Maneuvering target tracking is always a challenging problem due to the complexity of target motion state. Especially with highly maneuvering target, existing tracking algorithms struggle to swiftly and accurately respond to the sudden changes in target motion. In this letter, it is proposed for the first time that we should pay attention to the non-stationarity of maneuvering trajectory segments, and a self-attention-based maneuvering target tracking framework is developed. Specially, the proposed method first resorts to the maneuvering factors acquired from the mean and variance of trajectory segments to extract non-stationary maneuvering information, and then relies on the long-term dependence between observation segments to achieve maneuvering target tracking. Additionally, to enhance the robustness of the tracking model under the sudden change of motion patterns, a local feature embedding module is proposed to extract dynamically the local motion information of maneuvering target. Numerical experiments demonstrate the superiority of our proposed method over advanced deep learning-based maneuvering target tracking methods in enhancing modeling capabilities and improving robustness under abrupt changes in motion patterns.

Most synthetic aperture radar (SAR) automatic target recognition (ATR) methods are developed for the closed-set environment, so these ATR methods can only identify known classes in the target library. However, it is known that the real scenario is open, so it requires the ATR model should be capable of identifying unknown categories while classifying known categories. Therefore, this paper proposes a pseudo-unknown class guided-based open-set learning network for SAR ATR tasks. First, to enhance the separability between known categories, a separable feature embedding space based on von Mises-Fisher (vMF) distribution is established. Second, unknown class decision boundaries are constructed based on pseudo-unknown classes synthesized by known categories, and are expanded based on the idea of contrastive learning, which is very helpful in promoting the ability of the model to identify unknown categories. Experiments on moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate the effectiveness of the proposed method.

Deep learning-based synthetic aperture radar (SAR) automatic target recognition (ATR) have shown great potential recently, however, the performance of these methods is subject to the number of annotated samples. In real application scenarios, it tends to acquire limited number of samples due to the acquisition cost, in which case the exising ATR method is susceptible to over-fitting. To achieve SAR target recognition robustly in the case of limited samples, this paper proposes a global-local information interactive learning network. Specifically, we first develop a global-local interactive learning architecture, which is dedicated to extract global-local discriminative feature by interactively integrating the merits of local convolution and sparse self-attention. Then, a hierarchical feature discrimination module is proposed to improve intra-class compactness and inter-class divergence, thereby boosting the recognition performance of the ATR model. Evaluation experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset illustrate that the proposed method is superior to advanced SAR ATR methods under the condition of limited samples.

Compared to the horizontal bounding box (HBB), the oriented bounding box (OBB) exhibits substantial advantages in SAR ship detection owing to its outstanding ability to reflect the shape and orientation of ships. However, most OBB detectors are anchor-based, which not only requires the manual setting of numerous hyper-parameters but also increases the difficulty of network training and inference due to the introduction of angle information. Therefore, this paper proposes an anchor-free OBB detector to achieve arbitrary-oriented SAR ship detection. Specifically, the OBB is unified as a 6-distance representation related to each sample point, which is conducive to reducing the negative impact of measurement inconsistency on network training. Moreover, to boost the location accuracy of the ship, a neighborhood-weighted rotation non-maximum suppression method is presented to filter high-quality redundant boxes for corner coordinate weighting. Experiments on RBox-SSDD and HRSID illustrate that the proposed method outperforms some advanced arbitrary-oriented ship detectors.

In real synthetic aperture radar (SAR) scenarios, the scarcity of labeled samples is a common problem, which brings challenges to deep-learning based automatic target recognition (ATR) methods. This work proposes an enhanced prototypical network with multiscale region-aware convolution (MRCEPN) to specially tackle the few-shot SAR ATR problem. We first develop a feature extraction module based on multiscale region-aware convolution, which can adaptively adjust convolutional kernels according to each SAR image’s own feature and make full use of variable spatial information, thus augmenting the capability to extract more discriminative features. Then, an enhanced prototypical network is proposed for label prediction, which can update the class prototype with support and query samples together, thus raising the classification accuracy. Moreover, a hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which helpfully improves the recognition performance. Experiments performed on moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate that the proposed method is competitive with some state-of-the-arts for few-shot SAR ATR tasks.

