target images are time-consuming and laborious, and it is difficult to obtain sufficient training data in many cases. The insufficient training data can make deep learning based models suffering from over-fitting, which will severely limit their wide application in SAR target classification. Motivated by the above problem, this paper employs transfer-learning to transfer the prior knowledge learned from a simulated SAR dataset to a real SAR dataset. To overcome the sample restriction problem caused by the poor feature discriminability for real SAR data. A simple and effective sample spectral regularization method is proposed, which can regularize the singular values of each SAR image feature to improve the feature discriminability. Based on the proposed regularization method, we design a transfer-learning pipeline to leverage the simulated SAR data as well as acquire better feature discriminability. The experimental results indicate that the proposed method is feasible for the sample restriction problem in SAR target classification. Furthermore, the proposed method can improve the classification accuracy when relatively sufficient training data is available, and it can be plugged into any convolutional neural network (CNN) based SAR classification models.

Synthetic aperture radar (SAR) target recognition under extended operating conditions (EOCs) is a challenging problem due to the complex application environment, especially for insufficient target variations and corrupted SAR images in the training samples. This paper proposes a new strategy to solve these problems for target recognition. The SAR images are firstly characterized by multi-scale components of monogenic signal. The generated monogenic features are decomposed to learn a class dictionary and a shared dictionary, which represent the possible intraclass variations information and the common information, respectively. Moreover, a sparse representation of the class dictionary and a dense representation of the shared dictionary are jointly employed to represent a query sample for classification. The validity of the proposed strategy is demonstrated with multiple comparative experiments on moving and stationary target acquisition and recognition (MSTAR) database.

In the field of synthetic aperture radar (SAR) target recognition, leveraging computer-generated labeled simulated SAR data to aid in the recognition of unlabeled real SAR data has garnered significant attention. However, the domain shift between the simulated and real domain contravenes the assumption of independent and identically distributed in deep learning. Moreover, the categories of real SAR data may not align precisely with those of the simulated SAR data, and the prior knowledge of the label shift typically remains unavailable. To align the common category samples and identify target-private unknown category samples in the presence of both domain shift and potential label shift, we introduce a two-stage universal domain adaptation (UniDA) method for simulation-assisted SAR target recognition based on adversarial uncertainty and neighbor relation (AUNR). The proposed AUNR introduces Dirichlet distribution-based evidential deep learning, which not only performs classification tasks but also characterizes decision uncertainty, enabling the model to express the concept of “Unknow”. In the first stage, alongside supervised learning, we leverage data augmentation-based contrastive learning to achieve robust feature representation. In the second stage, adversarial learning based on model and data uncertainty is employed to separate common and unknown class samples, and contrastive learning based on neighbor relation aligns samples in two domains according to their geometric relationships. To the best of our knowledge, this is the first study on UniDA for SAR target recognition. Experimental results demonstrate that our method outperforms previous state of-the-art methods in various label shift scenarios, which indicates the effectiveness of the proposed method.

Images from different aspect views for one target, known as multiple views, are widely applied to improve synthetic aperture radar (SAR) target recognition. However, most of existing multi-view methods have strict constraint on the angle interval among multiple views. In this paper, a new multiview fusion method free from interval limitation using expectation maximization (EM) is explored for SAR image classification. Firstly, we apply convolutional neural network (CNN) to extract features effectively owning to its powerful ability of feature learning and then obtain the classification probability. Secondly, Multi-view Label Set (MLS) is automatically constructed from multiple views according to the probability and finally we use EM algorithm to classify SAR images intelligently. It is worth noting that the proposed method can be used flexibly according to the number of perspectives obtained and without angle interval constraint among multiple views. Experiments demonstrate that the proposed method has better recognition performance than some state-of-the-art methods on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset.

