In practical SAR target detection applications, it is often encountered that the training and testing data come from different SAR sensors, leading to a decline in SAR target detection performance. Although domain adaptation methods can achieve model generalization through feature transfer, the change of scattering characteristics for the same target and the difference of feature distribution, caused by cross-sensor, cannot be ignored in SAR images. It is inevitable to lead to the escalation of the offset in the bounding box regression and deviation of the feature alignment. To address these issues, a cross-sensor SAR image target detection method based on dynamic feature discrimination and center-aware calibration is proposed. Based on the domain adaptation framework, initially, a Dynamic Feature Discrimination Module (DFDM) is introduced to address the exacerbated offset in the regression. A bidirectional spatial feature aggregation mechanism is employed to aggregate features in both horizontal and vertical directions and a multi-scale structure is adopted to enhance the scattering and semantic features, which can dynamically constrain the target position while improving target discrimination capability. Then, the Center-Aware Calibration Module (CACM) is designed to address the alignment deviation in feature transfer. The target salience relationship is modeled based on the distance between different positions and the target center to suppress background clutter interference. The perception center of the target is focused by combining the centerness map and classification map, which can calibrate the domain-invariant features and alleviate misalignment. Finally, the proposed method is tested on two datasets, MiniSAR and FARAD, and compared with the latest domain adaption methods. Both mAP and F1 values have improved by more than 6%-20%, verifying the effectiveness of the proposed method.

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.

Fine-grained aircraft detection from synthetic aperture radar (SAR) imagery is of significance in transportation and military domains. Based on the prior knowledge that aircraft are frequently found in airports, current research adopts airport-to-aircraft detection pipelines for aircraft detection and classification in panoramic SAR images. Geospatial information, including the approximate airport location and the spatial relationships between the airport and aircraft, represents valuable supplementary information that can enhance fine-grained aircraft detection performance. However, due to unreliable geographical information and the limited perceptual field, it is challenging to detect and classify aircraft in panoramic SAR imagery. To address this, a novel geospatial contextual prior-enabled knowledge reasoning framework is proposed. First, an unsupervised and lightweight geospatial-driven airport detection (AD) method is presented by combining geographic information matching and optical-to-SAR image registration, which can quickly locate airports and narrow the scope for fine detection. Then, an improved real-time model for object detection with a recursive-gated spatial interaction module (RTMDet-RSIM) is proposed for fine-grained aircraft detection. RTMDet-RSIM uses frequency-domain analysis to improve global information modeling ability without excessive computational cost. Finally, a relational prior-based reasoning strategy is proposed by modeling the geospatial category relationships from historical images to strengthen classification performance. Experiments on 61 panoramic SAR images covering 13 aircraft categories show that the proposed method achieves high detection accuracy with a mean average precision (mAP) of 81.2%, while the average test time for an image size of$8738\times 7636$is about 8.58 s. The source code and dataset will be released.

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.

In response to the problems existing in traditional SAR image target detection methods, such as complicated processes, long detection times, and poor detection effects in complex backgrounds, this paper proposes an integration of detection and recognition method based on deep learning for large-scene SAR images with vehicle targets. The paper introduces the issues of target identification through three stages of target detection, identification, and classification in traditional methods. To address these problems, this paper introduces a one-stage detection network based on YOLOv5 to construct a SAR image vehicle target detection and recognition algorithm. To verify the performance of the algorithm, this paper generated a dataset containing 10 different vehicle targets in large-scene SAR images and applied it to experiments. The results demonstrate that the algorithm has good performance and fast detection speed. The research results of this paper can provide important references for large-scale SAR image target detection.

Due to the all-day, all-weather imaging characteristics of Synthetic Aperture Radar (SAR), aircraft recognition in SAR images is emerging as an vital issue. Electromagnetic scattering (ES) characteristics of target are the unique features of SAR systems. In particular, it is more pronounced for aircraft targets with discrete appearance. However, the existing deep learning methods effectively extract features in image domain, ignoring the potential ES features. To obtain more valuable features for SAR aircraft recognition, an innovative scattering features spatial-structural association network (SFSA) is proposed in this paper. In this work, the strong scattering points (SSPs) of aircraft are extracted and converted into graph structure data, which more directly represent the spatial-structural association among SSPs and clearly reflect the geometric structure of the aircraft target. Subsequently, the graph convolutional neural network (GCN) is employed to extract the structural and high-level semantic ES features. Then, a modified VGGNet is designed to effectively extract the image domain features of aircraft with extremely discrete appearances. In brief, the SFSA network is an end-to-end network that integrates structural ES features and more discriminative image domain features of the aircraft to achieve higher recognition performance. It is also demonstrated that structural ES features facilitate the recognition of aircraft. Experiments on the SAR aircraft dataset validate the effectiveness of SFSA network compared to traditional CNN-based SAR recognition networks and graph neural networks (GNN).