Over the past few years, the flourishing of deep learning has strongly promoted synthetic aperture radar (SAR) automatic target recognition (ATR) advancement. Many SAR ATR methods have performed well under static environment assumptions, but in real application scenarios where target categories continue to increase over time, they are prone to catastrophic forgetting on old categories of targets. In response to this problem, this paper proposes a novel method named layer-wise representative exemplar selection-based incremental learning (LwRSIL) for SAR target recognition. Specifically, we propose a layer-wise representative exemplar selection strategy, which is capable of picking out representative exemplars covering the entire class distribution. To achieve incremental learning of the ATR model, a multi-task mixed loss is formulated to continually learn reasoning capability on new categories while recalling the knowledge of old categories. Experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate that the proposed method is competitive with some state-of-the-art incremental ATR methods.

In realistic synthetic aperture radar (SAR) scenarios, new categories of targets continue to appear over time. It requires the automatic target recognition (ATR) model should continue to accommodate and recognize new categories of targets while maintaining stability on old categories of targets. Therefore, how to strike a glorious balance between plasticity and stability is a matter of wide concern for incremental SAR ATR. In this letter, we propose a new incremental ATR method called dynamic embedding relation distillation network (DERDN), which strives to alleviate the dilemma between plasticity and stability. Specifically, we first develop a dynamic feature embedding model whose parameters can be adjusted adaptively with the data, which is very conducive to continuous generalization to new category targets. To mitigate catastrophic forgetting of model on old categories, we then propose a relation distillation strategy based on exemplar replay to recall knowledge of old categories, thereby improving the stability of the model. Moreover, a hybrid loss that takes into account both label space and embedding space is presented, which ensures inter-class discriminability while enhancing intra-class compactness in the feature space, so as to sustainably accommodate new categories. Evaluation experiments on two benchmark datasets, i.e., moving and stationary target acquisition and recognition (MSTAR) and the synthetic and measured paired labeled experiment (SAMPLE), illustrate that the proposed method is competitive with many state-of-the-arts. The recognition rate of the proposed method is up to 3% higher than that of the best competitor, and its forgetting rate is about 2.5% lower than that of the best competitor.

As an important research branch of infrared small target detection, dense target detection (e.g., drone swarm detection) has always been a topic worth exploring. Currently, existing datasets cover only one or several (sparse) targets, with almost no dataset available for the research on dense small target detection. To advance this kind of search, for the first time, we synthesize two special dense moving target datasets (DMIST-60 and DMIST-100) on DAUB data. They both contain far more than 50 infrared small targets per frame. In the meantime, for evaluating our new datasets and flourishing detection methodology research, we propose a linking-aware sliced network (LASNet) as the baseline of our datasets. It mainly consists of visual feature extraction, motion feature extraction and motion-affinity fusion. The comprehensive experiments on our synthesized datasets confirm: i) both datasets are practical and effective for dense moving infrared small target detection and ii) proposed LASNet could always obviously outperform other compared methods in both sparse and dense target scenarios. Our new datasets and source codes are currently available athttps://github.com/UESTC-nnLab/DMIST.

The existing synthetic aperture radar (SAR) automatic target recognition (ATR) methods have shown impressive results in static scenarios, yet the performance drops sharply as new categories of targets are continuously increased. In response to this problem, this letter proposes a novel ATR method named multi-level adaptive knowledge distillation network (MLAKDN) to achieve incremental SAR target recognition. To be specific, an adaptive weighted distillation strategy is first proposed, which can alleviate the model from forgetting the knowledge of old categories by distilling multi-stage soft label information of old categories at the classification level. Then, a feature distillation method based on gradient maximum criterion is developed to filter and distill discriminative features, so as to further recall more knowledge of old categories at the feature level. Meanwhile, a model rebalancing technique is designed to effectively strike the balance of the model on new categories and old categories. Finally, a weighted incremental classification loss is presented to train the whole model. Experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset and the synthetic and measured paired labeled experiment (SAMPLE) dataset illustrate that the proposed method is superior to some state-of-the-arts for incremental SAR target recognition tasks.