Cross-task and cross-domain SAR target recognition: A meta-transfer learning approach

The current Synthetic Aperture Radar (SAR) target detection methods based on Convolutional Neural Network (CNN) rely on a large amount of slice-level labeled train samples. However, it takes a lot of labor and material resources to label the SAR images at slice-level. Compared to label samples at slice-level, it is easier to label them at image-level. The image-level label indicates whether the image contains the target of interest or not. In this paper, a semi-supervised SAR image target detection method based on CNN is proposed by using a small number of slice-level labeled samples and a large number of image-level labeled samples. The target detection network of this method consists of region proposal network and detection network. Firstly, the target detection network is trained using the slice-level labeled samples. After training convergence, the output slices constitute the candidate region set. Then, the image-level labeled clutter samples are input into the network and then the negative slices of the output are added to the candidate region set. Next, the image-level labeled target samples are input into the network as well. After selecting the positive and negative slices in the output of the network, they are added to the candidate region set. Finally, the detection network is trained using the updated candidate region set. The processes of updating candidate region set and training detection network alternate until convergence. The experimental results based on the measured data demonstrate that the performance of the proposed method is similar to the fully supervised training method using a much larger set of slice-level samples.

To solve the problem of low recognition rate of synthetic aperture radar (SAR) target based on feature recognition, a target recognition method of SAR image based on multi-feature fusion is proposed, which combines Hu moment, Harris corner point and Gabor feature. The three kinds of features describe the target's geometric shape feature, corner feature and image texture feature respectively, which can improve the accuracy of SAR target recognition from the aspect of feature extraction. Based on the MSTAR data set, the experiment is carried out under standard and extended operating conditions. The results show that the proposed method can effectively overcome the deficiency of insufficient single feature description information and improve the SAR target recognition rate to a certain extent.

Because of the special imaging mechanism of Synthetic Aperture Radar (SAR) and the existence of the speckle noise, SAR target configuration recognition has been a hard task in SAR target recognition. We propose a method of SAR target configuration recognition by using the Locality Preserving Projections (LPP), which is a subspace analytical method based on manifold learning. The proposed method extracts features from SAR images in the manifold space which is more suitable for the real SAR images than the Euclidean space. The feature extraction method by LPP not only preserves the global topology structure, but also captures the local information of the target with different configurations. Experimental results on MSTAR datasets suggest that the proposed method can provide a higher recognition rate in SAR target configuration recognition.

Synthetic Aperture Radar (SAR) images are prone to be contaminated by noise, which makes it very difficult to perform target recognition in SAR images. Inspired by great success of very deep convolutional neural networks (CNNs), this paper proposes a robust feature extraction method for SAR image target classification by adaptively fusing effective features from different CNN layers. First, YOLOv4 network is fine-tuned to detect the targets from the respective MF SAR target images. Second, a very deep CNN is trained from scratch on the moving and stationary target acquisition and recognition (MSTAR) database by using small filters throughout the whole net to reduce the speckle noise. Besides, using small-size convolution filters decreases the number of parameters in each layer and, therefore, reduces computation cost as the CNN goes deeper. The resulting CNN model is capable of extracting very deep features from the target images without performing any noise filtering or pre-processing techniques. Third, our approach proposes to use the multi-canonical correlation analysis (MCCA) to adaptively learn CNN features from different layers such that the resulting representations are highly linearly correlated and therefore can achieve better classification accuracy even if a simple linear support vector machine is used. Experimental results on the MSTAR dataset demonstrate that the proposed method outperforms the state-of-the-art methods.

The deep learning has some new vitality to the synthetic aperture radar (SAR) automatic target recognition (ATR). By introducing different constraints, deep belief network (DBN) has become apply to SAR target recognition recent years, but the existing DBN algorithms have some questions including the high training epochs, low recognition rate and complex structure. Therefore, an algorithm based on guided reconstruction and weighted norm-constrained DBN is proposed. Firstly, in order to reduce the dimension of the image output feature, increase the speed of preprocessing, generate a one-dimensional image vector and normalized, a SAR target classification algorithm with two-scale fusion character based on guided filter reconstruction algorithm is introduced. Then, the sparse feature extraction of SAR image is carried out by weighted norm-constrained DBN. By the regularization constraint of the probability distribution, the algorithm can minimize the joint probability distribution of visual layer and hidden layer by samples. The low-dimensional feature is further improved based on the generalized optimization of norm-constrained RBM. Finally, by a regularized Softmax which can classify the targets and obtain output results. The experimental results show that the SAR target recognition algorithm based on the guided reconstruction and weighted constrained deep confidence network not only improves the target recognition performance and generalization ability, but also reduces the output feature dimension and network training times, and the recognition performance of the algorithm is further improved.

