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.

Synthetic aperture radar (SAR) images are highly susceptible to imaging conditions. However, the majority of deep learning (DL) models in SAR automatic target recognition (ATR) adopt enhanced network structures similar to those in dealing with optical image classification tasks, which is obviously unreasonable since the huge gap of the imaging conditions between training and testing data severely deteriorates the recognition performance. The main idea of the framework is to introduce SAR imaging condition information into the DL training stage to eliminate domain discrepancies between training and testing data. Based on this framework, we propose a task-driven domain adaptation (TDDA) transfer learning method, which can alleviate the degradation of recognition caused by the variance of depression angle between training and testing data. In order to introduce the prior imaging information into the method, simulated SAR data is first obtained by adding a simulated object radar reflectivity to a terrain model of individual point scatters using the known training and testing SAR imaging parameters. Then a domain confusion metric and a supervised classification loss are calculated on simulated data and source training data, respectively, to learn a representation that is semantically meaningful and domain invariant. Comparative experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate that the proposed method can obtain better recognition performance than the other methods.

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 relations 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 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 employs 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 relations 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 an mAP of 81.2%, while the average test time for an image size of 8738×7636 is about 8.58s. The source code and dataset will be released.

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.

Change detection using heterogeneous remote sensing images is an increasingly interesting and very challenging topic. To make the heterogeneous images comparable, some graph-based methods have been proposed, which first construct a graph for the image to capture the structure information and then use the graph to obtain the structural changes between images. Nonetheless, previous graph-based change detection approaches are insufficient in representing and exploiting the image structure. To address these issues, in this paper, we propose an auto-weighted structured graph (AWSG)-based regression method for heterogeneous change detection, which mainly consists of two processes: learning the AWSG to capture the image structure and using the AWSG to perform structure regression to detect changes. In the graph learning process, a self-conducted weighting strategy is employed to make the graph more robust, and the local and global structure information are combined to make the graph more informative. In the structure regression process, we transform one image to the domain of the other image by using the learned AWSG, where the high-order neighbor information hidden in the graph is exploited to obtain a better regression image and change image. Experimental results and comparisons on four real datasets with seven state-of-the-art methods demonstrate the effectiveness of the proposed approach.