Synthetic aperture radar (SAR) imagery is susceptible to multiple factors, including radar parameters, imaging mode, and imaging angle. Consequently, the resolution of the training and test data often differs, leading to the failure of existing deep learning-based SAR target recognition methods. To address this challenge, this paper introduces a transformer-based cross-resolution target recognition method for SAR images based on feature alignment and aggregation. The objective is to improve the accuracy of cross-resolution target recognition. Experimental results obtained from OpenSARShip, an openly available dataset with ship images captured at various resolutions, demonstrate the superior performance of the proposed method compared to the current state-of-the-art (SOTA) technique.

In recent years, the rapid development of synthetic aperture radar (SAR) technology has enabled researchers to obtain SAR targets from multiple sources within the same class. However, most existing SAR target recognition methods are designed for single-source target recognition. When applied to multi-source target recognition in SAR imagery, the different data distributions from various sources can negatively impact recognition accuracy. To address this issue, this paper proposes a scale distillation method for multi-source target recognition in SAR imagery. This approach involves extracting feature representations of the target, incorporating multi-scale semantic information through the multi-scale feature fusion mechanism (MSFFM), and performing knowledge distillation on the output scale features to enhance discriminative knowledge about the target scales. Experimental results on the FUSAR-Ship and OpenSARShip datasets from different sources demonstrate an average improvement in classification accuracy by 2.5% after applying scale distillation, which is a significant advantage over other methods.

Synthetic aperture radar (SAR) targets are often characterised by high manoeuvrability and strong concealment, resulting in scarce and few-shot SAR data. Due to the scarcity and variability of samples causing significant fluctuations in class centers, the sample distribution is challenging to determine, resulting in the model’s inability to accurately represent the potential representative features of few-shot SAR targets. Therefore, we propose a few-shot SAR target detection method based on Gaussian meta-feature directed aggregation (GMDA) using the meta-learning paradigm. Specifically, a Gaussian distribution is constructed on the support branch to estimate the class distribution of the few-shot SAR target and replace the traditional class prototype. Based on this, we propose the feature information maximization module (FIMM) to avoid the bias of the feature information and achieve the directional expression of the features in order to complete the efficient aggregation. Experiments on SRSDD-v1.0 and MSAR-1.0 datasets show that our method almost beats the state-of-the-art methods at this stage and achieves state-of-the-art performance in all settings.

The features employed for synthetic aperture radar (SAR) target recognition have evolved from traditional SAR target geometric features and pattern features to modern deep features, indicating a trend of increasing recognition accuracy but decreasing feature interpretability. Therefore, the fusion of multidimensional features has been investigated by many researchers. Existing feature fusion methods typically involve simple concatenation or addition of geometric features and pattern features with deep features, or directly incorporating them into deep networks. However, such fusion methods mentioned above inadequately consider the potential conflicts between features and hard to fully exploit multidimensional features. To solve the above problem, a multidimensional feature joint learning framework (MFJL-Framework) that serves the SAR target recognition task is proposed in this article, which consists of three models. Specifically, the SGC-GA-Model can select pattern features for SAR targets based on geometric feature constraints, the Global and local Feature Information interaction Capture model (GFIC-Model) can select deep features with high-level abstract semantics, and the MFFS-Model can complement and fuse these two types of features to maximize the utilization of feature information. Experiments and comprehensive ablation studies on four datasets, namely OpenSARShip-1.0, FUSAR-Ship, MSTAR-T72Variants, and SAR-AIRcraft-1.0, collectively demonstrate that the recognition performance of our proposed FJL-Framework outperforms the current state-of-the-art methods.