In recent years, convolutional neural network (CNN)-based methods have been extensively explored for synthetic aperture radar (SAR) target detection. Nevertheless, the convolutional sampling locations of CNNs cannot accurately fit vehicle targets due to the fixed sampling mechanism in the convolutional kernel. In this paper, we focus on the vehicle target detection task in SAR images and propose a novel rectangle-invariant rotatable convolution (RIRConv) to determine more accurately the convolutional sampling locations for vehicle targets. Specifically, this paper considers the shape characteristic of vehicle targets in SAR images, which always retain a rectangular shape despite having varying sizes, aspect ratios, and rotation angles. The proposed RIRConv equips three additional learnable attribute parameters, namely, width, height, and angle attributes, to adaptively adjust the sampling locations in the convolutional kernel according to the targets. In addition, the RIRConv applies a modulation mechanism to focus on the sampling locations that significantly affect the output. Finally, the RIRConv is introduced into the single-shot multibox detector (SSD) to realize SAR vehicle target detection. In this way, the feature representation capability of SSD for vehicle targets can be enhanced, thus leading to higher detection performance. Notably, the proposed RIRConv is “plug-and-play” and can also be used with other existing advanced technologies to achieve higher detection performance. The experiments based on the measured miniSAR data validate the effectiveness of the proposed method.

The existing deep learning studies on synthetic aperture radar (SAR) automatic target recognition (ATR) mainly focus on the utilization of the amplitude of SAR image via convolutional neural network (CNN), while the electromagnetic scattering information is rarely considered. Given that scattering centers (SCs) can characterize the target’s electromagnetic scattering characteristics and describe the target’s physical structure information, the SC feature should be helpful for SAR ATR. Therefore, we propose a novel SAR ATR method that combines electromagnetic scattering information and graph convolutional network (GCN) effectively and directly. Specifically, we model each extracted SC as a node to convert the SCs into graph data. The constructed graph is learned via GCN to describe the target’s physical structure information, where the features of different GCN layers are fused to avoid the over-smoothing of GCN. Label smoothing is combined with GCN for the first time to alleviate the overfitting caused by the limited training data. To the best of our knowledge, this study is the first to introduce the GCN for effectively utilizing the SCs, proving that the structural characteristics of the SCs of SAR targets are remarkably beneficial for recognition. Extensive experimental results on the measured moving and stationary target acquisition and recognition (MSTAR) dataset show that our method can obtain superior recognition performance compared with the existing methods.

Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm.

This tutorial presents a state-of-the-art survey of advanced applications of Synthetic Aperture Radar (SAR). Applications include: the detection and recognition of targets in SAR imagery, and the detection of targets under trees (and in the open) using change detection (coherent change detection, full-polarization change detection, etc). A fully polarimetric SAR sensor is described. Models of clutter and targets are developed from analysis of full-polarization SAR imagery. Algorithms for optimum (GLRT) processing of full-polarization data are derived, including the polarimetric whitening filter (PWF) and the polarimetric matched filter (PMF). Polarization-based discrimination features (polarization entropy, anisotropy, etc.) are developed and applied to SAR image terrain classification. Model-based and template-based target recognition systems are described and classifier performance results versus resolution and polarization are summarized for 10-and 20-target classifiers. Super-resolution algorithms are applied to SAR imagery and target recognition performance improvements demonstrated. Multi-polarization and multi-pass change detection algorithms are developed and demonstrated using UHF SAR imagery of targets under trees; fusion of multi-polarization coherent and non-coherent change detection algorithms is demonstrated. The effect of SAR image compression on change detection performance is quantified. Change detection performance using multi-pass VHF SAR imagery of targets under trees is demonstrated using data gathered by the Swedish CARABAS SAR system.