With the development of Deep Learning (DL) and Synthetic Aperture Radar (SAR) imaging techniques, Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has come to a breakthrough. Numerous algorithms have been proposed and competitive results have been achieved in detecting different targets. However, due to the influence of various sizes and complex background of ships, detecting multi-scale ships in SAR images is still challenging. To solve the problems, a novel network, called Attention Receptive Pyramid Network (ARPN), is proposed in this paper. ARPN is a two-stage detector and designed to improve the performance of detecting multi-scale ships in SAR images by enhancing the relationships among non-local features and refining information at different feature maps. Specifically, Receptive Fields Block (RFB) and Convolutional Block Attention Module (CBAM) are employed and combined reasonably in Attention Receptive Block (ARB) to build a top-down fine-grained feature pyramid. RFB, composed of several branches of convolutional layers with specifically asymmetric kernel sizes and various dilation rates, is used for grabbing features of ships with large aspect ratios and enhancing local features with their global dependences. CBAM, which consists of channel and spatial attention mechanisms, is utilized to boost significant information and suppress interference caused by surroundings. To evaluate the effectiveness of ARPN, experiments are conducted on SAR Ship Detection Dataset (SSDD) and two large-scene SAR images. The detection results illustrate that competitive performance has been achieved by our method in comparison with several CNN-based algorithms, e.g., Faster-RCNN, RetinaNet, FPN, YOLOv3, DAPN, DS-CNN, HR-SDNet and SER Faster-RCNN.

Recently, deep learning based methods have been successfully applied in synthetic aperture radar automatic target recognition (SAR ATR) fields. However, due to the effects of the special structures of aircrafts and the complexity of SAR imaging mechanism, detecting aircrafts accurately in SAR images is still challenging. To alleviate this problem, a novel network called pyramid attention dilated network (PADN) is proposed in this letter. The key component of PADN is the dilated attention block (DAB), which is composed of two submodules - multibranch dilated convolution module (MBDCM) and convolution block attention module (CBAM). In our method, MBDCM is used to enhance the relationship among discrete backscattering features of aircrafts. CBAM is employed to refine redundant information and highlight significant features of aircrafts. A well-designed fine-grained feature pyramid is established by combining the two modules reasonably into DAB when building lateral connections. To alleviate class imbalance, focal loss (FL) is employed to train our network. Experiments on a mixed SAR aircraft data set illustrate the efficiency of the proposed method for aircraft detection.

Aircraft detection in synthetic aperture radar (SAR) imagery is a challenging task in SAR automatic target recognition (SAR ATR) areas due to aircraft’s extremely discrete appearance, obvious intraclass variation, small size, and serious background’s interference. In this article, a single shot detector (SSD), namely, attentional feature refinement and alignment network (AFRAN), is proposed for detecting aircraft in SAR images with competitive accuracy and speed. Specifically, three significant components, including attention feature fusion module (AFFM), deformable lateral connection module (DLCM), and anchor-guided detection module (ADM), are carefully designed in our method for refining and aligning informative characteristics of aircraft. To represent the characteristics of aircraft with less interference, low-level textural and high-level semantic features of aircraft are fused and refined in AFFM thoroughly. The alignment between aircraft’s discrete backscatting points and convolutional sampling spots is promoted in DLCM. Eventually, the locations of aircraft are predicted precisely in ADM based on aligned features revised by refined anchors. To evaluate the performance of our method, a self-built SAR aircraft sliced dataset and a large scene SAR image are collected. Extensive quantitative and qualitative experiments with detailed analysis illustrate the effectiveness of the three proposed components. Furthermore, the topmost detection accuracy and competitive speed are achieved by our method compared with other domain-specific methods, e.g., dense attention pyramid network (DAPN) and pyramid attention dilated network (PADN), and general convolutional neural network (CNN)-based methods, e.g., Feature Pyramid Network (FPN), Cascade R-CNN, SSD, RefineDet, and RepPoints Detector (RPDet).