Synthetic aperture radar (SAR) target recognition plays an indispensable role in interpreting SAR images. However, differences in radar parameters (including factors such as imaging modes and imaging angles) often lead to resolution differences between training and test data, posing challenges for existing methods in recognizing SAR targets under cross-resolution conditions. To address this issue, this article proposes a domain adaptation (DA) framework based on feature aggregation and compensation (FAC) for cross-resolution target recognition in SAR imagery. Initially, we employ a unique local vision transformer (LocalViT) to establish global and local adversarial networks that capture invariant features under cross-resolution conditions. Following this, we design a multiscale feature fusion module (MSFFM) to capture multiscale semantic features of targets at different resolutions. Subsequently, we propose a novel class feature aggregation module (CFAM) to map targets of varying resolutions onto the unit sphere, thereby aggregating features of samples from the same class and distinguishing those of samples from different classes. Finally, we narrow down the difference in frequency-domain information of targets at different resolutions by developing a resolution semantic compensation module (RSCM). This module compensates for the semantic feature about resolution during target recognition across varying resolutions by converting high- and low-frequency information. The experimental results on three SAR datasets (OpenSARShip, FUSAR-Ship, and SRSDD-v1.0) confirm that our method outperforms the state-of-the-art (SOTA) DA methods, with an increase of 2.06%, 1.96%, and 1.61% in three sets of cross-resolution scenes, respectively.

Deep neural networks have shown remarkable effectiveness in SAR target recognition. However, the explainability problem for deep neural networks remains insufficiently addressed. One approach to tackle this challenge is the SHAP method. It enhances the explainability of deep neural networks in SAR target recognition by observing how the target, shadow, and clutter regions play their own distinct roles. The masked regions are typically filled with Zero, Mean, or Random values in optical images. But if the same operation performed on SAR images, it will affect the distribution of clutter and thus introducing new out-of-distribution challenge. In this paper, we propose a novel masking method to enhance the reliability and efficiency of the SHAP method in SAR-ATR applications. Experimental results on the MSTAR and OpenSARShip-1.0 datasets demonstrate that our proposed method provides a more faithful representation to show the importance of every single regions in SAR target recognition. Compared to methods using Zero values, Mean values, and Random baselines, our proposed method significantly enhances the reliability of explainability.

Synthetic Aperture Radar (SAR) targets often exhibit characteristics such as high mobility and strong concealment, resulting in scarce SAR data and the manifestation of few-shot data properties. These few-shot SAR targets are susceptible to interference from complex background information and mutual interference of target features, making it challenging to distinguish SAR targets from the background. Additionally, there is confusion in features among different targets, leading to models being highly insensitive to few-shot SAR targets under complex distribution conditions in new tasks. Similarly, these few-shot SAR targets exhibit significant sample scarcity and sample variations, resulting in pronounced fluctuations in class centers and difficulty in determining sample distributions. This leads to challenges in accurately representing the potential representative features of few-shot SAR targets by the model. To address these issues, further enhancement of SAR target features is necessary to provide a robust foundation for the ultimate aggregation module. Therefore, based on the meta-learning paradigm, we propose a method for few-shot target detection in SAR imagery via intensive meta-feature aggregation (IMFA), aiming to reinforce SAR target features for improved representation. Specifically, firstly, we propose a novel hierarchical multi-head cross attention (HMCA) to capture global multiscale contextual information in different subspaces and analyze representative features between different targets to distinguish SAR targets from the background. Then, based on HMCA, we introduce a novel feature coupling module (FCM) to couple support features with cognitive information from the query image on the support branch. This is done to reduce the confusion and mutual interference of features between targets while enhancing the model’s generalization ability on new tasks. Finally, on the support branch with query-aware information, we construct a Gaussian distribution to estimate the class distribution of few-shot SAR targets and replace traditional class prototypes. On this basis, we propose the feature information maximization module (FIMM) to avoid feature information shift, greatly strengthening the expression of potential features. Through these steps, reinforced meta-features can be obtained, enabling efficient aggregation. Experiments on the SRSDD-v1.0 and MSAR-1.0 datasets demonstrate that our method has consistently outperformed state-of-the-art approaches in all configurations, achieving state-of-the-art performance.