Recently, sparse representation has achieved the impressive performance on target recognition in synthetic aperture radar (SAR) image. However, the unstable and unsupervised optimization of the sparse representation may lead to undesired recognition result. In this paper, a locality-constrained and class-specific sparse representation (LCSR) framework is presented to alleviate these problems. Instead of the sparse constraint, the locality constraint is designed to utilize the local structure information of the training samples. It provides stable representation for the samples with minor variations, which is beneficial to classification. To further improve the recognition performance, the query sample is represented as a linear combination of class-specific galleries based on the supervision of class information. The inference is reached corresponding to the class with the minimum reconstruction error. The experimental results demonstrate the effectiveness and robustness of the proposed method.

Downward-looking linear array 3-D synthetic aperture radar (SAR) has attracted increasing attention in the field of radar imaging. As widely reported, the volume of data can be significantly reduced by a random sparse linear array. However, the 2-D under-sampled azimuth-cross-track data brought by the sparse linear array will produce high-level side-lobes, as well as the aliasing and the false-alarm targets. To deal with those problems, this paper introduces a recently developed theory, matrix completion (MC). The new theory could recover a matrix with a small subset of known elements of the matrix. It is founded on the assumption that the matrix is essentially low rank. For downward-looking 3-D SAR with a random sparse linear array, the received 3-D data can be treated as a series of uncorrelated 2-D matrices by the separated channel process. First, range compression can be realized by means of pulse compression. Then, the sets of the 2-D under-sampled azimuth-cross-track matrix can be completed into a full-sampled one via MC trick. The resulting 3-D images can be focused by synthetic aperture technique along the azimuth direction and beamforming operation along the cross-track direction, with the recovered full-sampled matrix. The proposed algorithm achieves high resolution and low-level side-lobes with the acceptable computational cost and memory consumption. It is verified by several numerical simulations and multiple comparative studies on real data. The experimental results clearly demonstrate the imaging performance across different under-sampling rates and signal-noise rates.

Change detection is a process of identifying changes in the state of objects between the reference and test images. This letter presents a target prescreening method that employs the change detection technique for automatic target recognition in synthetic aperture radar (SAR) images. First, four translated versions of an original SAR image are generated, and the corresponding four likelihood ratio images are computed. Then, a robust threshold is derived from the ratio of the histogram at two adjacent gray-level values of the likelihood ratio images. Finally, the threshold is applied to perform the prescreening. The proposed method implements the procedure without any prior knowledge and overcomes the weak adaptability of traditional algorithms. Two different real X-band airborne SAR images acquired over Beijing are used to quantitatively and qualitatively demonstrate the effectiveness of the proposed method.

For downward-looking linear array three-dimensional SAR, the resolution in cross-track direction is a curial problem. Hence, compressive sensing algorithm has been used to acquire the superresolution performance in cross-track direction. The limits of the proposed algorithm are investigated in this paper. How accurately can the scattering intensity of the scatterers be estimated? What is the closest separable distance of two scatterers at different levels of SNR? What is the influence of the acquisitions N on the resolution? For all of these questions, the theoretical analysis is given by Cramér-Rao Bound and numerical simulations are proven. The results can be considered as a fundamental bound on parameter estimates.

For downward-looking linear array 3-D synthetic aperture radar, the resolution in cross-track direction is much lower than the ones in range and azimuth. Hence, superresolution reconstruction algorithms are desired. Since the cross-track signal to be reconstructed is sparse in the object domain, compressive sensing algorithm has been used. However, the imaging processing on the 3-D scene brings large computational loads, which renders challenges in both data acquisition and processing. To cover this shortage, truncated singular value decomposition is utilized to reconstruct a reduced-redundancy spatial measurement matrix. The proposed algorithm provides advantages in terms of computational time while maintaining the quality of the scene reconstructions. Moreover, our results on uniform linear array are generally applicable to sparse nonuniform linear array. Superresolution properties and reconstruction accuracies are demonstrated using simulations under the noise and clutter scenarios.

In this letter, a novel imaging algorithm of downward-looking linear array 3-D synthetic aperture radar (SAR) is presented. To improve the resolution in cross-track direction, a multiple-signal-classification algorithm has been used. However, the computational cost is unattractive. To cover the shortage, fast Fourier transform (FFT) is utilized to roughly focus the targets in cross-track direction in this letter. Then, the range of peak searching can be considerably reduced. In addition, the backscattering coefficient can be directly obtained by FFT. On the other hand, since the scattering centers are always correlated in a real SAR system, the estimated covariance matrix is singular. To address the problem, the nearby spatial smoothing method is proposed. The array vectors of the nearby range and azimuth units are used to reconstruct the estimated correlation matrix. The effective aperture of the array can also be kept.