Recent years have witnessed a remarkable breakthrough in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) with the development of deep learning (DL). Nonetheless, once deployed, the DL-based methods’ ability to incrementally learn new knowledge from few-shot samples without forgetting the old is fragile, hindering them from discriminating unseen targets in real-world situations. In this paper, we propose a Cosine Prototype Learning (CPL) framework to first unlock few-shot class-incremental learning (FSCIL) in the SAR ATR field inspired by the intrinsic relationships between target azimuth-aware knowledge and semantic features under the cosine criterion. By condensing class-specific characteristics into individual prototypes, stable profiles of targets are depicted without losing generalization. For the model’s plasticity, a pairwise structure separation (PSS) loss is introduced to separate old and new classes and compact intra-class features. Meanwhile, the model’s transferability on new classes is guaranteed by a prototype consistency (PC) loss. For the model’s stability, we propose a prototype-exemplar distillation (PED) loss and a prototype re-calibration (PR) strategy to penalize semantic drifts of old-class feature spaces and alleviate the misalignment of the learned prototypes successively. At inference, a nearest-class-mean (NCM) classifier is adopted for evaluation by comparing cosine similarity scores between testing samples and class-specific prototypes. In experiments, the proposed components of our method are explored by ablation studies. Strong baselines are established, and extensive experiments conducted on the MSTAR dataset show that our method outperforms state-of-the-art methods under various FSCIL conditions, verifying its effectiveness for the FSCIL of SAR ATR.

With the rapid acquisition of high-resolution synthetic aperture radar (SAR) images, new categories are continually observed with few-shot instances in openly noncooperative scenarios. Powering an SAR automatic target recognition (SAR ATR) system with an ability of few-shot class-incremental learning (FSCIL) is nontrivial. Observing the pronounced azimuth-dependence and part-sparsity of targets in SAR images, an azimuth-aware subspace classifier (AASC) on the Grassmannian manifold is proposed to tackle the FSCIL of SAR ATR stably and accurately. In the AASC, losses covering both semantic and manifold facets, which include semantic margin separation (SMS), deep subspace separation (DSS), and structure less forgetting (SLF), are designed to strike both the intrinsic model’s stability and plasticity dilemma and domain-specific challenges. For plasticity, the novel-to-old semantic margins are enlarged by the SMS loss for knowledge transferring while avoiding inappropriate adaptions. The DSS loss derived from the Grassmannian geometry aims to regularize class subspaces orthogonality. For stability, semantic drifts of target spatial and global structures are punished by the SLF loss. As the periodicity and volatility of target azimuth-aware patterns, an azimuth-aware exemplar selection (AES) strategy is designed to select representative and complementary exemplars. In experiments, the advantages of the subspace classifier and the designed losses and strategies are deeply verified. Comprehensive experiments on three FSCIL scenarios derived from both airborne and spaceborne datasets, including the moving and stationary target acquisition and recognition (MSTAR), the SAR-AIRcraft-1.0, and self-collected datasets, show that our method significantly outperforms various task-specific benchmarks, verifying its effectiveness for the FSCIL in real-SAR ATR scenarios.

Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has ushered in a new era dominated by deep-learning (DL) techniques. However, the DL-based recognition systems inevitably confront Catastrophic forgetting for learned knowledge and Overfitting for the new, once deployed in openly dynamic scenarios where targets of new classes continually appear with few-shot instances. For practical applications, a Decoupled Self-supervised Subspace Classifier (DSSC) with Few-Shot Class-Incremental Learning (FSCIL) ability is proposed for prompt knowledge transferring and stable discrimination w.r.t., intrinsic and domain-specific challenges of the FSCIL of SAR ATR. Specifically, observing the significant componentity and azimuth sensitivity of targets in SAR imagery, two self-supervised tasks powered by a Scattering Mixup Module (SMM) and a Rotation-aware Transformation Module (RTM) are designed to synthesize virtual samples and related labels to unleash the classifier's transferability to future categories while enhancing its discriminability to fine-grained scattering patterns. Once deployed, the model's parameters are frozen to decoupled with dynamic worlds for general knowledge extraction. At inference, a subspace classifier with class-aware target priors proposed by a Max-Coverage Feature Selection (MCFS) mechanism is formed for stable point-to-space discrimination. Extensive experiments on three FSCIL datasets built from SAR-AIRcraft-1.0, Self-owned, and MSTAR data sets, which cover various categories captured by airborne and spaceborne SAR payloads, show the state-of-the-art performance achieved by our method compared to numerous latest benchmarks.