Recently, optical object detection has made significant advancements in the field of remote sensing. However, small-scale object detection is still a major challenge in optical remote sensing image interpretation. Therefore, this letter proposed a novel object detection method called stepwise locating bidirectional pyramid network (Sw-LBPN) to heighten the ability of remote sensing image object detection. Precisely, a stepwise locating attention scheme is proposed to highlight useful information and suppress useless ones of objects step by step at the feature channel level for large-scale remote sensing images. To effectively realize multiscale feature aggregation, a simplified bidirectional feature pyramid network (SBFPN) is designed. Moreover, the skip connection is leveraged in the middle level of SBFPN, aiming at offsetting and reusing small-scale object information. Several experiments on the measured object detection in optical remote sensing images (DIOR) and Northwestern Polytechnical University very high resolution 10-class remote sensing images (NWPU VHR-10) datasets demonstrate the effectiveness and the superiority of the proposed method compared with some state of the arts.

Deep learning-based synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms have achieved outstanding performance under the condition of hundreds or thousands of training samples in recent years. Nevertheless, it is often rare to acquire great quantities of target samples in real SAR application scenarios. This article proposes a novel ATR method called transductive prototypical attention reasoning network (TPARN) to solve the problem of SAR target recognition with only a few training samples. To be specific, a region awareness-based feature extraction model is first developed, which can effectively focus on the target region of interest and suppress the background clutter by embedding direction-aware and position-sensitive information to extract more transferable knowledge. To heighten the discrimination of the sample features, a cross-feature spatial attention module is then proposed following the feature embedding model. Finally, a transductive prototype reasoning method is presented to realize the identity reasoning of the target, which can continuously update each class prototype with training samples and test samples together, thereby improving the classification accuracy. In addition, a marginal adaptive hybrid loss is proposed to obtain a discriminative feature embedding space with intra-class compactness and inter-class divergence, aiming to facilitate subsequent target identity reasoning. Extensive experiments on the moving and stationary target acquisition and recognition (MSTAR) benchmark dataset reveal that the proposed method outperforms some state-of-the-arts under different few-shot SAR ATR tasks.

Data-driven automatic target recognition (ATR) methods have become the mainstream in the synthetic aperture radar (SAR) community at this stage. However, in real SAR application scenarios, the scarcity of training samples is a common problem. Especially in military application scenarios, only a small number of samples of each type of target are usually available. In the case of limited available samples, it is bound to bring challenges to the feature extraction model and classifier inference learning. In this paper, we propose a novel method called adaptive convolutional subspace reasoning network (ACSRNet) to address few-shot SAR target recognition tasks. To be specific, we first present a dynamic-aware convolutional feature embedding network based on the siamese architecture, which can not only learn more transferable knowledge for the few-shot tasks, but also dynamically adjust the convolution kernel according to the input data to extract more discriminative features. To effectively achieve target identity reasoning, we then resort to high-order information of samples, i.e., the idea of adaptive subspace learning, to develop a few-shot subspace classification module, which can online reason the identity of the unknown target through the spanning subspace of training samples. Meanwhile, a dual-loss is designed to train a feature embedding space with intra-class compactness and inter-class divergence, aiming to facilitate subsequent classification. Meta-learning integrating random sampling episode way is introduced into the process of model training to realize few-shot SAR ATR tasks by emulating the human cognitive process. Experimental results on the moving and stationary target acquisition recognition (MSTAR) dataset demonstrate that the proposed method is competitive with some state-of-the-arts for few-shot SAR ATR tasks.

This paper proposes a Bi -similarity prototypical network with capsule-based embedding to solve the problem of few-shot SAR target recognition. The proposed method comprises two procedures, i.e., feature embedding module and Bi-similarity reasoning module. Specifically, we build a feature embed-ding network with capsule operation, which can enable a feature embedding network to extract more informative features by effectively encoding relative spatial relationships between features. To reason the identity of target robustly, we develop a reasoning module based on Bi-similarity metric. Moreover, a mixed loss is proposed to train a discriminative representation space with both intra-class aggregation and inter-class separation. Experimental results on moving and stationary target acquisition and recognition (MSTAR) dataset show that the proposed method is effective and superior to some state-of-art methods in few-shot SAR target recognition tasks.

