Deep learning (DL) techniques have recently ignited remarkable prosperity in the Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) field. Nevertheless, as targets of new categories are observed continually with few-shot examples in openly dynamic scenarios, endowing the DL-based SAR ATR systems with Few-Shot Class-Incremental Learning (FSCIL) ability is urgently demanded. In response, a Decoupled Scattering Augmentation Classifier (DSAC) is proposed to mitigate both intrinsic and domain-specific challenges of the FSCIL of SAR ATR. Specifically, as the significant partability of target structures in SAR imagery, virtual targets with potential scattering patterns are synthesized and pre-allocated by a Scattering Augmentation Module (SAM) to unleash the model’s forward compatibility for future categories. Once deployed, the DSAC is decoupled with dynamic worlds for prompt knowledge representation. Also, a prototypical Nearest-Class-Mean (NCM) classifier with cosine criterion is leveraged for stable and general identification. Extensive experiments conducted on an FSCIL of SAR ATR dataset verify the superiority of our method compared to various latest benchmarks.

Deep neural network (DNN)-based synthetic aperture radar automatic target recognition (SAR ATR) methods have made great progress in recent years. However, the performance of DNN models relies on a large number of independent and identically distributed measured synthetic aperture radar (SAR) images, which is contrary to the SAR ATR in practice. Furthermore, DNN models also suffer from catastrophic forgetting when learning a sequence of new classes. To tackle these problems, we introduce simulated data into the class incremental learning of SAR ATR for the first time. Specifically, we aim to continuously learn a sequence of new classes with a small amount of measured data and a large amount of simulated data. We first investigate the properties of incremental learning using simulated data, and the main observation is that simulated data can achieve good performance in short-term incremental learning rather than long-term incremental learning. A novel class incremental learning method, namely, feature guided evolution and distillation (FGED), is then presented. On the one hand, FGED encourages simulated data to have the same feature relationship structure as the corresponding measured data to reduce their distribution discrepancy in short-term incremental learning. On the other hand, FGED adopts a feature distillation strategy to simultaneously reduce the distribution discrepancy accumulation of previous incremental classes and alleviate the catastrophic forgetting in long-term incremental learning. The experimental results obtained on the MSTAR benchmark dataset and two simulated datasets demonstrate the effectiveness of FGED.

With the rapid acquisition of high-resolution Synthetic Aperture Radar(SAR) images, new categories are continually observed with few-shot instances in openly non-cooperative scenarios. Powering a 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 MSTAR, the SAR-AIRcraft-1.0, and self-collected data sets, show that our method significantly outperforms various task-specific benchmarks, verifying its effectiveness for the FSCIL in real SAR ATR scenarios.

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.

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).

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.