Sufficient training samples are prerequisite for most existing automatic target recognition (ATR) algorithms to obtain satisfactory recognition performance. Nevertheless, sometimes only a few samples are available in real synthetic aperture radar (SAR) application scenarios. Therefore, this paper proposes a Siamese subspace classification network to address the problem of SAR ATR with insufficient training samples. To be specific, the proposed method first establishes a feature embedding network based on Siamese structure, and leverages contrastive learning to train a low-dimensional representation space with both intra-class compactness and inter-class divergence. A subspace learning-based classifier is then designed to reason the identity of the target. Experimental results on moving and stationary target acquisition and recognition (MSTAR) benchmark data set demonstrate the effectiveness and superiority of the proposed method.

Deep learning-based automatic target recognition (ATR) methods can perform well with sufficient training samples, yet their performance will degrade significantly when the number of training samples available is quite small. Therefore, this paper proposes a multi-task representation learning network to achieve few-shot synthetic aperture radar (SAR) target recognition. On the one hand, the proposed network can perceive the input transformation, identity itself and class discrimination simultaneously. On the other hand, the proposed network can also extract the morphological features of the target and realize the feature refinement by channel attention mechanism. The powerful feature learning ability of the proposed network provides a guarantee for feature extraction under the condition of a few samples. Experiments on moving and stationary target acquisition and recognition (MSTAR) data set validate the effectiveness of the proposed network.

Deep learning has attracted intensive attention in synthetic aperture radar (SAR) automatic target recognition (ATR). Usually, a considerable number of labeled samples are necessary to learn a deep model for obtaining good generalization capability. However, the process of sample labeling is time-consuming and costly. This letter proposes an active self-paced deep learning (ASPDL) approach to SAR ATR. In a nutshell, we first introduce the Bayesian inference into the process of deep model parameter optimization, aiming at learning a robust classification model in the case of a limited number of labeled samples. Next, a cost-effective sample selection strategy is presented to iteratively and actively select the informative samples from a pool of unlabeled samples for labeling. Concretely, high-confidence samples are actively selected through self-paced learning (SPL) way and automatically pseudo-labeled with the current classification model, whereas low-confidence samples are chosen through an active learning strategy and manually labeled. Finally, we update the parameters of the model by minimizing a dual-loss function using a new training set that is constructed by incorporating new labeled samples with original ones. Experiments on the moving and stationary target acquisition and recognition (MSTAR) benchmark data demonstrate that the proposed method can achieve better classification accuracy with relatively few labeled samples compared with some state-of-the-art methods.

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.

In this paper, a class-oriented local structure preserving dictionary learning (CLPDL) algorithm is developed for synthetic aperture radar (SAR) target recognition. Unlike most sparse representation algorithms whose sparse model is predefined via dictionary with atoms being training samples themselves, class-oriented dictionary leaning can derive multiple class-dictionary from training set. To preserve data local structure, a local weighted constraint, i.e., Tikhonov regularization, is introduced into the dictionary learning procedure, which is very helpful for certain challenging scenarios such as configuration recognition and large depression variations. Moreover, to reduce the influence of target aspect sensitivity of SAR image on target recognition, the query sample is represented as a linear combination of class-dictionary to eliminate disturbances. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database demonstrate the validity of the proposed method.

In this paper, a novel method named discriminant neighborhood preserving projections using L1-norm maximization (DNPP-L1) is developed for Synthetic Aperture Radar (SAR) target recognition. The proposed method can preserve the local geometry information from raw high-dimension data and utilize useful class discriminant information to improve the performance of target recognition effectively. The proposed DNPP-L1 is based on L1 norm distance metric, which is very robust for SAR images target with noise. Experimental results on MSTAR database demonstrate the effectiveness of the proposed method.